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Fixed asset management is a crucial component in supporting the performance of local governments. However, the Polewali Mandar Regency Government still faces obstacles in managing assets conventionally, such as the risk of recording errors, duplicate data, and difficulties in field data verification which is time-consuming. This study aims to design and build a Web-Based Fixed Asset Management Information System integrated with Quick Response Code (QR Code) technology as a digital solution for real-time asset data collection, tracking, and monitoring. The approach taken for system development is known as the Waterfall approach, encompassing the phases of analyzing requirements, system design, execution, evaluation, and ongoing support. The creation of the system was carried out utilizing the PHP coding language, the MySQL database system, and Tailwind CSS to achieve a mobile-friendly interface. Data collection techniques were conducted through observation, interviews, and literature studies at the General Affairs Division of the Regional Secretariat of Polewali Mandar Regency. The result of this research is a fixed asset management information system featuring the generation of unique QR Code labels for each asset, category and location management, and reporting features divided into three access levels: Admin, Operator, and Verificator. Based on the system testing results, an average score of above 4.00 was obtained for indicators of ease of use (user-friendly), access speed, and information accuracy. In conclusion, the implementation of this QR Code-based system is proven to increase the efficiency of the asset identification process in the field, minimize manual input errors, and improve accountability in regional asset management in Polewali Mandar Regency.

The development of digital technology has driven significant changes in modern learning methods by integrating various multimedia media. However, multimedia's ability to facilitate learning depends largely on the extent to which its presentation aligns with the principles of learning psychology, particularly those outlined in the Cognitive Theory of Multimedia Learning (CTML). This study aims to examine the role and effectiveness of multimedia in transforming modern learning methods by combining the results of recent empirical research from 2020 to 2024. This study used the Systematic Literature Review (SLR) method by analyzing 22 journal articles obtained from several databases such as Scopus, Web of Science, ScienceDirect, Google Scholar, and Sinta. Research shows that the use of interactive multimedia helps increase student enthusiasm for learning, participation, understanding of concepts, and the ability to remember course material. However, disorganized multimedia design can add unnecessary mental burden, thereby reducing learning effectiveness. These findings suggest that the successful use of multimedia depends not only on the level of technological advancement but also on the quality of learning design that applies cognitive theory. This study provides a comprehensive summary of the latest research and provides tangible benefits for educators in creating effective digital learning content.

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

This study aims to analyze the effect of information technology (IT)-based learning media on improving students' academic performance. With the advancement of digital technology, the use of IT-based media such as interactive presentations, educational videos, Learning Management Systems (LMS), and online quiz applications has become part of modern teaching strategies. This study uses a quantitative approach with a quasi-experimental method. The subjects of the study were secondary school students divided into experimental and control groups. The instruments used include learning achievement tests to measure academic performance and observation sheets to assess the implementation of IT media usage. Data were analyzed using t-tests and simple regression analysis. The results show a significant difference in academic performance between students who used IT-based learning media and those who used conventional methods. The experimental group showed a higher average score compared to the control group. These findings indicate that the use of IT-based learning media, when planned and implemented systematically, can improve students' motivation, engagement, and understanding of learning materials. Therefore, the integration of information technology into the learning process is recommended as an innovative strategy to enhance the quality of education.

This study aims to formulate a Blue Ocean Strategy to enhance business competitiveness through a SWOT analysis approach. The research was conducted using a descriptive qualitative method. Primary data were obtained through interviews with competent parties to identify the internal and external factors of the business, while questionnaires were used as supporting data to capture respondents’ perceptions of the identified factors. The interview results served as the basis for developing the IFAS and EFAS matrices to determine the business position within the SWOT matrix. Furthermore, the SWOT analysis results were used to formulate alternative strategies aimed at creating new value in line with the Blue Ocean Strategy concept. The questionnaire results indicate that respondents’ perceptions tend to support the internal and external factors identified through interviews, thereby strengthening the analytical findings. Based on the results, it can be concluded that formulating a Blue Ocean Strategy through SWOT analysis provides a more innovative and competitive strategic direction. The resulting strategies are expected to help businesses create sustainable competitive advantages and avoid direct competition in saturated markets.

