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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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ERP Adoption in Higher Education: A TAM-Based Analysis of Botswana’s Technical University Otlhomile, Boitshoko Effort; Rafifing, Neo; Mphale, Ofaletse; Mosinki, Joyce; Mabina, Alton
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1198

Abstract

This study investigates ERP adoption at a technical university in Botswana using the Technology Acceptance Model (TAM). It examines how Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioural Intention (BI) influence Actual System Use (AU). Data were collected from administrative staff using a structured survey and analyzed using regression analysis. The results show that PEOU significantly influences both BI (R² = 0.964, p = 0.0029) and PU (R² = 0.864, p = 0.022), indicating that system usability is crucial for ERP adoption. Furthermore, PEOU positively impacts PU (R² = 0.817, p = 0.035), and BI strongly predicts AU (R² = 0.821, p = 0.034). These findings highlight the importance of user-friendly interfaces, comprehensive training programs, and institutional support to ensure successful ERP implementation. The research provides valuable insights for universities aiming to enhance operational efficiency, streamline data management, and improve decision-making processes through effective ERP adoption, particularly in developing countries like Botswana.
Enhancing Mobile Library App User Experience Using HCD and Usability Metrics Siahaan, Ranty Deviana; Sitopu, Tesalonika Aprisda; Marbun, Tabitha Aquila; Bukit, Gerry Benyamin
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1236

Abstract

This study analyzes the improvement of usability metrics and the correlation between usability variables and user experience in the development of a mobile-based digital library application. Using the Human-Centered Design (HCD) approach, the study employed Concurrent Think Aloud (CTA) and Post-Study System Usability Questionnaire (PSSUQ) for usability evaluation. The focus was on four main usability metrics: effectiveness, efficiency, satisfaction, and learnability. The study involved 100 respondents from IT Del students. The results showed significant improvements in all usability metrics: effectiveness increased from 88% to 100%, efficiency rose from 0.087 to 0.148 goals/second, satisfaction improved from 82.05% to 87.05%, and learnability improved with the number of failed tasks reducing from four to zero. Multiple linear regression analysis revealed a strong positive correlation between usability metrics and user experience, with an R² value of 0.665, meaning 66.5% of the variation in user experience can be explained by the usability metrics. All usability metrics positively contributed to improving user experience. These findings confirm that applying HCD and systematic usability evaluation can significantly enhance the quality of digital applications, particularly for mobile-based libraries, and offer valuable insights for the design of digital library apps in higher education contexts.
Predicting Accounts Receivable of the Social Security Administration for Employment Using LSTM Algorithm Khansa, Ainna; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1274

Abstract

This study explores the use of Long Short-Term Memory (LSTM) networks for predicting outstanding contributions from employers to the BPJS Ketenagakerjaan, Indonesia’s social security agency. The research aims to address the challenges BPJS faces due to delayed or unpaid contributions, which impact the institution's operational stability and financial health. The LSTM model, a deep learning technique well-suited for time-series prediction, was applied to historical data from BPJS Ketenagakerjaan to predict overdue contributions across three different training-validation splits: 70:30, 80:20, and 90:10. The results demonstrate that the 80:20 split achieved the highest validation accuracy of 84.71%, offering the optimal balance between training data and model generalization. The model's ability to predict overdue contributions with high accuracy could significantly improve BPJS's receivables management, allowing for more proactive financial planning and risk mitigation. The study also highlights the integration of an attention mechanism within the LSTM model, enhancing its predictive capabilities by focusing on the most relevant historical data. This research contributes to the field of predictive analytics in public sector financial management, showcasing the potential of machine learning in enhancing the efficiency and effectiveness of social security programs.
A Unified Framework for Theoretical and Experimental Evaluation of Classical and Modern Sorting Algorithms in Real-Time Systems Wiredu, Japheth Kodua; Akobre, Stephen; Aabaah, Iven; Wumpini, Umar Adam
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1287

