International Journal of Advances in Applied Sciences
The International Journal of Advances in Applied Sciences (IJAAS), (p-ISSN 2252-8814, e-ISSN 2722-2594) is a peer-reviewed, open-access international journal dedicated to advancing knowledge, innovation, and technological excellence in applied science, engineering, and computer science. Serving as a global platform for researchers, academics, industry professionals, and innovators, IJAAS publishes high-quality original research and emerging technological developments that address real-world challenges and contribute to a sustainable, digitally connected future. In alignment with its international scholarly profile, the journal particularly emphasizes research in Electrical and Electronic Engineering, interdisciplinary Engineering applications, Computer Science Applications, and Energy-related technologies, while also welcoming contributions that bridge scientific discoveries with practical and industrial implementation. Through the dissemination of impactful, future-oriented research, IJAAS promotes scientific advancement, technological innovation, and cross-disciplinary collaboration that drive progress across academia, industry, and society. Published by Intelektual Pustaka Media Utama (IPMU), IJAAS upholds rigorous editorial standards and international best practices in scholarly publishing. The journal is officially accredited by the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia under Decree No. 79/E/KTP/2023, reflecting its commitment to academic excellence, research integrity, and global relevance. Since 2019, articles published in IJAAS have been indexed in Scopus, where the journal is classified under the subject areas of Electrical and Electronic Engineering, Engineering (Miscellaneous), Computer Science Applications, and Energy (Miscellaneous). IJAAS is also indexed and evaluated by SCImago Journal Rank (SJR), further strengthening its international visibility, citation impact, and scholarly reputation. By fostering cutting-edge research and emerging technologies, IJAAS continues to position itself as a trusted and influential venue for disseminating high-impact scientific contributions that shape the future of engineering, computing, and energy innovation worldwide. IJAAS Features: Plagiarism: CrossCheck plagiarism powered by iThenticate. DOI: DOI Number is indexed by Crossref. Open Access: Open access provides immediate and unrestricted access to the latest research. Full Archive Available: Every issue of Journal of IJAAS is available online. ISSN Record Information: The ISSN of IJAAS journal is registered at https://portal.issn.org/resource/ISSN/2722-2594 <!--[if mso]> <xml><w:WordDocument xmlns:w="urn:schemas-microsoft-com:office:word"><w:DontUseAdvancedTypographyReadingMail/></w:WordDocument> <o:OfficeDocumentSettings><o:PixelsPerInch>96</o:PixelsPerInch><o:AllowPNG/></o:OfficeDocumentSettings></xml> <![endif]--><!--[if !mso]><!--><!--<![endif]--><!--[if mso ]> <![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/B9lB-4sZNI4?si=o5H0_HS2NaXlxe-Z" style="height:46px;width:210px;v-text-anchor:middle;" arcsize="9%" fillcolor="#3202b6"> <v:stroke dashstyle="Solid" weight="0px" color="#3202b6"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO REGISTER<!--[if mso]> </v:textbox></v:roundrect><![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/hQD9-HeNy1k?si=_Q7Dsawb-OEvh2Gl" style="height:46px;width:187px;v-text-anchor:middle;" arcsize="9%" fillcolor="#ff47ce"> <v:stroke dashstyle="Solid" weight="0px" color="#ff47ce"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO SUBMIT<!--[if mso]> </v:textbox></v:roundrect><![endif]-->
Institute of Advanced Engineering and Science
COPYRIGHT AND LICENSING AGREEMENT This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Intelektual Pustaka Media Utama (IPMU) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IPMU the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Real-time human activity recognition using deep learning techniques for the next-generation healthcare system
In today's world, healthcare systems are being built with human activity recognition (HAR) to help the elderly, disabled, and children's activities by constantly observing their behavior. However, HAR using computer vision and traditional machine learning techniques is not an efficient use of healthcare system resources because of potential and accuracy issues. This study aims to examine the use of deep learning techniques in real-time HAR. This proposal is a hybrid method that utilizes the EfficientNetB0 architecture and a convolutional extension of a long short-term memory network (EfficientNetB0ConvLSTM), to achieve human-like intelligence. The EfficientNetB0 is utilized to extract image features, and convolutional long short-term memory (ConvLSTM) is utilized to categorize six human actions to recognize human activities. This approach leverages the strengths of convolutional neural networks (CNNs) in extracting spatial features from video frames and LSTMs in capturing temporal dependencies within activity sequences. Firstly, an extensive investigation is conducted on existing literature studies to select a suitable dataset. Next, the proposed method was evaluated on the challenging HMDB51 video datasets and finally achieved an accuracy of 89.22%, which is significantly higher than other methods on this dataset. This outcome shows the potential of EfficientNetB0ConvLSTM for real-time HAR applications like healthcare.
Copyright (c) 2026 Subrata Kumer Paul, Rakhi Rani Paul, Md. Ekramul Hamid, Md. Rafiqul Islam (Rafiq) http://creativecommons.org/licenses/by-sa/4.0
Comparison of thermal and non-thermal images for tomato fruit detection
Farmers use manual observation to sort, grade, and estimate tomato production results to meet market demands. However, this method requires a lot of energy and time, making it unsuitable for large-scale tomato cultivation utilizing the detection process. This study aims to develop the automatic tomato detection technology in an industrial environment based on a conveyor belt by using thermal or non-thermal imaging and you only look once version 8 (YOLOv8). The dataset consists of 570 images obtained from each thermal and non-thermal camera and has undergone augmentation techniques to enrich the data variety. The model was trained and validated using 640×640-pixel images for 40 epochs. In this paper, we conduct a comparative analysis of the tomato detection result using YOLOv8 on thermal and non-thermal imaging. The results indicate that the model trained with thermal data significantly outperformed the non-thermal model, achieving 99% precision, 98% recall, 98% F1-score, and 99% mean average precision (mAP)50 during validation. The thermal model received a 99% accuracy rate during validation, while the non-thermal model attained 94% accuracy, exhibiting a slightly poorer performance and committing several mistakes in detection. The use of thermal cameras on moving automation systems has demonstrated its capability and effectiveness, making it more optimal for application in the agricultural industry.
Copyright (c) 2026 Sulfayanti Faharuddin Situju, Wawan Firgiawan, Hironori Takimoto http://creativecommons.org/licenses/by-sa/4.0
Efficacy of Toona sureni Merr. leaf extract against Aedes spp. and Culex spp. by electric diffusion
Aedes spp. and Culex spp. mosquitoes are major vectors of dengue fever and filariasis. The extensive use of chemical insecticides has contributed to resistance and environmental concerns, underscoring need for eco-friendly alternatives. Suren leaf extract (Toona sureni Merr.) contains bioactive compounds with potential as a botanical insecticide. This study evaluates suren leaf extract delivered via an electric diffusion device for indoor adult mosquito control at concentrations of 45%, 55%, and 65% against Aedes spp. and Culex spp. A post-test-only control group design was applied with three replications, and outcomes were recorded over a 60 minutes exposure period. Mortality increased with both concentration and exposure time. At 60 minutes, mortality increased from 80% to 100% in Aedes spp. and from 64% to 96% in Culex spp. when the concentration increased from 45% to 65%. Two-way analysis of variance (ANOVA) confirmed significant effects of concentration and exposure time (p <0.001). These findings indicate that electric diffusion delivery of suren leaf extract is effective and supports its potential use for indoor vector control in endemic areas.
