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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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Building Digital Trust in Jakarta’s Micro and Small Enterprises: From Awareness to Adaptation Marcel, Marcel; Monica, Katherin
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.v7i3.1246

Abstract

Digital transformation opens many opportunities for micro, small, and medium enterprises (MSMEs) while also creating new challenges in security and trust. This study examines how MSMEs in Jakarta build digital trust through basic information security practices using a mixed-method approach. A survey involving 30 MSMEs showed that 70 percent of respondents understood the importance of strong passwords and 80 percent were aware of phishing risks. However, only 40 percent used two-factor authentication and 20 percent followed formal security guidelines. Interviews with ten business owners revealed that awareness often develops after personal experiences with fraud, while adaptive strategies such as self-learning, small internal training sessions, and the use of built-in security tools help them cope with limited knowledge and resources. The integration of quantitative and qualitative findings resulted in a conceptual model of incremental digital trust adaptation that progresses through awareness, practical adaptation, and gradual governance. Theoretically, the model explains digital trust as a continuous and context-based process within MSMEs. Practically, it provides guidance for governments, business associations, and digital platforms in creating simple, scalable, and realistic programs to strengthen the digital resilience of small enterprises.
Adaptive-Delta ADWIN for Balancing Sensitivity and Stability in Streaming IDS Sebopelo, Rodney Buang
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.v7i3.1260

Abstract

In dynamic traffic networks, intrusion detection systems (IDS) must handle dynamic data stream where traffic changes occur, and concept drift is customary. Traditional concept drift detection approaches often experience a challenge between sensitivity and stability, resulting in delayed adaptation and uncontrolled false alarms. This paper proposes an AdaptiveDelta ADWIN framework that tunes sensitivity detectors using online lightweight controllers: Volatility (VC), that tune a delta based on error volatility, and AlertRate Controller (ARC), which modulates the drift alarms frequency. The framework is implemented using Bagging ensemble of Hoeffding Adaptive Trees and evaluated on a network preprocessed traffic dataset. Comparative experiments opposed to a fixed, ultrasensitive delta detector illustrate that adaptive tuning authorizes timely drift detection while maintaining stability, decreasing false alarms by more than 25%, and enhancing predictive overall performance. AdaptiveDelta baseline maintains a stable accuracy approximately 80% 82% accentuating the importance of balancing detection sensitivity with operational stability in streaming IDS implementation. These results highlight the practical value of the proposed framework, which is lightweight, computationally efficient, and suitable for real-time deployment in streaming IDS environments.
ISO/IEC 27005-Based E-Learning Risk Management with Blockchain Architecture: A Case Study of Semarang University Irfan, Muhammad Nur; Ramadhania, Salwa; Hadi, Soiful; Pungkasanti, Prind Triajeng
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.v7i3.1265

Abstract

This study aims to analyze information security risk management in the Semarang University E-Learning System using the ISO/IEC 27005 standard and to design a blockchain-based architecture as a conceptual strategy for improving data security. The implementation of blockchain in this study is limited only to the conceptual design stage, which serves as a risk mitigation framework without direct application to the system. The research method uses a Waterfall approach that includes the stages of risk identification, needs analysis, risk evaluation, adjustment through expert judgment, risk prioritization, and design of a blockchain-based mitigation architecture. Data were collected through quantitative surveys of students, lecturers, and system users, and qualitative assessments from information technology administrators. The analysis results show that the risks with very high priority are R005 with a score of 22.03 related to personal data security, and R007 with a score of 21.03 related to system access failure at critical times. The integration of blockchain in this design serves to improve data integrity, transaction process transparency, and service availability through distributed recording and smart contract-based automatic verification. This study provides novelty by simultaneously combining the ISO/IEC 27005 approach and blockchain architecture concepts in the context of a university e-learning system, resulting in a comprehensive strategic framework for information security risk management. The blockchain implementation in this study is limited to the conceptual design stage.
Reducing Semantic Distortion of Multiword Expressions for Topic Modeling with Latent Dirichlet Allocation Sitopu, Widya Astuti; Nababan, Erna Budhiarti; Budiman, Mohammad Andri
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.v7i3.1266