Abstract

This paper presents a theoretical and experimental evaluation of eight popular sorting algorithms HeapSort, QuickSort, MergeSort, Parallel MergeSort, TimSort, IntroSort, Bitonic Sort, and MSD Radix Sort—assessing their suitability for real-time computing environments. The study combines algorithmic analysis with large-scale benchmarks across various input distributions (random, almost sorted, reverse-sorted) and data scales, focusing on execution time and memory usage. Results show that hybrid and adaptive algorithms outperform classical ones. TimSort had the shortest execution times (as low as 1.0 ms on sorted data), and IntroSort showed consistent performance across data types (11-13 ms on random inputs) with minimal memory (<7.90 MB). HeapSort maintained predictable O (n log n) behavior, suitable for hard real-time constraints, while QuickSort and MergeSort had lower latency but higher memory usage. These findings are significant for latency-sensitive applications like high-frequency trading and sensor data processing. The study recommends using hybrid algorithms like TimSort and IntroSort for general-purpose workloads, providing evidence-based guidance for real-time system design.
Perceptions and Key Factors Influencing the Concept of Smart Bangladesh Talukdar, Md. Monowar Uddin; Alam, Khayrul; Ferdous, Shakila; Akter, Ripa
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1289

Abstract

This study explores the concept of a smart Bangladesh, focusing on the roles of smart citizens, government, society, and economy. It argues that leveraging technology is not limited to digitizing government services but also involves transforming the interactions between citizens, society, the economy, and the government. The findings highlight the significant influence of smart citizens and a smart economy on this concept, emphasizing its relevance for both developing and underdeveloped countries. A total of 179 responses were collected using random sampling, ensuring comprehensive coverage for structured interviews based on the Likert scale. The impact was analyzed using inferential statistics with SmartPLS (Version: 4.0.9.9). Bibliographic data spanning from 2018 to 2023 were visualized using VOSViewer, mapping 214 pieces of literature from the Web of Science (WoS) database to support the concept. Structural Equation Modeling (SEM) and Exploratory Factor Analysis (EFA) yielded an R-square value of 51%. The results confirm the acceptance of hypotheses H1 (β = -0.057, t = 0.730, p > 0.233) and H4 (β = 0.603, t = 5.459, p < 0.000), showing a direct effect on the smart concept. This study presents a holistic approach to sustainable development through technological transformation, consolidating research across smart domains like healthcare, education, agriculture, payments, and grids.
AI-SEC-EDU Conceptual Framework: Securing E-Learning in Low-Income Countries’ Higher Education Institutions Mukalere, Justine; Omona, David Andrew; Ikwap, Agatha Flavia
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1297

Abstract

The evolving digital threat landscape, characterized by sophisticated AI-driven attacks, increasingly targets Higher Education Institutions (HEIs) through e-learning systems. This study introduces the AI-SEC-EDU framework to guide the integration of security controls and AI-enabled intelligence into cybersecurity strategies for e-learning platforms. The framework is based on a narrative review of existing cybersecurity interventions for e-learning in Low-Income Countries (LICs) and their approach to managing cybersecurity in the age of Artificial Intelligence. A search across four databases—ACM, Springer, ScienceDirect, and Google Scholar—in May 2025 identified 621 papers, of which eight met the inclusion criteria using PICO and PRISMA guidelines. The selected papers focused on cybersecurity in e-learning, discussing frameworks, models, and algorithms for platforms like Moodle, Google Classroom, and Coursera, some of which incorporate AI and open-source options. The study identifies three key security risk domains: technological infrastructure, human factors, and institutional governance, all of which are compounded by limited AI integration. Existing measures focus on system hardening but fail to address AI-based threat prediction and human behavior vulnerabilities. The AI-SEC model integrates AI, user awareness, and governance controls to provide adaptive, context-sensitive cybersecurity solutions for e-learning in LICs. This framework serves as a diagnostic and planning tool, aligning policies, institutional practices, and national strategies.
A Lightweight ITSM Framework for Balancing Service Value and Cost Efficiency in Digital MSMEs Marcel, Marcel; Alexander, Jessica
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1301

Abstract

This study explores how Indonesian Micro, Small, and Medium Enterprises (MSMEs) manage IT services with limited staff and budgets, proposing that a simpler, lightweight approach to IT service management (ITSM) is more suitable for this context. A mixed-method case study was conducted with four MSMEs in Greater Jakarta—two technology-based and two non-technology-based. Key performance indicators such as response time, downtime, and customer satisfaction were derived from service logs and customer ratings, while semi-structured interviews with owners and staff were analyzed for recurring themes. Results revealed that non-technology-based MSMEs achieved a median response time of 12.5 minutes and an average satisfaction score of 4.55, while technology-based MSMEs had a median response time of 1.8 hours and an average score of 3.95. Technology firms logged approximately seven hours of downtime per month, compared to 1.5 hours in non-tech firms, indicating a trade-off between faster responses and higher satisfaction at the cost of less systematic documentation and control. All MSMEs utilized freemium SaaS tools, marketplace dashboards, limited-service hours, and no dedicated IT staff to minimize costs. The study proposes a lightweight ITSM framework and checklist, adaptable with free tools, for use in MSME incubators and support programs, advancing ITSM literature for resource-constrained businesses.
Enhancing Smart Wheelchair Control: A Comparative Study of Optical Flow and Haar Cascade for Head Movement Muriyah, Nimatul Ma; Paerin, Paerin; Yulianto, Andik
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1302