Copyright (c) 2026 Sulasmi, Budirman, Haderiah, La Taha, Nurhaidah http://creativecommons.org/licenses/by-sa/4.0
Improving warehouse efficiency of sparepart storage through comparison of dedicated and class-based storage methods
Warehouses play a crucial role in supporting the efficiency of supply chain operations, especially in material management and goods movement. However, in a company engaged in the paper printing and recycling industry, inefficiency in searching and retrieving goods in the spare parts warehouse is a major challenge, with a sub-optimal layout hampering smooth operations. This research offers a new perspective by evaluating the effectiveness of two storage methods, namely class-based storage and dedicated storage, in the context of spare parts warehouses that have different layout characteristics. This study aims to compare the two methods in improving material-handling efficiency and optimizing warehouse layout. The results show that the class-based storage method is more effective in reducing travel distance and material-handling time than dedicated storage. In addition, this method can improve operational efficiency and support the smooth production process through the arrangement of goods based on picking frequency. The theoretical implications of this research contribute to the development of warehousing management literature, while practically, the results of this study can serve as a guide for industries in implementing more efficient warehouse layout strategies.
Copyright (c) 2026 Prayoga Prima Hermawan, Qurtubi http://creativecommons.org/licenses/by-sa/4.0
Predicting Indonesian academician turnover intention: validity and reliability analysis
This study evaluates Indonesian academic turnover intention (TOI) by analyzing demographic and work-related factors through feature selection methods and utilizes random forest (RF) as a baseline classifier for TOI prediction, while applying statistical methods to ensure the reliability of the collected primary dataset. The main advantage of this approach is to find out the importance of these factors with statistical validation to reliably investigate Indonesian academicians’ TOI. Feature selection methods such as information gain (IG) and SelectKBest were used to find out feature importance, while the reliability of the dataset was assessed through statistical approaches such as Cronbach alpha, confirmatory factor analysis (CFA), average variance extracted (AVE), and consistency ratio (CR). To test the importance of demographic and work-related factors, Python was used as an implementation tool for the Indonesian academic TOI dataset (IRB reference: 19.12.4/UN32.14/PB/2024), comprising 527 samples. The superiority of the importance of work-related factors in contrast to demographic factors was consistently demonstrated by feature selection methods, and a statistical approach confirmed the reliability of the collected primary dataset, consequently ensuring the robustness of the findings. It is envisaged that this approach can be very useful for human resource (HR) departments to pay more attention to the important demographic factors for reducing Indonesian academic TOI.
Copyright (c) 2026 Faisal Al Abid, Aryati Bakri, Hasin Jawad Ali, Darmawan Satyananda, Shefayatuj Johara Chowdhury, Jia Uddin http://creativecommons.org/licenses/by-sa/4.0
Intelligent hyperparameter optimization of multilayer perceptron for water quality classification
Assessment of water quality is essential for monitoring the environment and sustainable resource management. However, complex physicochemical parameters and class imbalance pose challenges for reliable predictive modeling. Although the multilayer perceptron (MLP) is widely applied for classification tasks, its performance depends strongly on effective hyperparameter optimization. This study evaluates three metaheuristic approaches—Bayesian optimization (BO), genetic algorithm (GA), and particle swarm optimization (PSO)—to enhance the performance of MLP on an imbalanced water potability dataset, which is addressed using synthetic minority over-sampling technique (SMOTE). Experimental results obtained using stratified 10-fold cross-validation demonstrate consistent improvements over the baseline MLP (accuracy of 0.7899±0.013). BO and GA improve predictive performance, while PSO achieves the best overall results with an accuracy of 0.9324±0.006, F1-score of 0.9338±0.006, and receiver operating characteristic–area under the curve (ROC–AUC) of 0.9652±0.005. The findings indicate that PSO provides superior convergence stability and generalization by effectively balancing exploration and exploitation during hyperparameter search. The integration of class balancing and swarm-based optimization substantially enhances classification robustness and discriminative capability, supporting its applicability for intelligent water quality monitoring systems.
Copyright (c) 2026 Edi Ismanto, Rahmad Gunawan, Harun Mukhtar, Rahmad Al Rian, Vitriani, Hadhrami Ab Ghani http://creativecommons.org/licenses/by-sa/4.0
Estimating road transport emissions in Thailand using data-driven modeling approaches
Traffic emissions remain a pivotal factor in Bangkok’s air quality issues, particularly as the city expands and vehicle types become more varied and traditional emission-inventory tools, such as Computer Programme to Calculate Emissions from Road Transport (COPERT) and laboratory driving cycles, are useful but often fall short when applied to congested real-world conditions. This work responds to that gap by combining laboratory measurements, regulatory emission factors, and vehicle-registration information to estimate CO2 emissions at the vehicle level. Data from 1,846,111 registered vehicles (January 2023–January 2025) were consolidated from Environmental Protection Agency (EPA) fuel-economy tests, COPERT/Euro standards, and the Thai DLT registry. After harmonizing attributes across all sources, additional features were created and screened using recursive feature elimination (RFE). Three models were trained and evaluated using coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). CatBoost provided the most reliable predictions (RMSE =23.4 g/km; MAE =14.6 g/km; R2 =0.921), followed by deep neural network (DNN) (R2 =0.887) and support vector regression (SVR) (R2 =0.846). RFE consistently identified fuel consumption measures, emission factors, and tailpipe CO2 as the most influential variables and the results provide a clearer picture of how vehicle technologies contribute to Bangkok’s CO2 burden and support policy measures such as emission taxes, incentives for low-emission vehicles, and monitoring of high-emission groups.
Copyright (c) 2026 Phachaya Chaiwchan, Kunyanuth Kularbphettong http://creativecommons.org/licenses/by-sa/4.0
Vertical-horizontal flow roughing filter for improving water quality in sustainable water supply infrastructure
Water is fundamental to cooking, bathing and hygiene, and essentially for life itself. Across many areas, communities rely on bore well water as a source of raw water for domestic needs. Yet this resulting water is mostly not of an appropriate quality, as it has a high iron content and hardness. This study evaluates the performance of a combined vertical-flow and horizontal flow roughing filter (VRF and HRF) system for bore well water treatment. The system was tested using various alternative filter media, including coconut fiber, silica sand, activated carbon, zeolite, pumice stone, and volcanic black sand, arranged in different configurations. The vertical-flow unit used a media depth of 6 cm, while the horizontal-flow unit used a depth of 15 cm. The results showed that all configurations effectively improved water quality and met standard requirements. The highest performing model, consisting of volcanic black sand, activated carbon, zeolite, and silica sand, achieved a 97.5% reduction in iron and a 13.2% reduction in hardness. These findings indicate that optimized roughing filter systems offer a low cost and efficient solution for decentralized water treatment.
