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Journal of Sustainable Software Engineering and Information Systems
Published by CV Media Inti Teknologi
ISSN : -     EISSN : 31233007     DOI : https://doi.org/10.58723/jsseis.v2i1.158
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Journal of Sustainable Software Engineering and Information Systems (JSSEIS) focuses on the critical intersection of Software Engineering, Information Systems, and Sustainability and the Environment. The journal aims to advance knowledge and practices in leveraging the principles and methodologies of Software Engineering and Information Systems to address environmental challenges and promote sustainability
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Articles 10 Documents
 1 
Understanding the Capabilities of Data Quality Measurement and Monitoring Normi Sham Awang Abu Bakar; Alea Syaffa Mohd Sabri; Aisya Sufiah Hazlan
Journal of Sustainable Software Engineering and Information Systems Vol. 1 No. 1 (2025): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v1i1.51

Abstract

Background of Study: Ensuring high-quality data is essential for organizations that depend on analytics, automation, and regulatory compliance. Aims and Scope of Paper: This paper explores the foundational concepts and evolving practices of two interrelated capabilities: data quality measurement and data quality monitoring. While measurement focuses on quantifying attributes such as accuracy, completeness, consistency, and timeliness, monitoring emphasizes the continuous detection and alerting of anomalies during data operations. Methods: This paper examines the application of frameworks like Total Data Quality Management (TDQM), ISO 8000, and Data Management Association Data Management Body of Knowledge (DAMA DMBOK), alongside emerging tools such as rule-based engines, metadata-driven platforms, and AI-driven anomaly detection systems. Results: Findings reveal a persistent gap in systems that integrate both measurement and monitoring effectively, hindering long-term data governance. This paper discusses a case study of the Data Quality framework implementation in the Healthcare sector. It was found that the healthcare organization implemented the Total Data Quality Management (TDQM) framework and Apache Griffin to ensure the accuracy, completeness, consistency, timeliness, and validity of clinical and IoT data through continuous monitoring, automated validation, and anomaly detection. Governance mechanisms aligned with ISO 8000 and HIPAA standards ensured full compliance, traceability, and accountability across all data quality and auditing processes. This study contributes to a deeper understanding of how integrated data quality practices can support digital transformation and operational resilience across industries. Conclusion: The paper concludes by recommending the adoption of continuous quality measurement practices aligned with governance policies and supported by both human expertise and automation, arguing that data quality must be embedded as a dynamic and strategic function within the digital enterprise. While measurement emphasizes the quantification of data attributes such as accuracy, completeness, consistency, and timeliness, monitoring focuses on the continuous detection and alerting of data anomalies during data operations.
Personalized E-Portfolio: A Dynamic Web-based Tool for Students’ Professional Growth Muhammad Syamil bin Manaf; Wan Hussain Wan Ishak; Fadhilah Mat Yamin
Journal of Sustainable Software Engineering and Information Systems Vol. 1 No. 1 (2025): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v1i1.64

Abstract

Background of study: Electronic portfolios (e-portfolios) have become vital tools for students to document learning and build professional identity. Yet, many existing platforms are hindered by technical complexity, limited personalization, and low engagement. Aims and scope of paper: This paper introduces a personalized e-portfolio system designed to overcome these issues by applying agile and user-centered approaches, focusing on usability and adaptability in higher education. Methods: The system was developed using HTML, CSS, JavaScript, PHP, and phpMyAdmin through six agile phases: planning, design, development, testing, deployment, and review. User needs were gathered from students and lecturers, while 30 students evaluated the system using the Website Analysis and Measurement Inventory (WAMMI) across five usability factors. Result: Usability testing showed high satisfaction. Learnability (4.47), controllability (4.22), and efficiency (4.17) scored the highest, indicating that the system is intuitive and effective. Participants valued its role in reflection and personal branding, while suggesting improvements in visual design, customization, and integration with platforms like LinkedIn. Conclusion: The study confirms that agile and user-centered design can produce an adaptable e-portfolio system that enhances students’ professional growth and provides a scalable model for higher education institutions.
Development of a Web-Based Industrial Training Student Activity Management System Wan Hussain Wan Ishak; Siti Nur Aisyah binti Abdullah
Journal of Sustainable Software Engineering and Information Systems Vol. 1 No. 1 (2025): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v1i1.65