Advances in information technology have driven the transformation of banking services towards digitalization through mobile banking. Bank Central Asia (BCA), one of the largest private banks in Indonesia, offers m-BCA services to facilitate quick, practical, and efficient customer transactions. However, issues such as delayed notifications, system disruptions, and login problems still exist. This study aims to evaluate m-BCA user satisfaction using the PIECES Framework, which consists of six dimensions: Performance, Information, Economy, Control and Security, Efficiency, and Service. A descriptive quantitative approach was used, with a questionnaire distributed to 105 active m-BCA users in South Jakarta. Data were analyzed using mean values to assess satisfaction levels for each dimension, and validity and reliability tests were conducted. The results showed average scores of 4.02 for Performance, 4.08 for Information, 4.08 for Economy, 4.18 for Control and Security, 4.20 for Efficiency, and 4.09 for Service. The overall score of 4.10 falls into the "Satisfied" category. The highest score was for Efficiency, indicating strong support for users’ banking activities, while the Performance dimension scored lower, suggesting room for improvement. Overall, users are satisfied with m-BCA, but system performance improvements are needed for service stability and sustained satisfaction.

This study aims to evaluate the level of user satisfaction with the user interface of the Academic Information System (SIAKAD) at Esa Unggul University using the End User Computing Satisfaction (EUCS) method. This method assesses user satisfaction based on five key dimensions: content, accuracy, format, ease of use, and timeliness. The study employed a quantitative descriptive approach by distributing questionnaires to active Esa Unggul University students as primary system users. The collected data were analyzed using SPSS software to test validity, reliability, and the relationships between variables that influence user satisfaction with the SIAKAD interface. The results show that, in general, users are quite satisfied with the SIAKAD interface, particularly in the ease of use and accuracy dimensions, which obtained the highest scores. This indicates that usability and information accuracy are the dominant factors in creating a positive user experience. However, the timeliness and content dimensions still require further improvement, as they were rated as less optimal in providing fast and comprehensive information. These findings highlight the importance of an intuitive, efficient, and informative interface design in enhancing user satisfaction. This research is expected to serve as a reference for Esa Unggul University in developing and improving its Academic Information System to become more effective, efficient, and user-friendly. Evaluating user satisfaction through the EUCS approach provides valuable insights for system developers to refine the interface, improve information quality, and enhance system responsiveness. Thus, the results of this study are expected to contribute to improving academic service quality and supporting the digitalization of education at Esa Unggul University.

The increasing integration of internet technology in educational institutions requires structured network governance to ensure that digital resources support academic activities effectively. Unrestricted access to online platforms often leads to non-academic usage such as online gaming and social media engagement during instructional hours, which may reduce learning concentration and degrade network performance. This research develops and evaluates a network access control simulation using a MikroTik RouterBoard RB951Ui-2HnD device. The system applies firewall filtering mechanisms, hotspot-based authentication, and bandwidth allocation strategies through Simple Queue configuration. Network segmentation is implemented to differentiate teacher and student access privileges. The study adopts a Research and Development (R&D) approach to design, configure, test, and evaluate the proposed system. Testing results indicate that the firewall configuration successfully restricts access to selected online games (Mobile Legends, Clash of Clans, Roblox) and social media platforms (YouTube, TikTok, Shopee, Instagram, Telegram). Furthermore, bandwidth management demonstrates effective traffic prioritization, ensuring more stable allocation for teacher accounts in accordance with configured maximum limits. The findings confirm that structured firewall and bandwidth policies can improve network discipline, enhance performance stability, and support a controlled digital learning environment in schools.

This study aims to redesign the raw material yarn warehouse layout at PT. XYZ using the Class Based Storage method to improve storage and retrieval efficiency. The main problem identified in the warehouse is random item placement, resulting in relatively long retrieval times of approximately 10–15 minutes per pallet. This research applies a descriptive quantitative approach using a case study method. The data used consists of inbound, outbound, and inventory records of yarn raw materials from November 2024 to April 2025. The analysis was conducted using the FSN (Fast Moving, Slow Moving, and Non-Moving) method through the calculation of consumption rate and average stay, combined with ABC classification to determine storage priority. The results show that 9 types of yarn are classified as Class A, 11 types as Class B, and 11 types as Class C. Based on this classification, a new warehouse layout was designed by placing Class A items near the input-output area, Class B items in the middle area, and Class C items in the back area of the warehouse, thereby improving storage efficiency and reducing retrieval time.