Copyright (c) 2026 Anggara Wiyono Wit Saputra, Prasetyo Rubiantoro, Very Dermawan http://creativecommons.org/licenses/by-sa/4.0
Semantic clustering of scientific abstracts with transformer embeddings and traditional text representations
The large and diverse quantity of scientific documents in the world, and specifically in Indonesia, makes the process of processing scientific document data an interesting study. One that represents the entire scientific document is through abstracts. The approach that can be done for the process of processing and grouping documents is to apply clustering. In this case, text-based clustering is currently heavily influenced by the feature representation of the text data used. Some popular representations of features are term frequency-inverse document frequency (TF-IDF) and count vectorizer, but they still have significant weaknesses in the context of understanding the meaning of natural language. To cover the drawbacks, it can use transformers or embedding types. In this study, several test scenarios will be carried out to obtain information and an overview of how to compare the effectiveness of the traditional TF-IDF model and the bidirectional encoder representations from transformers (BERT) and sentence bidirectional encoder representations from transformers (SBERT) embedding models in Indonesian-language scientific abstract clustering with several clustering models, such as k-means and agglomerative. The results of the study showed that the most effective clustering obtained was by using an embedding model of a combination of BERT and k-means, which was the most consistent with the most optimal number of clusters being 2 clusters.
Copyright (c) 2026 Musthofa Galih Pradana, Pujo Hari Saputro, Ardhyansyah Mualo, Arbiati Faizah, Wahyuni Fithratul Zalmi http://creativecommons.org/licenses/by-sa/4.0
Westernization and preservation: an appropriated edutourism application as educational media for archaeological sites in Bedulu, Bali
The commercialization of tourism developing in West Bali poses risks that can damage the cultural identity not only of the island but, more crucially, of archaeological sites of cultural value. This research aims to develop a glocal educational tourism application for the Bedulu archaeological sites that offers a cultural alternative to the homogenization of the island's tourism culture. The novelty of this research lies in the combination of integrated digital storytelling for social media (IDSM) and advanced interactive digital storytelling (A-IDS), with a design approach that eliminates colonization and is community-centered. Using the Borg and Gall model, which is limited to the planning stage, data for this research were collected through literature review, field observation, surveys of 30 subjects, and semi-structured interviews with 5 purposively selected key informants (site managers, archaeologists, and user interface (UI)/user experience (UX) designers), as well as community co-design workshops. This research resulted in a cultural content map and a prototype application, which is the first in the world, and serves as an operational glocalization model, the first in the world to become a model for intergenerational educational digital heritage literature, for inclusive community development, and sustainable Balinese cultural heritage.
Copyright (c) 2026 Putu Sabda Jayendra, Gusti Ayu Dessy Sugiharni, Gusti Ngurah Yoga Semadi http://creativecommons.org/licenses/by-sa/4.0
An integrated EHS-based risk assessment framework for hazardous substance storage: regulatory gap analysis in Thailand
This study examines hazardous substance storage risk assessment within the environmental, health, and safety (EHS) framework, focusing on Thailand’s regulatory context. Using a systematic review and comparative regulatory analysis, the research evaluates existing risk assessment methodologies and identifies structural and implementation gaps in current practices. The findings reveal deficiencies in the integration of toxicological data with legal requirements, fragmented regulatory enforcement, limited standardization, and insufficient practical tools for workplace application. A comparative assessment of international frameworks, including European Union Registration, Evaluation, Authorization, and Restriction of Chemicals (EU REACH), United States Occupational Safety and Health Administration (US OSHA), and Japan’s Chemical Substances Control Law (CSCL), highlights best practices in proactive risk management and transparency. Based on these insights, the study proposes an integrated EHS-based risk assessment framework that aligns Department of Industrial Works (DIW) regulations with international standards such as ISO 14001, ISO 45001, and globally harmonized system (GHS). The proposed model supports proactive risk identification, regulatory coherence, and improved stakeholder awareness, contributing to enhanced chemical safety governance and ASEAN harmonization.
Copyright (c) 2026 Hengheng Jarunongkran, Therdpong Daengsi http://creativecommons.org/licenses/by-sa/4.0
PhyFizball: a game-based tool to enhance student’s understanding of force and motion
Difficulties in understanding the concepts of force and motion continue to challenge secondary school students due to the abstract nature of physics and the limited use of interactive teaching methods. This study introduces PhyFizball, a pinball-inspired, low-cost game-based learning (GBL) tool designed to make physics learning more engaging and concrete. The tool aims to help students visualize fundamental concepts such as Newton’s laws, friction, and the relationship between force and motion through hands-on and collaborative gameplay. A quasi-experimental pretest–posttest control group design was employed with 30 form two students from a Malaysian secondary school, divided equally into experimental (n =15) and control (n =15) groups. Over four weeks, the experimental group learned using PhyFizball, while the control group received conventional lecture-based instruction. A validated conceptual understanding test was administered before and after the intervention. Results from paired and independent t-tests revealed that the experimental group achieved significantly higher post-test scores than the control group (t(28) =3.282, p =0.003). The findings confirm that PhyFizball effectively enhances students’ conceptual understanding and engagement in learning physics. Its accessible design demonstrates potential as a cost-effective and scalable teaching tool for improving science learning outcomes in secondary education.
Copyright (c) 2026 Muhammad Alfi Yusra Jalani, Adibah Abu Bakar, Syazwan Saidin http://creativecommons.org/licenses/by-sa/4.0
Program defect prediction model based on topology aware node evaluation pool graph topology model
Traditional fuzzing struggles with efficiency, as maximizing code coverage does not guarantee the discovery of additional vulnerabilities. To solve this, the study introduces topology-aware node evaluation (TANE-Pool), a deep learning model that proactively predicts defects to guide the fuzzing process. The model analyzes the structural properties of a program’s attributed control flow graph (ACFG) via a diffusion attention mechanism. This process identifies fragile code regions and generates a static vulnerability score (SVS) for each basic block. The fuzzer then uses this score to prioritize test cases, concentrating its efforts on the areas most likely to contain flaws. Evaluated on the Juliet test suite and a set of real-world programs, TANE Pool demonstrates superior prediction accuracy. Its integration into a fuzzer significantly enhances the rate of vulnerability discovery, proving that a defect-prediction-guided approach is a more efficient and effective strategy for software security testing.