Abstract

Background of study: Industrial training is essential for bridging academic knowledge and workplace practice, equipping students with technical and soft skills required for future employment. However, traditional documentation methods such as manual logbooks are often inefficient, error-prone, and limit effective supervision and timely feedback. Aims and scope of paper: This paper presents the development of the Web-Based Industrial Training Student Activity Management System (WIT), designed to streamline industrial training management at the School of Computing, Universiti Utara Malaysia. The system aims to enhance communication, ensure accurate reporting, and support sustainable digital supervision. Methods: The WIT system was developed using the Waterfall Model, with stakeholder input incorporated at each stage of requirements, design, development, testing, and maintenance. Usability testing was conducted with 30 participants (students and staff) using the WAMMI framework, which evaluates five key usability dimensions. Result: The evaluation results demonstrated high levels of user satisfaction across all metrics: Attractiveness (91.67%), Controllability (97.5%), Helpfulness (94.17%), Efficiency (92.5%), and Learnability (96.67%). These findings confirm that WIT provides an effective, user-friendly platform for activity logging, reporting, and supervision. Conclusion: WIT successfully addresses challenges in traditional training management by promoting transparency, accountability, and efficient supervision. The system contributes to ICT-driven educational innovation and has strong potential for future scalability, including mobile integration and advanced analytics.
Implementation of a Gemini Api-Based Chatbot Integrated with Mysql Database for Social Security Service Nandito Ananda Ginting; Muhammad Irvi Hafizi Manullang; Yasir Riady; Habieb Pahlevi
Journal of Sustainable Software Engineering and Information Systems Vol. 1 No. 1 (2025): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v1i1.70

Abstract

Background of study: The increasing demand for intelligent and responsive digital services in the public sector has encouraged government institutions to integrate artificial intelligence into their service systems. Social security agencies in Indonesia require an innovative solution to deliver faster, more accurate, and accessible information to citizens. Aims and Scope of Paper: This study aims to design and implement a Gemini API-based chatbot integrated with a MySQL database to enhance social security services through natural and interactive communication. The chatbot was developed during an internship program at the Social Security Office (BPJS Ketenagakerjaan) Medan Kota branch. Methods: The system was developed using Natural Language Processing (NLP) capabilities of the Gemini API to process user input and retrieve structured data from a MySQL database. An admin dashboard was built with the Laravel framework to manage PDF uploads, perform text extraction, and handle user queries in real time. Result: Testing showed that the chatbot could effectively respond to general and data-driven queries, extract and analyze uploaded documents, and improve response time compared to traditional service channels. The integration of Gemini API and MySQL ensures scalability, accuracy, and secure data management. Conclusion: The implementation of a Gemini API-based chatbot integrated with a MySQL database demonstrates how AI-driven conversational systems can modernize public services. It enhances operational efficiency, accessibility, and user satisfaction, supporting the digital transformation of social welfare institutions in Indonesia.
Jitter That Occurred in the Flight Management System During the Vor Operational Check Using the Aeroflex IFR-4000 on the Beechcraft King Air 350I Aircraft Muhammad Ihsan
Journal of Sustainable Software Engineering and Information Systems Vol. 1 No. 1 (2025): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v1i1.98