Abstract

The development of Artificial Intelligence, particularly in Computer Vision, has enabled real-time recognition of human movements such as head gestures, which can be utilized in smart wheelchairs for users with limited mobility. This study compares two lightweight non-deep-learning methods Lucas–Kanade Optical Flow and Haar Cascade Classifier for real-time head movement detection. Both methods were implemented in Python using OpenCV and tested in four basic directions (left, right, up, and down) under three different lighting conditions: bright, normal, and dim. Each condition consisted of 16 trials per method, resulting in a total of 96 trials. The evaluation focused on detection accuracy and decision time. Under bright lighting, Optical Flow achieved 87.5% accuracy with a decision time of 0.338-1.41 s, while Haar Cascade reached 50% accuracy with 0.616–1.20 s. Under normal lighting, Optical Flow maintained 87.5% accuracy with 0.89–1.21 s, compared to Haar Cascade’s 68.75% accuracy with 0.83–1.25 s. Under dim lighting, Optical Flow improved to 93.8% accuracy with 0.90–1.31 s, whereas Haar Cascade dropped to 62.5% accuracy with 0.89–1.58 s. These findings confirm that Optical Flow delivers more reliable and adaptive performance across varying illumination levels, making it more suitable for real-time smart wheelchair control. This study contributes to the development of affordable assistive technologies and highlights future directions for multi-user testing and hardware integration.
Sentiment Classification of TikTok Reviews on Almaz Fried Chicken Using IndoBERT and Random Oversampling Zaki, Imam Syahputra; Kurnia, Rizka Dhini; Meiriza, Allsela
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1310

Abstract

The socio-political context surrounding the Indonesian Ulema Council's Fatwa No. 83 of 2023, which catalyzed a significant consumer shift, necessitates an accurate measure of public sentiment toward alternative local brands like Almaz Fried Chicken. Analyzing real-time consumer discourse on the challenging TikTok platform, the study utilized a final dataset of 4,374 unique comments to overcome the inherent problem of dataset imbalance and linguistic informality. The core method involved a seven-stage quantitative approach: data collection, preprocessing, sentiment labeling, data splitting (70:15:15), Random Oversampling (ROS), IndoBERT fine-tuning, and evaluation. This pipeline fine-tuned IndoBERT, a Transformer-based model, integrated with ROS applied exclusively to the training data. Evaluation demonstrated that ROS significantly reduced model bias and enhanced performance: Overall Accuracy increased by 2.0% (from 91% to 93%), and the Macro F1-Score improved by 3.4% (from 0.87 to 0.90). Most critically, the F1-Score for the minority Negative sentiment class surged from 0.78 to 0.84, confirming ROS's effectiveness in accurately detecting critical feedback. These findings provide timely, data-driven insights into brand perception amidst the boycott campaign and establish a robust, reliable IndoBERT-ROS methodology for advanced sentiment monitoring in dynamic social media environments.
Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management Brawijaya, Fanny; Almais, Agung Teguh Wibowo; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1313

Abstract

This study aims to develop a structured approach to predict the number of hospital drug purchases using deep learning techniques. The Multivariate Long Short-Term Memory (LSTM) model is designed to capture temporal and contextual patterns including transaction time, polyclinic type, and drug type to improve the efficiency of pharmaceutical management. The model was tested using outpatient transaction data at RSIA Fatimah Probolinggo hospital in East Java, Indonesia, through three configurations (A, B, and C) to determine the optimal parameters. The best model, the Model B1, produces a Mean Absolute Error (MAE) value of 10.239, Mean Absolute Percentage Error (MAPE) of 1.976%, and the Coefficient of Determination (R²) of 0.199, which indicates a high degree of accuracy. The results of the study prove that multivariate LSTM is able to model complex intervariable dependencies and provide superior results than conventional forecasting methods. In practical terms, this model can be used as a decision-making tool for hospital management in planning drug procurement, optimizing inventory, and preventing shortages and overstocks. The application of this model contributes to data-driven pharmaceutical supply chain planning in smart hospital management systems.

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