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Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
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Articles 832 Documents
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AI-Assisted Development Tools and Team Dynamics in South African Software Engineering Teams Bassey Isong; Tshipuke Vhahangwele
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1615

Abstract

AI-assisted development tools are widely adopted in software engineering (SE), yet their effects on team dynamics and software delivery outcomes remain poorly understood in sub-Saharan African settings. This paper investigates how AI tool integration influences team roles, collaboration, skill requirements, and software delivery outcomes among South African software development professionals. A mixed-methods design was used, combining a structured survey with thematic analysis of open-ended responses from 40 participants across developer, tester, DevOps, and team lead roles. Multiple linear regression and Spearman's rank correlation were applied to quantitative data; thematic analysis followed the six-phase approach of Braun and Clarke. Findings show that GitHub Copilot was used by 75% of respondents. Interpersonal trust was the strongest predictor of development speed (β = 0.485, p = 0.017), exceeding all AI-specific variables in the model. AI use at the adoption onset reduced development speed; frequency of use increased it. Role transformation was reported by 95% of respondents and predicted team productivity. However, causal inference is not warranted given the cross-sectional design and reliance on self-reported measures. The findings are further constrained by a purposive sample of 40 drawn from networked professional communities, which limits statistical power and generalisability. To the authors’ knowledge, no prior published study has examined AI adoption and team dynamics within a South African SE population, using this combination of methods, though a systematic literature review of that literature was beyond the scope of this study.
An Empirical Comparison of C4.5, Naive Bayes, and KNN for Scholarship Selection Burham Isnanto; Rahmat Sulaiman
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1617

Abstract

Scholarship selection is a critical process in higher education that requires objective, fair, and efficient evaluation of applicants based on academic and socio-economic criteria. However, manual assessment methods are often vulnerable to bias, inconsistency, and administrative inefficiencies, which may affect the transparency and quality of decision-making. This study compares the performance of three supervised machine learning algorithms—C4.5 Decision Tree, Naive Bayes, and K-Nearest Neighbor (KNN)—for scholarship recipient classification. The dataset consisted of 1,500 student records obtained from the KelasAI repository and included ten predictor attributes, namely Grade Point Average, Parental Income, Academic Semester, Family Dependents, Organizational Involvement, Academic Achievement, Regional Origin, Scholarship Type, National Examination Score, and Economic Status. The target variable was categorized into Accepted and Rejected classes. Experiments were conducted using RapidMiner Studio with 10-fold stratified cross-validation to ensure reliable model evaluation. The results showed that Naive Bayes achieved the best performance, with 81.6% accuracy, 81.8% precision, and 81.3% recall, outperforming C4.5 and KNN. These findings demonstrate the potential of machine learning to support more transparent and data-driven scholarship selection processes.
Conceptual Design of a Permissioned Blockchain-Based Asset Management System in Higher Education using Hyperledger Fabric Ichwan Subekti; Irman Hermadi; Yani Nurhadryani
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1618

Abstract

Effective asset management is a fundamental pillar of higher education operations and strategic sustainability. However, existing conventional systems are often hampered by systemic challenges, including operational inefficiencies, lack of transparency, and chronic data integrity issues resulting from fragmented information silos. This study proposes a conceptual design for a higher education asset management system utilizing blockchain technology to address these governance weaknesses. Based on a comprehensive literature review and system architecture modeling, this study synthesizes concepts from asset management, blockchain principles, and enterprise platforms without deploying a functional prototype. The proposed conceptual framework is built on a permissioned blockchain platform, specifically Hyperledger Fabric, and adopts a multi-layered system architecture for modularity. To balance data integrity with storage efficiency, the system conceptually applies a hybrid data storage pattern combining on-chain ledger records and off-chain databases. Furthermore, the business logic is modeled into smart contracts (chaincode) to automate and secure all stages of the asset lifecycle, from procurement to disposal. The study proposes a theoretical blueprint designed to provide a single, immutable source of truth and enforce multi-level digital approvals. The impact of this research offers a conceptual architectural blueprint that aims to shift academic asset management from a reactive administrative task to an automated, transparent governance ecosystem, paving the way for future validation tasks including performance, scalability, usability, and organizational feasibility.
Integrating ISO, CIPP and CMMI Frameworks for Data Privacy Compliance: A System-Level Maturity Assessment with PDCA-Based Architecture in a KBMI Group IV Bank Delfindra Faiz Noorhadi; Mukhammad Andri Setiawan
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1623