This study aims to redesign the raw material yarn warehouse layout at PT. XYZ using the Class Based Storage method to improve storage and retrieval efficiency. The main problem identified in the warehouse is random item placement, resulting in relatively long retrieval times of approximately 10–15 minutes per pallet. This research applies a descriptive quantitative approach using a case study method. The data used consists of inbound, outbound, and inventory records of yarn raw materials from November 2024 to April 2025. The analysis was conducted using the FSN (Fast Moving, Slow Moving, and Non-Moving) method through the calculation of consumption rate and average stay, combined with ABC classification to determine storage priority. The results show that 9 types of yarn are classified as Class A, 11 types as Class B, and 11 types as Class C. Based on this classification, a new warehouse layout was designed by placing Class A items near the input-output area, Class B items in the middle area, and Class C items in the back area of the warehouse, thereby improving storage efficiency and reducing retrieval time.

Starfruit (Averrhoa bilimbi L.), commonly known in Indonesia as belimbing wuluh, is widely used as a natural acid in traditional cuisine. It is often processed into dried sour starfruit (asam sunti), which can last 1–1.5 years. However, traditional sun-drying methods are inefficient due to weather dependency, long processing times, and inconsistent product quality and color. This study aims to design and develop a tunnel-type starfruit dryer equipped with a blower system and heat control based on the Arduino Mega 2560. The research employs a quantitative method to evaluate tool performance. The dryer is cylindrical and supported by key components, including LPG gas as a heat source, a blower for air circulation, and a drum holder. Drying is conducted in six stages, each lasting 120 minutes, totaling 12 hours to achieve optimal dryness. Temperature monitoring at three points (T1, T2, T3) uses a MAX6675 sensor with a thermocouple connected to the Arduino Mega 2560, while weight measurement is done manually. Results indicate the tool functions effectively. A denser drying chamber and proper blower installation are recommended to ensure even heat distribution and improved drying efficiency for community use.

Starfruit (Averrhoa bilimbi L.), commonly known in Indonesia as belimbing wuluh, is widely used as a natural acid in traditional cuisine. It is often processed into dried sour starfruit (asam sunti), which can last 1–1.5 years. However, traditional sun-drying methods are inefficient due to weather dependency, long processing times, and inconsistent product quality and color. This study aims to design and develop a tunnel-type starfruit dryer equipped with a blower system and heat control based on the Arduino Mega 2560. The research employs a quantitative method to evaluate tool performance. The dryer is cylindrical and supported by key components, including LPG gas as a heat source, a blower for air circulation, and a drum holder. Drying is conducted in six stages, each lasting 120 minutes, totaling 12 hours to achieve optimal dryness. Temperature monitoring at three points (T1, T2, T3) uses a MAX6675 sensor with a thermocouple connected to the Arduino Mega 2560, while weight measurement is done manually. Results indicate the tool functions effectively. A denser drying chamber and proper blower installation are recommended to ensure even heat distribution and improved drying efficiency for community use.

Corn is a strategic agricultural commodity that contributes significantly to food security and economic development. Cilacap Regency, particularly Jeruklegi District, has considerable potential for corn production. However, the Wanasri Women Farmers Group (KWT) in Jeruklegi Wetan Village has not yet optimized corn utilization due to production and marketing constraints, resulting in limited value addition.This study aims to analyze the value added and production process efficiency of corn wonton chips as a healthy processed product to support the economic independence of women farmers. The study applies the Hayami value-added method and descriptive analysis to assess production efficiency. The findings show that processing 1 kg of corn into 15 packages of corn wonton chips generates an added value of IDR 98,500, with a value-added ratio of 54.72% and a profit rate of 49.16%. These results indicate that corn processing provides substantial economic benefits and is financially feasible. Improving production efficiency and cost control can further enhance profitability and sustainability. Overall, value-added processing of local corn has strong potential to increase income and strengthen the economic resilience of women farmers’ groups.

Corn is a strategic agricultural commodity that contributes significantly to food security and economic development. Cilacap Regency, particularly Jeruklegi District, has considerable potential for corn production. However, the Wanasri Women Farmers Group (KWT) in Jeruklegi Wetan Village has not yet optimized corn utilization due to production and marketing constraints, resulting in limited value addition.This study aims to analyze the value added and production process efficiency of corn wonton chips as a healthy processed product to support the economic independence of women farmers. The study applies the Hayami value-added method and descriptive analysis to assess production efficiency. The findings show that processing 1 kg of corn into 15 packages of corn wonton chips generates an added value of IDR 98,500, with a value-added ratio of 54.72% and a profit rate of 49.16%. These results indicate that corn processing provides substantial economic benefits and is financially feasible. Improving production efficiency and cost control can further enhance profitability and sustainability. Overall, value-added processing of local corn has strong potential to increase income and strengthen the economic resilience of women farmers’ groups.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.