Copyright (c) 2026 Dan Li, Poh Soon JosephNg, Peng Yin Choo, Koo Yuen Phan, Wong See Wan http://creativecommons.org/licenses/by-sa/4.0
SmartCloud QR: a next-gen online attendance system leveraging QR code and cloud technology
This study presents SmartCloud quick response (QR), a cloud-based attendance monitoring system designed to enhance efficiency, accuracy, and fraud prevention in educational institutions. Traditional paper-based attendance methods are time-consuming, error-prone, and vulnerable to proxy attendance. To address these limitations, the system was developed using a software-as-a-service architecture that integrates QR code technology with cloud computing, enabling scalable deployment without requiring dedicated institutional hardware infrastructure. The research was conducted in two phases: phase 1 involved user needs analysis to identify operational challenges in attendance management and phase 2 evaluated system effectiveness through large-scale implementation involving 27,070 participants. Statistical analyses, including independent samples t-tests and one-way analysis of variance (ANOVA), were performed to examine satisfaction differences across gender and user roles. The results demonstrated a high overall satisfaction rate (92.84%), with only minimal and practically negligible differences observed across demographic groups. The findings empirically support the technology acceptance model (TAM), indicating that perceived usefulness and ease of use are reinforced by fraud prevention mechanisms and infrastructure-light deployment. This study contributes to the applied educational informatics literature by introducing a scalable software-as-a-service attendance framework that enhances fraud prevention, reduces institutional hardware investment, and operationalizes privacy-by-design principles in large-scale real-world implementation.
Copyright (c) 2026 Kriangsak Chanthinok, Palan Jantarajaturapath, Konkanok Donsophon http://creativecommons.org/licenses/by-sa/4.0
Characteristics of biochar from agricultural residues produced by traditional combustion and its potential as a soil amendment
Biochar is a carbon-rich material produced through pyrolysis. However, pyrolysis methods that generate high-quality biochar typically require expensive equipment and technical expertise. This study examines the characteristics of biochar produced from rice husk, cassava wood, and corn cobs using a traditional burning method as an alternative to pyrolysis. The research results showed that the carbon content of biochar produced using traditional methods was comparable to, or even higher than, biochar produced using more advanced pyrolysis methods. Corncob biochar had the highest carbon content among all samples. X-ray fluorescence (XRF) analysis revealed that rice husk biochar had the highest silica content, while cassava wood biochar showed higher percentages of calcium (Ca) and magnesium (Mg) than the other biochars. X-ray diffraction (XRD) characterization results indicated a higher degree of graphitic crystallinity in the corncob biochar, while Fourier transform infrared spectroscopy (FTIR) spectra confirmed the presence of hydroxyl groups, aromatic C=C, and cellulose residues in all samples. thermogravimetric–differential thermal analysis (TG-DTA) and differential scanning calorimetry (DSC) analyses provided insights into thermal behavior and carbonization levels, with corncob biochar having the highest carbon content and thermal resistance. The application of biochar to acidic soils has been shown to significantly increase soil pH and organic carbon content. The simple combustion method used in this study is capable of producing biochar with diverse physicochemical properties, thus offering a sustainable approach to soil improvement, increased nutrient retention, and carbon sequestration, while providing added value to agricultural waste and waste valorization strategy.
Copyright (c) 2026 Ali Rahmat, Sukamto, Santi Ari Respati, Fera Arum, Wiwiek Dwi Susanti, Yudia Azmi, Zurrahmi Wirda http://creativecommons.org/licenses/by-sa/4.0
Multi-level redundancy with internet of things battery supply for fault mitigation in grid-tied photovoltaic systems
The development of grid-tied photovoltaic (PV) systems in tropical regions remains a strategic focus for achieving sustainable clean energy. However, energy conversion efficiency is often hampered by fluctuations in panel surface temperature and electrical faults. To ensure long-term system reliability, this study implements a multi-level redundancy architecture integrated with dynamic internet of things (IoT) monitoring for fault mitigation in grid-tied PV systems. The system employs a machine learning (ML) method using the k-nearest neighbors (KNN) algorithm for thermal classification, achieving an accuracy of 84% in identifying normal (25 °C to 35 °C) and overheating conditions. Furthermore, an electrical redundancy layer is designed with an automatic tripping mechanism that activates when the current exceeds a 1.30 A threshold, demonstrating a rapid response latency of 150 ms. To ensure monitoring resilience, the system is supported by a dedicated 18650 Li-ion battery backup. The implementation results confirm that this multi-level protection framework effectively monitors real time energy usage, prevents critical component damage, and enhances the overall safety of household-scale PV installations. This research provides a scalable and intelligent solution for fault mitigation, supporting the broader adoption of renewable energy in tropical environments.
Copyright (c) 2026 Habib Satria, Muhammad Fadlan Siregar, Indri Dayana, Dadan Ramdan, Muhammad Irwanto, Syafii http://creativecommons.org/licenses/by-sa/4.0
Bioinsecticide-based malaria control strategy: epidemiological implications and environmental sustainability
Anopheles mosquitoes are the main vector of malaria, which remains a global health problem. This study aims to evaluate the effectiveness of various types of plants as bioinsecticides on the mortality of Anopheles larvae based on variations in extract concentration and exposure time. The experimental study design used a completely randomized factorial design. The variables were studied in bioinsecticide formula consisting of 66 levels; 3 levels of concentration and contact time to Anopheles larvae. The number of replications was four times following the Foreder formula. The study was conducted at the Parasitology Laboratory from January to June 2025. Findings showed the factors of plant type (p <0.001), extract concentration (p <0.001), and exposure time (p <0.001) had a significant effect on the level of larval mortality. The interaction between plant type and concentration showed a synergistic effect, where the effectiveness of the bioinsecticide increased with the right combination. Longer exposure times also significantly increased the larval mortality rate. The results of this study indicate that the use of plants as bioinsecticides can be a potential alternative in malaria vector control. The study suggests that plant-based bioinsecticides can effectively combat malaria by enhancing effectiveness through synergistic interactions between plant species, extract concentration, and exposure duration.
Copyright (c) 2026 Mei Ahyanti, Prayudhy Yushananta http://creativecommons.org/licenses/by-sa/4.0
Mapping artificial intelligence applications in electronic medical records research
The integration of electronic medical records (EMR) and artificial intelligence (AI) in healthcare improves data accessibility, security, diagnostic accuracy, personalized care, and overall system performance. Despite increasing interest, a comprehensive understanding of the field’s development, key contributions, and dominant research themes remains limited. This study presents a bibliometric analysis of 681 articles selected from 1893 initial records retrieved from the Scopus database (2015–2025) using the keywords “electronic medical record” AND “artificial intelligence.” Data were analyzed using Microsoft Excel for trend analysis and VOSviewer for keyword co-occurrence and thematic clustering. Results show steady publication growth, mainly from developed countries and health informatics institutions. Four main research themes emerged: i) AI adoption in healthcare systems, ii) patient characteristics and clinical assessment, iii) predictive models and machine learning (ML) algorithms, and iv) deep learning (DL) and diagnostic accuracy. Nevertheless, research gaps persist in areas such as patient safety, data privacy, ethical issues, primary care implementation, healthcare workforce roles, and specific algorithmic approaches. Trust in AI systems also requires deeper investigation.