Abstract

This study examines the organizational and technical readiness of a systemically important Indonesian bank (KBMI Group IV) in responding to the enactment of the Personal Data Protection (PDP) Law, which necessitates robust privacy engineering and system architecture adaptation. This maturity assessment is conducted as a single-case study based on empirical data collected from two primary respondents: a Business Branch Manager and a Department Head Application Developer. To comprehensively evaluate the system, this study integrates a multi-model framework. First, the Context, Input, Process and Product (CIPP) model qualitatively measures the organization's governance, resources, workflows and policy impacts. These qualitative findings are then translated into CMMI-based process maturity scores. The observed empirical findings reveal an overall maturity score of 3.69, positioning the organization at Level 3 (Defined) as an institutional baseline rather than a sector-wide indicator. The observed findings also expose a regulatory conflict between the PDP Law's 'Right to be Forgotten' and mandatory financial data retention regulations. To address these observed gaps, the study proposes two framework outputs: a comprehensive mapping matrix that aligns regulatory requirements with ISO 27001 and ISO 27701 standards and a conceptual PDCA-based system architecture utilizing data masking and pseudonymization. Although the proposed framework is developed within the context of a single institution, it offers a valuable preliminary foundation for evaluating technical privacy compliance in the banking sector, subject to further validation across a broader range of financial institutions.
A Hybrid SEM-PLS and ANN Approach for Predicting Student Loyalty in Higher Education Learning Management Systems Hamidah; Sarwindah; Hengki; Tri Sugihartono
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1625

Abstract

This study aims to develop a hybrid Structural Equation Modeling–Partial Least Squares (SEM-PLS) and Artificial Neural Network (ANN) approach to analyze student loyalty in Learning Management Systems (LMS) at ISB Atma Luhur. Data were collected from 200 students at ISB Atma Luhur, representing a single-institution sample, and analyzed using SEM-PLS to examine causal relationships and ANN (Multilayer Perceptron) implemented in SPSS to support predictive analysis. The model includes e-service quality, user experience, information quality, and system quality as predictors of satisfaction and loyalty. The SEM-PLS results show that E-Service Quality (β = 0.350), System Quality (β = 0.170), and User Experience (β = 0.292) significantly affect Satisfaction, whereas Information Quality is not statistically significant (p = 0.054). Satisfaction positively influences Loyalty (β = 0.360), and User Experience has the strongest direct effect on Loyalty (β = 0.484). The model explains a substantial proportion of variance (R² = 0.717 and 0.631) with positive Q² values (0.460 and 0.379). Across ten independent runs, the ANN model achieved an average accuracy of 84.88% (SD = 2.82) and an average AUC of 0.949 (SD = 0.003), indicating stable predictive performance, indicating promising predictive performance under the current testing configuration. The findings provide context-specific explanatory and predictive insights into student loyalty in LMS, however, they should be interpreted with caution due to discriminant-validity limitations and the single-institution setting of the study.
CSF and IT-BSC Integration in IS/IT Strategic Management: A Conceptual Model from a Systematic Literature Review Salsabila Nurisma Nadia Salma; Sholiq
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1627

Abstract

In the era of digital transformation, aligning Information Systems and Information Technology (IS/IT) strategies with organizational objectives remains a critical challenge in strategic management. Previous studies have examined Critical Success Factors (CSFs) in Strategic Information Systems Planning (SISP) and the IT Balanced Scorecard (IT-BSC) as a performance measurement framework. However, these approaches are often discussed separately, while their integration within a unified IS/IT strategic management perspective remains limited. This study conducts a Systematic Literature Review (SLR) using the PRISMA approach to examine the roles of CSFs and IT-BSC in IS/IT strategic management. A total of 27 articles published between 2020 and January 2026 were selected from multiple academic databases. The findings show that CSFs primarily support the identification and prioritization of IS/IT strategic initiatives through the SISP process, while IT-BSC is widely used to measure performance, assess alignment, and support structured strategic improvement. The review further indicates that CSFs and IT-BSC should be understood as complementary components. Based on the literature synthesis, this study proposes a conceptual CSF and IT-BSC integration model that links strategic objectives, CSFs, IS/IT needs, IT-BSC perspectives, and performance evaluation. The proposed model is literature derived and has not yet been empirically validated. Therefore, it should be understood as a conceptual basis for future empirical testing and further development in IS/IT strategic management.
User Satisfaction with the Semeton LH Chatbot: An Integrated TAM and DeLone-McLean Approach Lisa Ria Fitriani; Sofiansyah Fadli; Jihadul Akbar
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1629

