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INDONESIA
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.
Arjuna Subject : -
Articles 653 Documents
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Bibliometric Analysis of Cybersecurity Research Trends in Bangladeshi Educational Institutions (2020-2025) Sharmin, Khadija
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1154

Abstract

This study provides a bibliometric analysis of cybersecurity research in Bangladeshi educational institutions from 2020 to mid-2025. Using data from the Scopus database and tools like R and VOSviewer, the results show a steady increase in research output, from 23 publications in 2020 to 77 in 2024, with projections for continued growth in 2025. Key research areas include network security, machine learning, deep learning, and blockchain technologies. Rajshahi University of Engineering and Technology has been a leading institution, with Md. Alamgir Hossain (State University of Bangladesh) being a prominent contributor, publishing 15 articles and accumulating 358 citations. International collaborations have enhanced Bangladesh's global standing in cybersecurity. These findings highlight Bangladesh’s increasing role in cybersecurity research, with implications for addressing local challenges and strengthening national cybersecurity resilience.
Enhancing the Security of Internet of Things Devices through Cybersecurity Framework Macharia, Godfrey M; Mgawe, Bonny; Mvula, Jaha; Sam, Anael E
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1155

Abstract

This study focused on enhancing the protection of IoT devices by assessing the effectiveness of existing cybersecurity frameworks (CSFs), identifying gaps in advanced technology cyber-attack tactics, and developing a comprehensive cybersecurity framework for IoT ecosystems. Technological Acceptance and Zero Trust Security Theories guided the study. A cross-sectional research design and mixed-methods approach was adopted, while semi-structured interviews and Focus Group Discussions provided in-depth qualitative insights. For quantitative data, a questionnaire was used. A total of 93 respondents from HLIs, hospitals, and broadcasting media were selected using purposive and random sampling techniques. Descriptive and inferential statistics were employed to analyze quantitative data. For qualitative data, Atlas.ti 9.0 Desktop was used. The findings revealed cyber vulnerabilities are associated with the spread of imported unsecured IoT devices, user unawareness, and lack of effective cybersecurity frameworks tailored to emerging cyber threats from advanced technologies such as AI, 5G, Edge computing, and Autonomous Systems. In conclusion, a framework was designed to strengthen IoT device security by integrating best practices, policy implementation, and technological safeguards. The study recommends that imported IoT devices should be digitally coded to detect cyber risks and adopt multi-layered ECSF-IoT framework and strengthen end-user cybersecurity education in developing countries such as Tanzania.
LSTM Forecasting and K-Means Clustering for Passenger Mobility Management at Bus Terminals Khairunnisa, Hasna Rizqia; Hendrawan, Aria
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1159

Abstract

Rapid urban population growth has increased the need for efficient public transportation systems, particularly at bus terminals as major mobility hubs. To address operational challenges such as traffic congestion and limited infrastructure, this study proposes an innovative data-driven approach. A hybrid model is applied, integrating Long Short-Term Memory (LSTM) for passenger volume forecasting and K-Means Clustering for mobility pattern segmentation at the Jepara Bus Terminal. Monthly passenger data was utilized, and the K-Means method was applied to group monthly mobility patterns into three categories: low, medium, and high. The optimal cluster selection (k=3) was based on the highest Silhouette score of 0.785, providing clear seasonal insights. Analysis results indicate that September is the peak mobility period, while months like January and February fall into the low category. Furthermore, an LSTM model was trained to predict future passenger volumes. The model's performance was carefully validated and proven accurate, with a Mean Squared Error (MSE) of 0.0304 and a Root Mean Squared Error (RMSE) of 0.1745. These findings confirm that the model is reliable in capturing complex passenger movement patterns. Overall, this study concludes that the combination of LSTM and K-Means is an effective solution for supporting proactive decision-making. The results of this study can assist terminal managers in optimizing resource allocation and formulating more adaptive operational strategies, thereby contributing to the development of a more responsive and efficient intelligent transportation system.
Application of Life Simulation Games in Teaching Network Security and Cryptography Taufani, Agusta Rakhmat; Soeprobowati, Tri Retnaningsih; Widodo, Catur Edi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1161