Abstract

Background of study: Ensuring the proper functioning of the Flight Management System (FMS) is crucial for flight safety, particularly during VOR Operational Checks. Jitter in the FMS can disrupt navigation and compromise the accuracy of flight operations. During an On-the-Job Training (OJT) program at the Balai Besar Kalibrasi Fasilitas Penerbangan (BBKFP), jitter was identified in the FMS of a Beechcraft King Air 350i during a VOR Operational Check using the AeroFlex IFR-4000. Aims and scope of paper: This paper aims to analyze and identify the cause of FMS jitter observed during VOR Operational Checks on the Beechcraft King Air 350i using the AeroFlex IFR-4000. The scope of the study is limited to the analysis of jitter occurrence during the VOR Operational Check process. Methods: A qualitative descriptive method was applied by analyzing data obtained from direct observation of the VOR Operational Check and referencing the aircraft maintenance manual. The analysis particularly examined the relationship between RF signal strength from the AeroFlex IFR-4000 and the occurrence of FMS jitter. Result: The study found that FMS jitter during the VOR Operational Check was caused by low RF signal levels generated by the AeroFlex IFR-4000. Adjusting the RF signal level from -10 dBm to -5 dBm successfully eliminated the jitter issue. Conclusion: FMS jitter has a direct impact on navigational accuracy and thus requires immediate attention during operational checks. This research emphasizes the importance of ensuring adequate RF signal strength in order to prevent FMS jitter, thereby maintaining reliable navigation and flight safety.
Improving the Classification Accuracy of Parang Batik Motifs with High Visual Similarity Through the Integration of GLCM and MobileNetV2 Haryanto; Husna Sarirah Husin
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.158

Abstract

Background: Despite its high aesthetic value, automatic classification of Parang Surakarta batik is difficult due to the extreme textural similarities between sub-motifs. Standard CNN architectures, including MobileNetV2often fail to detect the subtle textural details that distinguish each variation of the motif. Aims: This study develops a hybrid classification model that combines manual and automated spatial texture features to improve identification accuracy on motifs with high visual similarity. Methods: Using a dataset that has been expanded to 120 original images (40 per class) which is then augmented to a total of 1,200 images to ensure stronger model generalization. This methodology hybrid GLCM-MobileNetV2architecture through transfer learning techniques. Features from both methods are combined through feature fusion before being classified using a Dense layer. Result: The hybrid GLCM-MobileNetV2model achieved an accuracy of 99%. This performance outperformed the pure MobileNetV2 method (66.67%) and GLCM-SVM (85%), demonstrating that texture features provide significant discriminatory power against similar repetitive patterns. Conclusion: The integration of GLCM and MobileNetV2 is highly effective for classifying visually similar batik motifs, achieving a superior accuracy of 99% compared to the pure MobileNetV2 (66.67%). This hybrid approach provides a robust and efficient solution for digital cultural preservation on mobile devices.
Software and Information Systems for Sustainability: A Systematic Literature Review of Models, Applications, and Evaluation Metrics Deki Saputra; Eko Risdianto; Mohammad Qais Rezvani; Putri Wiji Rahayu
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.160

Abstract

Background: The existing literature on sustainable software and information systems is fragmented, with research often siloed into specific models, applications, or evaluation metrics without a cohesive overview. This fragmentation hinders the development of a unified understanding necessary for researchers, practitioners, and policymakers to effectively implement sustainability principles. Aims: This study aims to systematically analyze and synthesize research on software and information systems for sustainability. Its scope is to identify the dominant models, primary application domains, and key evaluation metrics used in the field to establish a consolidated understanding and guide future efforts. Methods: A Systematic Literature Review (SLR) following PRISMA screened 314 Scopus documents (2017–2026) to 25 articles, analyzed using Biblioshiny, VOSviewer, and thematic synthesis. Result: The analysis reveals a field in a consolidative phase, dominated by systematic review-based research (76%) focused on theoretical synthesis. While geographically diverse, the research centers on "sustainability" and "information systems" as core themes. A critical gap exists between conceptual frameworks and practical application, evidenced by a scarcity of empirical studies (only 4% quantitative) and the absence of standardized evaluation metrics. Conclusion: This review concludes that while significant progress has been made in mapping the conceptual landscape, the field of software and information systems for sustainability must now prioritize empirical validation, the development of AI-driven systems, and the establishment of uniform measurement standards to bridge the gap between theoretical promise and tangible real-world outcomes.
Butterfly Classification Accuracy Analysis Using EfficientNet with Adam and AdamW Optimizer Amanda Mutiara Bunga; Risma; Fauzan Ulyadda Putra
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.173