Copyright (c) 2026 Bima Ananta Putra, Merita Arini http://creativecommons.org/licenses/by-sa/4.0
Anomaly detection in containerized virtual environments using a process mining approach
The growing adoption of container-based virtual environments in cloud computing introduces new challenges in anomaly detection due to the systems’ dynamic nature. Traditional monitoring approaches often fail to capture inefficiencies or security risks effectively. This study proposes a process mining approach to identify anomalies by analyzing event logs from containerized systems. Event data from the AIOps challenge 2020 was converted into extensible event stream (XES) format and processed using the inductive visual miner in the ProM tool to generate Petri Net models, accurately visualizing container activity flows. A conformance checking analysis was conducted to evaluate the alignment between modeled and actual behavior. Results demonstrated a high fitness score, confirming the model’s precision in reflecting true operational processes and its ability to reveal minor deviations indicative of potential anomalies. These findings highlight process mining as a promising method to enhance security, transparency, and performance monitoring in virtual environments. The research also recommends integrating process mining with real-time monitoring systems for proactive anomaly detection, thereby improving responsiveness and resilience in cloud-based infrastructures.
Copyright (c) 2026 I Made Murwantara, Richard Angkawinata http://creativecommons.org/licenses/by-sa/4.0
A new generation of artificial intelligence contributing to improving the image quality
High-resolution (HR) images provide inclusive and critical information, which is substantial for many implementations. Production operation for high-quality images from low-quality images can be costly and time consuming. The main advancement in this domain is produced by enhanced super-resolution generative adversarial network (ESRGAN); the ESRGAN and different deep learning (DL) models exhibit prominent advances in image super-quality. This research proposes introducing the discrete wavelet transform (DWT) as a multi-scale analysis stage that feeds into the network, whereby the frequencies are analyzed before being fed into the generative adversarial networks (GAN). The goal is to enhance the ability to recover edges and fine details, especially in low-resolution images. The performance of this proposed model is implemented, evaluated, and comparatively assessed. Key performance parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), are calculated, which compare the proposed model with other image-improving models (Bicubic, SRResNet, and ESRGAN). The experimental results indicate that the proposed method ESRGAN new yields a good result in image improvement, with a PSNR of (26.22, 26.00, 25.51, and 23.89) and an SSIM of (0.6638, 0.6255, 0.5882, and 0.6286) for four datasets, respectively.
Copyright (c) 2026 Salwa A. Alagha, Hadeel N. Abdullah, Suad Khairi Mohammed http://creativecommons.org/licenses/by-sa/4.0
Phytoarchitecture for buildings based on photosynthetic pathways to combat volatile organic compounds
Volatile organic compounds (VOCs) originating from construction materials and human activities present considerable health hazards in indoor settings. Phytoarchitecture provides a sustainable approach by incorporating vegetation into architectural design to effectively mitigate pollutants. This research seeks to define criteria for plant positioning according to photosynthetic pathways (C3, C4, and crassulacean acid metabolism (CAM)) to optimize VOC absorption. Employing a systematic literature review methodology, data on plant physiology, and leaf morphology were examined to establish a design framework. The findings suggest that the positioning of plants should be based on their stomatal opening cycles: CAM plants, which absorb carbon dioxide at night, are optimal for indoor bedrooms, whereas C3/C4 plants are more appropriate for daytime active areas and outdoor facades. Additionally, plants exhibiting narrow leaf profiles and elevated stomatal density exhibit enhanced VOC removal efficacy. It was determined that synchronizing architectural design with plant photosynthetic cycles establishes an efficient, passive air purification system that improves both indoor environmental quality (IEQ) and building aesthetics.
Copyright (c) 2026 Ganjar Samudro, Harida Samudro, Dwi Rinnarsuri Noraduola, Sarwoko Mangkoedihardjo, Azzah Nazihah Che Abdul Rahim http://creativecommons.org/licenses/by-sa/4.0
Assessment of deep learning based Hindi Odia bidirectional machine translation system
India is a vast nation with a diverse range of cultures and languages. Most Indians choose to use their native languages when communicating with machines. To integrate smart technologies into every facet of Indian society, efficient systems that can positively identify Indian languages must be established. Machine translation (MT) studies comprise most of the natural language processing (NLP) in the era of multilingual computer-human interaction. Till now, less emphasis has been placed to develop MT systems among Indian languages. Yet again, building a qualitative and quantitative corpus in these languages is challenging. This work focuses on two Indic languages for the development of a Hindi to Odia bidirectional machine translation system (HOBMT). Bilingual evaluation understudy (BLEU), word error rate (WER), character error rate (CER), and metric for evaluation of translation with explicit ordering (METEOR) evaluation metrics are used to assess the accuracy of the translation. The most advanced sequential deep learning (DL) models, such as recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent unit (GRU), are used in this study. In this research, RNN is observed with improved translation results due to its sequential data handling with context preservation.
Copyright (c) 2026 Subhashree Satpathy, Smitaprava Mishra, Ajit Kumar Nayak http://creativecommons.org/licenses/by-sa/4.0
Analysis of weight loss rate, color, sugar content, and total acidity in tomatoes during storage with edible coating
Tomatoes, as climacteric fruits, offer numerous advantages, making them popular among many individuals. However, they are prone to mechanical and microbiological harm, which is why applying an edible coating is a viable post-harvest handling technique for tomatoes. A combination of corn starch, chitosan, and glycerol serves as an effective edible coating, with corn starch providing hardness, chitosan offering antimicrobial properties, and glycerol acting as a plasticizer. This research seeks to assess the rate of weight loss, color, sugar content, and total acidity in tomatoes during storage with an edible coating. The preparation of the edible coating involves using 2% (w/v) corn starch, 1.5% (w/v) chitosan dissolved in 0.5% (w/v) acetic acid, and 0.25% (w/v) glycerol. The tomatoes were immersed in the edible coating twice, with each immersion lasting 1 minute and a drying period of 5 minutes. Subsequently, the tomatoes were stored for 15 days, with observations made every 5 days. The edible coating has been shown to slow down the deterioration of tomatoes during storage by forming a barrier that restricts the movement of water and air, which are essential for the physicochemical reactions involved in the tomato ripening process.