Abstract

The Makan Bergizi Gratis (MBG) is one of the Indonesian government’s priority initiatives that has received significant coverage in online media. To understand the main themes within these narratives, this study applies topic modeling using Latent Dirichlet Allocation (LDA). However, the results of topic modeling are highly influenced by the preprocessing stage, particularly in handling multiword expressions (MWEs) such as named entities, collocations, and compound words. This study compares two preprocessing approaches: basic and extended, with the latter involving the masking of MWEs. Experimental results show that the extended preprocessing model achieved the highest coherence score of 0.5149 at K=22K = 22K=22, with four other scores also exceeding 0.496, whereas the basic preprocessing model only reached a maximum of 0.3932 at K=10K = 10K=10. Furthermore, cosine similarity scores between topics in the extended model were lower (maximum 0.7406) than in the basic model (maximum 0.8244), indicating that the topics produced were more diverse and less overlapping. These findings highlight the importance of preprocessing strategies that preserve phrase-level meaning to reduce semantic distortion and improve topic coherence and representation-particularly in analyzing media discourse on public policy programs such as MBG.
Blended Learning in Higher Education: Strategic Adaptations at BIUST During the COVID-19 Pandemic Otlhomile, Boitshoko Effort; Mokibelo, Eureka; Sebit, Sebit Mustafa
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.v7i3.1199

Abstract

The study investigated the views of teaching staff members and students about the blended learning strategies that were introduced by Botswana International University of Science and Technology (BIUST) when the university transitioned from the traditional face-to-face pedagogy to blended learning during the outbreak of the Covid-19 pandemic. The findings revealed that while most teaching staff members adapted well to the use of blended learning tools, there were some challenges which including poor infrastructure, such as poor internet connectivity, limited ongoing support and unequal access to technology created difficulties for staff and students. Students valued the flexibility and autonomy that blended learning offered, but highlighted some difficulties related to limited resources and poor internet connectivity. The combination of Diffusion of Innovation theory with Social Constructivist theory provided the research with a complex framework to analyse blended learning adoption because it included both technical and social elements. The findings have important implications for higher education institutions in Botswana and comparable regions, emphasising the need to establish reliable infrastructure, inclusive resource planning, continuous capacity building, and effective communication for creating sustainable blended learning environments.
Predicting Bitcoin and Ethereum Prices Using the Long Short- Term Memory (LSTM) Model Aswadi, M; Ependi, Usman
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.v7i3.1228

Abstract

Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and Ethereum (ETH) using historical data from Yahoo Finance and Binance. To enhance data richness and model robustness, datasets from both sources were vertically merged. The methodological framework included data preprocessing, Min–Max normalization, formation of 24-day sliding input windows, and training across three data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using the Root Mean Squared Error (RMSE). Results indicate that the LSTM model achieved high prediction accuracy, with the lowest RMSE values of 0.0137 for BTC and 0.0152 for ETH using the combined dataset with a 90:10 split. Beyond modeling, a web-based application was developed using Streamlit, enabling users to perform real-time predictions and export results. This study contributes to the field of cryptocurrency forecasting by demonstrating that multi-source data integration significantly improves predictive accuracy and model generalization. The proposed framework offers both theoretical insights and practical tools for researchers and investors in financial technology.
Blockchain Adoption in Healthcare: Enhancing Interoperability, Security and Data Exchange Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
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.v7i3.1267