Abstract

Information security-related mathematical methods are used in the science of cryptography. A collection of methods that offer information security, cryptography is more than just a means of concealing messages. Using only presentation slides or video links at each meeting, the interaction between lecturers and students via SIPEJAR e-learning hinders the Network Security and Cryptography learning process at the State University of Malang (UM) Information Engineering (IT) Undergraduate Study Program. To help students learn more about the area of encoding using SIPEJAR, a game that explicitly explains cryptography was created using these several challenges as the background. The creation of a cryptographic life simulation game is intended to serve as a teaching and learning aid for lecturers and students. Students are expected to better understand related material in a learning atmosphere that is new, more interesting, opens the horizons of the mind, and is more investigative. After going through the equivalence partitioning testing process, in general this system produces a total percentage of 100% in system item test success in the testing process of the 6 item tests carried out and a respondent satisfaction percentage of 84.3%. Thus, the system is running according to the prototype design.
The Future of Work: Digitalisation of Sub-Saharan Africa Labour Markets Genga, Cheryl Akinyi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1165

Abstract

Digital transformation is reshaping global operations by integrating technology into business, fundamentally changing how value is delivered. In Sub-Saharan Africa, this shift is altering work processes and job content, impacting the demand for skills and leading to the displacement of certain roles across all industries. Understanding the effects of digital technologies on the future of work in the region is essential for developing effective strategies. It is important to recognise how these changes will affect labour markets and workers' ability to transition to new opportunities. While technology can create new paths and improve access, it also exacerbates existing inequalities. This study aimed to explore the challenges shaping the future of work in Sub-Saharan Africa. A qualitative research approach and inductive thematic analysis were utilised for this study. The findings highlight that the major challenges affecting the future of work are digital skills, followed by Diversity, equity and inclusion- digital divide, gender inequality and discrimination and lack of DEI initiatives and finally, workforce- unemployment and inadequately skilled workforce. In conclusion, while the future of work in Africa presents significant challenges, it also offers great promise. Realising this potential depends on bold and proactive decisions by policymakers, educational institutions, and businesses. Strategic investments made today can empower the next generation of African workers, innovators, and entrepreneurs to thrive in an increasingly digital and competitive global economy.
Factors Driving Internet Banking Adoption in Guyana: A Study of Developing Countries Sarran, Dave; Mohammed, Ibrahim; DeFreitas, Penelope
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1166

Abstract

Internet banking across banking institutions has grown tremendously in popularity over the past two decades. Internet banking among customers remains a crucial challenge within the banking industry, especially in developing countries. As such, this research investigates the factors affecting internet banking adoption in Guyana by extending the Technology Acceptance Model (TAM) to include information quality, service quality, system quality and computer self-efficacy as predictor variables. The study evaluated hypotheses that these variables influence users’ perceived ease of use and perceived usefulness, which in turn affect actual usage of internet banking services. Data from 160 internet banking customers was collected and analysed using the Structural Equation Modelling (SEM) approach to test eight (8) hypotheses among constructs of the extended TAM model. The findings of the study suggest that service quality positively affects consumers’ perceived ease of use of Internet banking, while computer self-efficacy positively affects consumers’ perceived usefulness to adopt Internet banking. The findings also demonstrated that both perceived ease of use and perceived usefulness significantly impacted the actual usage of Internet banking. The findings of this study offer Guyanese banking institutions useful information, emphasizing the necessity of enhancing service quality standards and funding digital literacy programs to increase the adoption of online banking services.
Hybrid Cloud Architecture for Efficient and Cost-Effective Large Language Model Deployment Xin, Qi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1170

Abstract

Large Language Models (LLMs) have achieved remarkable success across natural language tasks, but their enormous computational requirements pose challenges for practical deployment. This paper proposes a hybrid cloud–edge architecture to deploy LLMs in a cost-effective and efficient manner. The proposed system employs a lightweight on-premise LLM to handle the bulk of user requests, and dynamically offloads complex queries to a powerful cloud-hosted LLM only when necessary. We implement a confidence-based routing mechanism to decide when to invoke the cloud model. Experiments on a question-answering use case demonstrate that our hybrid approach can match the accuracy of a state-of-the-art LLM while reducing cloud API usage by over 60%, resulting in significant cost savings and a ~40% reduction in average latency. We also discuss how the hybrid strategy enhances data privacy by keeping sensitive queries on-premise. These results highlight a promising direction for organizations to leverage advanced LLM capabilities without prohibitive expense or risk, by intelligently combining local and cloud resources.
Ensemble Learning for Software Defect Prediction: Performance, Practicality and Future Directions Isong, Bassey; Igo, Ekoro
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1171