Copyright (c) 2026 Rafli Zulfa Kamil, Fahira Adibah Thalib, Nurwantoro, Fariz Nurmita Aziz, Heni Rizqiati, Nurul Hasniah http://creativecommons.org/licenses/by-sa/4.0
The physiological and ecological characteristics of some phytoremediation plants for wastewater treatment: a review
Phytoremediation has emerged as an effective method for mitigating heavy metal contamination in waterways. This study synthesizes literature to assess the physiological and ecological characteristics enhancing the efficiency of five plant species traditionally recognized for their phytoremediation potential: Napier grass (Pennisetum purpureum), Phragmites australis, Typha spp., Pistia stratiotes, and Eichhornia crassipes. A systematic review methodology was utilized, focusing on data from Proquest, EBSCO, and Scopus, prioritizing studies based on pollutant absorption capacities, publication quality, and recency. Key parameters examined include the identification of plant species, growth conditions, pollutant absorption efficiency, ecological roles, and physiological characteristics. Phragmites australis is noted for its nutrient uptake and sediment stabilization, while Eichhornia crassipes demonstrates high heavy metal absorption and rapid growth. Pistia stratiotes effectively removes heavy metals and enhances water quality, and Typha spp. aids in nutrient removal while providing habitat. Napier grass contributes to biomass production and soil stabilization. The integration of these species into wastewater treatment systems not only improves water quality but also fosters sustainability and ecological health. This study emphasizes the potential utility of these plants in environmental management initiatives, highlighting the necessity for careful monitoring to mitigate associated ecological risks.
Copyright (c) 2026 Sri Sumiyati, Anik Sarminingsih, Budi Warsito, Muliyadi Muliyadi http://creativecommons.org/licenses/by-sa/4.0
Impact of heavy metal and pesticide contamination in water and soil on hemoglobin synthesis and lymphocyte counts
Heavy metal and pesticide pollution represent a significant public health concern, particularly due to their detrimental effects on hematological health. This study investigates the levels of heavy metals, specifically lead (Pb), cadmium (Cd), and arsenic, and organophosphate pesticides in water and soil within five sub-districts of Jeneponto City, utilizing a quantitative descriptive analysis methodology with the simple random sampling over a six-month period. Analytical techniques employed include atomic absorption spectrophotometry (AAS) for heavy metals and UV-Vis spectrophotometry for pesticides, complemented by blood analyses for hemoglobin and lymphocyte counts. Results reveal that soil samples exhibited heavy metal concentrations exceeding regulatory thresholds, with copper (Cu) and Cd identified as particularly concerning. While most organophosphate pesticide levels were below established limits, concerns regarding potential residue accumulation persist. Exposure to these pollutants has been linked to disrupted hematopoiesis and immune system impairments, potentially resulting in anemia and heightened vulnerability to infections. This study underscores the critical need for continuous monitoring of heavy metal and pesticide levels in environmental matrices to mitigate adverse health outcomes and protect ecosystems. Regular assessments are vital for public health policy and environmental management strategies.
Copyright (c) 2026 Artati Artati, Nuradi Nuradi, Herman Herman, Asyhari Asyikin, Nurisyah Nurisyah, Ratnasari Dewi, Rafidah Rafidah, Aan Yulianingsih, Asriyani Ridwan http://creativecommons.org/licenses/by-sa/4.0
Effects of Phaleria macrocarpa and Averrhoa bilimbi L. on fasting blood glucose in diabetic rats
Traditional ingredients have been proven to contain many substances that can be used to prevent and treat diabetes. This study aimed to investigate the effects of a combination of Phaleria macrocarpa and Averrhoa bilimbi L. extracts on fasting blood glucose levels. A randomized, post-test-controlled group design was used. Type 2 diabetes mellitus was induced in 36 Sprague Dawley rats by feeding them a high-fat diet for 2 weeks, followed by the administration of streptozotocin (STZ) and nicotinamide to induce type 2 diabetes mellitus. The rats were divided into six groups. The treatment was administered for 21 days, and fasting blood glucose levels were measured. The data were analyzed using one-way analysis of variance (ANOVA). The combined dose of Phaleria macrocarpa and Averrhoa bilimbi L. extract effectively reduced fasting blood glucose levels over 21 days. Group T1 demonstrated the highest efficacy and did not differ significantly from that of the C+ group. The effective dosage that influenced fasting blood glucose levels was a combination of Phaleria macrocarpa at 750 mg/kg W/day and Averrhoa bilimbi L. at 375 mg/kg. Phaleria macrocarpa and Averrhoa bilimbi L. extract have a synergistic effect, making them a promising natural medication for controlling blood glucose levels.
Copyright (c) 2026 Laila Sholehah, Fathurrahman Fathurrahman, Niken Widyastuti Hariati, Aprianti http://creativecommons.org/licenses/by-sa/4.0
Histopathology liver of the Mus musculus based on the intensity of induction time of ciu alcoholic beverages
The ciu alcoholic drink is a drink made from molasses that is fermented and undergoes a distillation process to obtain 25% ethanol that can cause liver’s damage. The purpose of this study was to determine the difference in the histopathological effect of the liver of Mus musculus treated with different periods. This study was experimental with 5 treatment groups, including negative control and 4 treatments of ciu, a traditional alcoholic drink by administering 0.6 ml/25 g for 3, 7, 14, and 30 days. The results of macroscopic observations showed that there was a difference between the control group and the ciu treatment, namely, the liver was brownish red when given the ciu alcoholic drink for 3 days and the liver organs were blackish red when given for 7 days, and on the liver weight, the longer the ciu traditional alcoholic drink is given, the greater the liver weight. The results of the significant value of the analysis of variance (ANOVA) test were 0.000, so it can be seen that the administration of the traditional alcoholic drink ciu can affect the histopathological of the mice liver, where the organ that is given ciu alcoholic beverages progressively causes tissue damage such as degeneration and necrosis.
Copyright (c) 2026 Magdalena Budi Verena Putri, Fitria Diniah Janah Sayekti http://creativecommons.org/licenses/by-sa/4.0
Food security-based strategies for Subak Kedisan organic rice sustainability
The Petani Kedisan Mandiri as farmers group in Subak Kedisan has implemented an organic rice farming system and obtained organic certification from Seloliman Organic Certification Agency (LeSOS). However, the group faces challenges in product marketing and limited promotion. This study aims to identify internal and external factors influencing organic rice marketing, formulate alternative marketing strategies, and determine priority strategies for the group. The research applied strengths, weaknesses, opportunities, and threats (SWOT) and quantitative strategic planning matrix (QSPM) analyses. The results identified six internal strengths, with high-quality organic rice as the main strength, and eight weaknesses, with reliance on word-of-mouth marketing as the primary limitation. Six external opportunities were found, particularly the increasing market demand for organic rice, while five major threats were identified, including market segmentation limited to upper-middle-class consumers. Based on the analysis, twelve alternative strategies and one priority strategy were formulated. The main strategy is to enhance digital promotion capacity through training in social media and e-commerce utilization. Recommendations include improving digital literacy, strengthening collaboration with external stakeholders, developing digital infrastructure, leveraging healthy consumption trends, and implementing regular monitoring and evaluation.