Abstract

Digital transformation in public services has encouraged the adoption of chatbots as effective and efficient tools for communication and information delivery. The Central Lombok Regency Environmental Agency developed the Semeton LH chatbot to improve public access to environmental service information. This study aims to analyze user satisfaction with the Semeton LH chatbot by integrating the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model. A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, based on data collected from 96 respondents. The measurement model evaluation indicates that several indicators met validity and reliability criteria; however, some constructs still showed limitations in convergent and discriminant validity, particularly in the Average Variance Extracted (AVE) and Heterotrait-Monotrait Ratio (HTMT) values. Therefore, the structural findings should be interpreted cautiously. The results show that Attitude Toward Using (ATU) has a positive and significant effect on Behavioral Intention to Use (BI) and User Satisfaction (US). In addition, Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) significantly influence ATU. Overall, user satisfaction is primarily shaped by positive attitudes formed through perceived ease of use and perceived usefulness.
A Systematic Literature Review of Dissolved Oxygen and Turbidity Monitoring in Biofloc Aquaculture: IoT and Machine Learning for Water Quality Management Candra Zulkarnain; Cucuk Wawan Budiyanto; Mohd Shafie Bakar
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1630

Abstract

Various technologies have been developed to monitor dissolved oxygen (DO) and turbidity in aquaculture, yet integrated evaluations focusing on biofloc systems, particularly those involving IoT and Machine Learning (ML), remain limited. This review analyzed 32 studies published between 2020 and 2026 using the PRISMA 2020 framework to examine DO measurement, turbidity measurement, IoT integration, and ML applications in biofloc aquaculture. To support methodological discussion, several studies from broader aquaculture and water-quality monitoring contexts were also considered. The reviewed literatures shows that IoT-based and manual methods are the most commonly used approaches for DO and turbidity monitoring. IoT systems, mainly based on ESP32, ESP8266, and Arduino platforms, support real-time monitoring and automation. ML models such as Random Forest, LSTM, and CNN-LSTM are frequently applied for water-quality prediction, anomaly detection, and decision support. However, challenges related to sensor calibration, data availability, and model generalization remain. These findings suggest a growing shift toward more intelligent and integrated aquaculture monitoring systems.
Customer Segmentation in an Internet Service Provider: A K-Means Case Study of Telecommunication Company Merleen Januar; Didi Supriyadi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1631

Abstract

PT Lintas Jaringan Nusantara, an internet service provider, faces challenges in utilizing customer data, which is mainly used for administrative purposes such as billing and support, limiting deeper analysis. This study applies K-Means clustering under the CRISP-DM framework for customer segmentation-based service-oriented attributes: internet package, price, and NAS location, using 972 customer records. Categorical attributes were transformed using frequency encoding and manual mapping. Model evaluation using the Elbow Method suggested 3 clusters, while the Silhouette Coefficient indicated that 10 clusters were optimal, improving the score from 0.5471 to 0.7704. The resulting clusters show variations in customer characteristics and provide an exploratory overview of grouping patterns. However, the 10 clusters solution should not yet operationally validated, as stakeholder validation involving marketing and customer service teams is still required to assess interpretability, business relevance, and practical applicability. Further validation using additional customer data or alternative datasets is also recommended. Overall, the findings serve as an initial analytical step to support future data-driven decision-making.
An Empirical Evaluation of Confidence Miscalibration in Vanilla BERT-Based Stress Detection on Social Media Rizaldi; Kusrini; Utami Ema; Agastya I Made Artha
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1634

Abstract

This study evaluates the reliability of confidence estimates produced by a Vanilla BERT classifier for stress detection using the Dreaddit benchmark. BERT-base-uncased was fine-tuned on 3,553 labeled text segments, following the standard split of 2,838 training samples and 715 test samples. The model was assessed as a single diagnostic baseline without additional linguistic features, label smoothing, post-hoc calibration, or other calibration interventions. Evaluation was conducted using discriminative performance metrics, including accuracy, precision, recall, and F1-score, as well as probabilistic reliability metrics, including Brier Score, Expected Calibration Error, Adaptive Calibration Error, and a reliability diagram. The Vanilla BERT model achieved 79.02% accuracy, 78.00% precision, 82.65% recall, and 80.26% F1-score, indicating competitive classification performance for stress detection. However, the calibration results revealed noticeable miscalibration, with a Brier Score of 0.1565, Expected Calibration Error of 0.0847, and Adaptive Calibration Error of 0.0880. The most prominent confidence mismatch occurred in the 0.8–0.9 confidence interval, while the 0.9–1.0 interval contributed the most to Expected Calibration Error due to its larger sample proportion. These findings show that although Vanilla BERT performs reasonably well in distinguishing stressed from non-stressed text, its confidence estimates are not fully reliable. Therefore, this study positions Vanilla BERT as a diagnostic reliability baseline and emphasizes the importance of evaluating stress detection models using both classification performance and probabilistic calibration criteria.

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