Abstract

Ensemble learning is a leading approach in software defect prediction (SDP), offering improved predictive performance on imbalanced and high-dimensional datasets. Despite growing research interest, persistent gaps remain in model interpretability, generalizability, and reproducibility, limiting its practical adoption. This paper presents a comprehensive analysis of 56 peer-reviewed studies published between 2020 and 2025, spanning both journal and conference venues. Findings show that ensemble methods, especially when combined with sampling, feature selection, or optimisation, consistently outperform single classifiers on important metrics such as F1-score, area under the curve, and Matthew correlation coefficient. Nonetheless, few studies incorporate explainability frameworks, effort-aware evaluation, or cross-project validation. Additionally, most models are static, rely on within-project testing, and depend on legacy datasets such as PROMISE and NASA, which limit external validity. Building on this synthesis, the review highlights future research priorities, including interpretable ensemble architectures, adaptive modelling, dynamic imbalance handling, semantic feature integration, and real-time prediction. Standardised benchmarks, transparent, scalable designs are recommended to bridge the gap between experimental performance and deployment-ready SDP solutions.
A Comparative Analysis of Machine Learning Techniques and Explainable AI on Voice Biomarkers for Effective Parkinson’s Disease Prediction Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1172

Abstract

Parkinson's disease (PD) is a neurological movement disorder that remains difficult to diagnose, although it affects millions globally. Early diagnosis can lead to more effective and improved patient outcomes. Diagnosis through traditional methods is subjective and often lacks transparency, raising concerns about reliability. In this study, the CRISP-DM framework was applied to compare eight ML algorithms, including Random Forest and Support Vector Machine (SVM). Recursive Feature Elimination (RFE) was used to preprocess, balance, refine the data and find the eight most predictive vocal features. With 195 recordings coming from the UCI Parkinson’s Speech Dataset, which contains voice measurements from 31 individuals (23 with PD and 8 healthy controls), Random Forest (Entropy) had the best performance (F₁ = 96.6%, ROC AUC = 0.98). Explainable AI tools (SHAP and LIME) were integrated, allowing both global and instance-level understanding of model predictions thereby identifying measures of pitch variability (MDVP: RAP, spread1, PPE) as key predictors of PD. This research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care. This raises the issue of the urgent need to detect PD early among Africa's aging populations to help protect the cultural heritage contained in the voices of the elders. this research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care.Future work should embark on validating these findings over much more varied cohorts, integrating additional data modalities (e.g., gait, imaging), and enhancing model robustness. Real-time speech analysis-based tools, in the end, will allow remote screening, early intervention, and tailored care.
A Blockchain-Based Digital Library System Integrated with CryptoJS for Enhanced Security and Transparency Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhua; Ajakaiye, Fiyinfoluwa; Aleke, Paschal Obioma
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1176

Abstract

In the context of digital library systems, blockchain presents a promising framework for enhancing the security, integrity, and transparency of operations such as book transactions, cataloging, and user authentication. Library systems face several challenges, including lack of transparency and security vulnerabilities. Previous research efforts have explored various centralized digital library management systems, but they often suffer from single points of failure and insufficient security measures. The methodology involves integrating blockchain technology using CryptoJS for advanced encryption and hashing, the backend was designed using PHP (Laravel), while the technologies used in the front end includes HTML, CSS and Javascript. The blockchain technology was implemented using Cryptojs which provides security by implementing AES encryption to safeguard user credentials and book transaction records, preventing unauthorized usage. The system was tested in a digital library environment and diverse user set, where results demonstrated enhanced data security and improved operational efficiency. The system is scalable and adaptable to academic, research, and public libraries, providing real-time verification of transactions and enhanced protection against unauthorized access. By combining blockchain’s immutability with strong encryption and modern web technologies, the platform delivers a secure, transparent, and future-ready solution for digital library management with 88% effectiveness. Findings indicate that the proposed blockchain-integrated system not only resolves existing issues in digital library management, but also introduces new opportunities for innovation, including real-time transaction verification and improved trust among users.

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