Copyright (c) 2026 Ni Made Classia Sukendar, Widhianthini, Ni Putu Rahayu Sastra Dewi, Trisha Susana Andrea http://creativecommons.org/licenses/by-sa/4.0
A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality enhancement in multi-feeder distribution systems
Non-linear sensitive loads are increasingly being used in a wide range of industrial and home applications. Particularly, some nonlinear sensitive loads degrade the power quality (PQ) of a multi-feeder distribution system by causing current as well as voltage quality to deviate from normal standards. To address these PQ issues, a unique multi-terminal interline unified power quality conditioner (MT-IUPQC) device has been implemented in a multi-feeder distribution system. This MT-IUPQC is made up of multi-voltage source inverters (VSI) coupled by a common direct current (DC)-linked capacitor, and which is controlled by using proportional integral (PI) control method. However, due to an inappropriate gain setting choice, this PI is not suitable for regulating the DC voltage at the specified voltage level. In this paper, an intelligent fuzzy-logic controlled MT-IUPQC provides an intelligent knowledge set with subjective assessments for improved mitigation of PQ difficulties. The recovered total harmonic distortion (THD) of source current is 2.45%, 2.71%, which are well within IEEE-519/2014 norms and significantly lower than the THD of non-linear sensitive load current of 30.19%, 30.05% in both feeder-1 and 2. In a similar way, the THD of non-linear sensitive load voltage is obtained at 0.43%, which fits well under IEEE-519/2014 norms and is significantly lower than the THD of the voltage source measured at 20.62% in feeder-1.
Copyright (c) 2026 Venna Jaya Lakshmi, Katragadda Swarnasri http://creativecommons.org/licenses/by-sa/4.0
Increasing the efficiency of deep learning performance using adaptive filters
Deep learning algorithms have become one of the most important innovative technologies that have entered almost all areas of life. These technologies perform complex operations and deal with huge data sets. One of the benefits of deep learning is the inherent flexibility in developing approximate estimates for vast and diverse data sets. Data scientists can develop approximate estimates of almost anything using deep learning and neural networks. The main challenges in deep learning include the problem of data quality and quantity while ensuring large, diverse, and high-quality datasets. It also suffers from the problem of providing computational resources due to the high demand for powerful devices, such as processing and memory units. Additionally, it suffers from the problem of interpretability of the case due to difficulty in understanding and explaining typical decisions. This study proposes an innovative method to reduce these problems in the working mechanisms of deep learning algorithms by merging their layers and hybridizing them using adaptive digital filters. These filters help provide devices for efficient resources and memory units, in addition to the capabilities of analyzing and interpreting various states of processing unit availability. In this study, models of hybrid deep learning techniques with adaptive digital filters were designed and implemented, obtaining good results in reducing training error rates, improving the efficiency of outputs, and reducing the computational effort to high levels.
Copyright (c) 2026 Suad Khairi Mohammed, Sabah A. Gitaffa, Reem I. Dawai http://creativecommons.org/licenses/by-sa/4.0
Using machine learning to understand the root causes of type 2 diabetes in Saudi Arabia
This study examines the elevated prevalence of type 2 diabetes mellitus (T2DM) in Saudi Arabia by integrating a large-scale, regionally specific dataset from the Saudi Ministry of Health (100,000 patient records). The primary contribution is this Saudi-focused data integration—addressing a critical gap in prior studies that rely on global datasets (e.g., PIMA, UK Biobank)—and a comparative evaluation of traditional machine learning (ML) models (random forest (RF), adaptive boosting (AdaBoost)) and few shot large language models (LLMs) for early risk detection. Key features include age, body mass index (BMI), HbA1c, blood glucose, hypertension, and smoking history. Class imbalance (9% diabetic) was resolved using synthetic minority over-sampling technique with edited nearest neighbors (SMOTEENN). Ensemble ML models achieved up to 97% accuracy, while few-shot LLM reached 98% across accuracy, precision, recall, and F1-score (area under the curve (AUC) 0.99). Feature importance confirmed that HbA1c and glucose were the top predictors. This regionally tailored, high performing framework enables early detection of T2DM and culturally sensitive interventions in high-prevalence settings such as Saudi Arabia.
Copyright (c) 2026 Mohammad Saeed Al Ghamdi, Alaa Omar Khadidos, Adel Omar Khadidos http://creativecommons.org/licenses/by-sa/4.0
SWOT-TOWS analysis of an electronic health referral system: basis for development strategy
Along with the implementing rules and regulations of Republic Act No. 11223 (Universal Health Care Act) and the national mandate for the digital transformation of basic services, the Philippine health sector faces the imperative to implement an interoperable electronic referral (eReferral) system. This study applies strengths, weaknesses, opportunities, threats (SWOT) and threats, opportunities, weaknesses, strengths (TOWS) to conduct a SWOT–TOWS strategic analysis of internal and external factors affecting the existing healthcare referral system and to generate evidence-based strategies for national eReferral system adoption. The analysis identifies strong policy support and institutional frameworks as key enablers, while fragmented information and communication technology (ICT) infrastructure, absence of Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) interoperability standards, limited bandwidth in rural facilities, and insufficient technical personnel constitute primary barriers. Comparative benchmarking against the UK NHS eReferral service and US EHR-based referral systems highlights critical design gaps in the Philippine context. The proposed strategies, encompassing a centralized system architecture, ICT readiness investments, and compliance with ISO/IEC 27799 health informatics security standards, provide an actionable framework for policymakers, ICT planners, and healthcare administrators. This study contributes to health informatics literature by applying strategic analysis to health IT system design as a novel methodological intersection.
Copyright (c) 2026 Emmanuel Carlos Navarro, Cristina Enriquez Dumdumaya http://creativecommons.org/licenses/by-sa/4.0
Assessment on the performance of bifacial monocrystalline solar photovoltaic panels in residential areas in Pampanga, Philippines
This research compared bifacial photovoltaic (BPV) in Pampanga households in limiter-only, hybrid battery, and net-metering. To evaluate the energy output, performance ratio (PR), and grid dependency, seven setups (5.65-12.5 kWp) were monitored over 31-37 days with the help of data loggers. The net-metered system of 12.5 kWp became fully independent of the grid and produced 206% of the household demands. The most ideal PR (75.17%) was achieved at the 7 kWp net-metered system. Battery-based hybrid systems minimized grid reliance by 56% and systems based on limiters obtained half supply. Net-metered configurations considerably beat the other configurations (p <0.001). Results prove that BPV is viable in tropical homes, which endorses sustainable development goals (SDGs) 7 and SDG 13.