Abstract

Background: Accurate butterfly species classification plays an important role in biodiversity monitoring and environmental conservation. However, image-based classification remains challenging due to similarities in wing color patterns and shapes among species. Aims: This study aims to evaluate the performance of butterfly image classification using transfer learning based on the EfficientNet architecture with different optimization strategies. Methods: EfficientNet-B2 and EfficientNet-B3 were implemented as the main models. Image preprocessing techniques, including resizing, normalization, and data augmentation, were applied to improve model performance. The models were optimized using Adam and AdamW optimizers with different learning rates. Performance evaluation was conducted using accuracy, precision, recall, and F1-score. Results: The results show that EfficientNet-B2 optimized with the Adam optimizer (learning rate 5 × 10⁻⁴) achieved the best performance, with a validation accuracy of 92.62% and an F1-score of 92.61%. In comparison, EfficientNet-B3 optimized using AdamW (learning rate 6 × 10⁻⁴) produced slightly lower performance, indicating the influence of optimizer selection and learning rate configuration on model convergence and generalization. Conclusion: EfficientNet-B2 combined with the Adam optimizer provides a stable and effective approach for butterfly species classification. These findings highlight the importance of optimization strategies in improving model performance and support the development of automated systems for biodiversity monitoring.
Comparative Analysis of Machine Learning Algorithms for Diabetes Prediction with Feature and Hyperparameter Optimization Fikri Fakhar Rahmadhan; Fikri Haikal; Muhammad Arif; Muhammad Agung Insani
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.174

Abstract

Background: Diabetes is a chronic disease with increasing global prevalence, making early detection essential. Machine learning has shown strong potential in improving prediction accuracy; however, robust validation and systematic optimization are still required. Aims: This study tries to compare different machine learning methods to predict diabetes using a. reproducible and methodologically sound framework. Methods: The Pima Indian Diabetes dataset (768 samples, 8 clinical features) was used. Six algorithms were evaluated: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting. Hyperparameter tuning was done with GridSearchCV, and the models were checked using stratified 5-fold cross-validation. The performance of the model was assessed using several metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Results: The results show that ensemble methods outperform traditional models. Random Forest achieved the highest The model performed with an accuracy of 98% plus or minus 1.8% and an AUC-ROC of 0.999 plus or minus 0.02, then Gradient Boosting achieved 91% plus or minus 2.1%. Logistic Regression and KNN had lower performance with accuracy scores of 79% plus or minus 2.3% and 77% plus or minus 2.5%, respectively. The analysis of which features are most important found that glucose levels, BMI, and age are the top factors that have the biggest influence. Conclusion: The study demonstrates that ensemble methods combined with hyperparameter optimization and robust validation significantly improve diabetes prediction performance and can support clinical decision-making.
Application of K-Means, Random Forest, and Linear Regression to Improve the Accuracy of Kaggle E-Commerce Shopping Behavior Analysis Azzahra Risa Putri; Anggraini Dwi Olivia; Virha Charoline Josica
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.175

Abstract

Background: Analyzing customer shopping behavior is essential for improving e-commerce marketing strategies. The use of machine learning enables the identification of purchasing patterns and enhances predictive accuracy in understanding customer preferences. Aims: This study aims to improve model accuracy and classification performance in analyzing customer shopping behavior using the shopping_behavior_updated.csv dataset from Kaggle. The scope includes the application of both supervised and unsupervised learning techniques for segmentation, classification, and prediction tasks. Methods: Three machine learning algorithms were applied: K-Means for customer segmentation, Random Forest Classifier for product category prediction, and Linear Regression for estimating purchase amounts. The research involved systematic preprocessing steps, including data cleaning, encoding, scaling, and feature engineering to enhance data quality and model interpretability. Result: The results show that the optimized Random Forest model achieved 100% accuracy. K-Means clustering produced five distinct customer segments with an inertia value of 41,928.17 and a silhouette score of 0.065. However, the Linear Regression model demonstrated poor performance with an R² value of -0.02. Conclusion: The findings indicate that integrating supervised and unsupervised learning methods is effective in identifying customer purchasing patterns and can contribute to improving e-commerce marketing strategies, although not all predictive models yield optimal performance.

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