Copyright (c) 2026 Edgardo M. Santos http://creativecommons.org/licenses/by-sa/4.0
Descriptive data mining for multi-shelf product allocation in traditional retail
The business expansion of minimarkets in small cities is one of the serious threats to the sustainability of traditional retail businesses or small independent retailers. Many traditional retailers eventually closed due to their inability to maintain competitiveness, as customers increasingly prefer shopping at modern retail outlets. A well-organized store layout can improve the shopping experience of customers, which has an impact on customer satisfaction and retail competitive advantage. Currently, shelf space allocation in traditional retail is still inattentive, making the placement of products on the shelf random and erratic. Based on these problems, this research aimed to design multi-shelf product allocation according to customer shopping patterns by combining clustering algorithms and market basket analysis (MBA). Clustering aims to divide data points into two different clusters, namely dominant product and less favored product, while MBA aims to identify the customer purchase pattern and preferences. The three MBA scenarios produced four, twelve, and forty rules. The research successfully designed two layouts by utilizing a combination of clustering and MBA algorithms. The utilization of data mining allows traditional retailers to extract information from the database to be arranged into a layout design that fits the shopping patterns and customer preferences.
Copyright (c) 2026 Singgih Saptadi, Wiwik Budiawan, Ary Arvianto, Purnawan Adi Wicaksono, Chaterine Alvina Prima Hapsari, Dhimas Wachid Nur Saputra http://creativecommons.org/licenses/by-sa/4.0
Region of interest (ROI) compression for adrenal tumor images: a hybrid approach
Developments in artificial intelligence have made it considerably easier for specialists to identify and diagnose diseases by applying computer-aided diagnosis (CAD) systems to medical images. This study introduces a novel hybrid compression technique that combines lossless compression of the region of interest (ROI) with lossy compression of the surrounding areas. This approach balances compression-ratio performance and diagnostic image quality by leveraging refined ROI extraction using morphological operations. This study proposes a hybrid ROI-aware medical image compression method designed to enhance the efficiency of adrenal tumor computed tomography (CT) image transmission and storage without compromising diagnostic accuracy. While surrounding regions are compressed using lossy techniques like scalar quantization and Huffman coding, tumor regions found by a reliable automated segmentation pipeline are compressed lossless using a Lempel–Ziv–Welch (LZW) algorithm. While maintaining 95% tumor detectability, an experimental evaluation of 95 annotated CT images produced an average compression ratio of 48.00%, outperforming traditional Huffman compression (20.79%). Peak signal-to noise ratio (PSNR), structural similarity index measure (SSIM), and a new ROI fidelity score were used to measure image quality. This approach is compatible with digital imaging and communications in medicine (DICOM) standards, supports bandwidth-efficient telemedicine, picture archiving and communication system (PACS) optimization, and may be incorporated into artificial intelligence-assisted diagnostic workflows. Future strategies should incorporate multimodal fusion, compression, and adaptive ROI tracking to further enhance clinical usability.
Copyright (c) 2026 Faisel G. Mohammed, Sajaa G. Mohammed, Asmaa Abdul-Razzaq Al-Qaisi, Raghda A. Ali http://creativecommons.org/licenses/by-sa/4.0
Validation of a child emotional learning activity kit using the fuzzy Delphi technique
Early childhood is a critical period for socio-emotional development, yet parents often lack structured tools to reinforce these skills at home. The study aims to obtain the experts’ unanimous agreement on the contents and components of an emotional learning activity kit for children. This study used the fuzzy Delphi method (FDM) to gather feedback from 13 experts in early childhood education. The survey contained 27 items, which used the seven-point Likert scale. FDM data was analyzed using triangular fuzzy numbers (TFN). The results of the study demonstrated consensus of the construct is at a high level. The overall expert consensus agreement exceeds 75%, the overall value of the threshold (d) is 0.2, and the α-cut exceeds 0.5. The value of the learning contents showed that the expert agreement of this kit is highly acceptable. Findings also showed learning material in this kit has a high agreement value, and it can be used to empower teachers and parents to cultivate their children’s emotional learning, bridging the gap between curriculum and home-based learning. Future research should consider expanding the application of this emotional learning activity to children in preschool.
Copyright (c) 2026 Uzzairah Nabila Ahmad Tazli, Zaharah Osman, Nadia Shahira Amiruddin, Mazlina Che’ Mustafa http://creativecommons.org/licenses/by-sa/4.0
Numerical comparison of mathematical modeling approaches on the performances of link coupling beams
The precision of inelastic steel-link response predictions in eccentrically braced frames (EBFs) is contingent on the material model employed. In the absence of comprehensive guidelines concerning constitutive choices for long-link coupling beams (LCBs), this study undertakes a comparative analysis of four models through nonlinear finite element simulations of three American Institute of Steel Construction (AISC) 341-22–compliant specimens under cyclic loading conditions in Ansys. Two mathematical formulations (Ramberg–Osgood and Kaufmann cyclic) and two plasticity-based formulations (multilinear kinematic hardening and isotropic hardening) were evaluated for shear strength, overstrength, and plastic rotation capacity. Mathematical models consistently generate stable hysteresis with uniform stiffness degradation, while plasticity-based models exhibit greater variability. It was observed that all specimens exceeded the nominal shear strength of 271.98 kN, attaining peak strengths of 350.72–391.86 kN (overstrength 1.29–1.44). The plastic rotations range from 0.039 to 0.051 rad, which exceeds the minimum requirement of 0.02 rad for long links. The Kaufmann cyclic model, when combined with multilinear kinematic hardening, has been shown to optimally reproduce key EBF hysteretic behavior. This advancement in model selection for numerical assessment and improvement in seismic performance predictions in performance-based design is a significant contribution to the field.
Copyright (c) 2026 Musbar, Hanif, Khairul Miswar http://creativecommons.org/licenses/by-sa/4.0
Predictive modeling of regional economic growth using agricultural and socioeconomic indicators
Economic growth and food security are closely interconnected dimensions of sustainable regional development, particularly in agrarian regions such as Lampung Province, Indonesia. However, conventional analytical approaches often fail to capture the complex and nonlinear relationships between agricultural productivity and socio-economic conditions. This study aims to analyze the determinants of regional economic growth by integrating agricultural and socio-economic indicators using a random forest–based modeling framework. Secondary panel data from 15 districts over the period 2014–2024 were analyzed, comprising 165 observations and 14 explanatory variables. The results show that agricultural production plays a dominant role, with rice production and harvested area contributing approximately 39.8% and 34.7% of total feature importance, respectively. The model achieved strong predictive performance with a coefficient of determination (R2) of 0.68 and root mean squared error (RMSE) of 6,346.82, indicating that the selected variables explain a substantial portion of gross regional domestic product (GRDP) variation. Socio-economic factors, including poverty rate, per capita expenditure, and human development index (HDI), also contribute meaningfully to regional economic outcomes. These findings highlight the importance of integrating agricultural productivity with social development policies to achieve inclusive and sustainable economic growth. The proposed approach provides a data-driven framework to support regional policy formulation and improve food security strategies.
Copyright (c) 2026 Septafiansyah Dwi Putra, Feri Utomo, Fitriani, Irmayani Noer, Heriansyah http://creativecommons.org/licenses/by-sa/4.0