Abstract

Fragmented data across the healthcare industry increasingly impedes interoperability, compromises data security, and ultimately interferes with safe and quality patient care delivery. This research introduces a framework that uses blockchain technology to enhance interoperability and data exchange in healthcare environments. Leveraging qualitative methods,semi-structured interviews were held with fifteen health care practitioners at various facilities who gave their insights and perceptions of data sharing and blockchain technology. The findings were thematic and conceptualized through the Technology Acceptance Model, focusing on perceived ease of use and perceived usefulness, and the Technology-Organization-Environment framework that examined organizational support and regulatory compliance. Thematic analysis identified four main themes, including (i) factors influencing adoption: ease of use with four participants, usefulness with three participants, organizational support with two participants, regulatory compliance with two participants, and technical infrastructure with two participants. (ii)Application areas included patient data management, billing and payment, and remote patient monitoring; (iii) benefits such as a more effective decentralized system, safer storage of data, and patient empowerment. (iv)Challenges included privacy concerns, the costs of implementation and system failure, and patients' knowledge and stakeholders' digital literacy. The findings suggested that stakeholders knew the potential disruption to any blockchain system. However, major issues needed to be addressed before implementation. This research expands the conversation about innovative solutions to health care interoperability. It exposes potential ways to address the challenges to adoption. Recommendations for future research include examining the scalability and integration of blockchain technology across different healthcare environments and addressing the pressing need for empirical evidence regarding its real-world applications and impacts.
Advancing Inclusive Educational VR: A Bibliometric Study of Interface Design Maguraushe, Kudakwashe; Masimba, Fine; Chimbo, Bester
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.v7i3.1271

Abstract

While virtual reality (VR) has shown transformative potential in education, its accessibility and inclusivity for learners with disabilities remain insufficiently explored. This study offers the first bibliometric mapping of educational VR interface design for inclusivity, analysing 4,735 documents from 1,714 sources (2020-2025) using Biblioshiny and VOSviewer. The analysis reveals a 13.22% annual publication growth rate, an average of 10 citations per document, and an international co-authorship rate of 25.85%, reflecting both rapid expansion and increasing collaboration. Dominant research themes include user experience, usability, and the metaverse, while underexplored areas such as cognitive accessibility and neurodiverse learners highlight emerging opportunities. The findings demonstrate a concentration of scholarly activity in North America and Asia, with limited representation from the Global South. Practically, the study informs developers on designing adaptive interfaces, guides educators in implementing inclusive VR pedagogies, and provides policymakers with evidence for promoting equitable digital learning ecosystems. By identifying trends, gaps, and collaboration patterns, this research advances the discourse on inclusive educational VR and underscores the need for interdisciplinary, AI-driven accessibility strategies that ensure equitable participation for all learners.
Robotic Process Automation Readiness Barriers and Enablers in South Africa’s Energy Supply Chain Motsoeneng, Mariah Thokozile; Segooa, Mmatshuene Anna; Motjolopane, Ignitia; Kgopa, Alfred Thaga
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.v7i3.1281

Abstract

South Africa’s energy industry faces ongoing challenges including power shortages, ageing infrastructure, and supply chain inefficiencies, while, limited empirical evidence exists on how organisations in this industry prepare for Robotics Process Automation (RPA) adoption. This study examines the RPA readiness barriers and enablers within the supply chain of South Africa’s energy industry. The research adopts a qualitative design grounded in the Technology-Organisation-Environment (TOE) framework and the Awareness, Desire, Knowledge, Ability, Reinforcement (ADKAR) change management model to connect technological capability with individual and organisational readiness for change. Data were gathered through semi-structured interviews with 18 professionals representing eight stakeholder groups, including supply chain managers, IT specialists, process improvement leads, and employees affected by automation. Four key readiness barriers emerged: readiness gaps (61 mentions), organisational misalignment (158), infrastructure strain (83), and job security and resistance (60). Corresponding enablers included leadership accountability, RPA governance and alignment frameworks, readiness checklists, structured communication protocols, KPI frameworks, capability audits, investment planning, psychological safety, and regulatory alignment mechanisms. The integration of TOE and ADKAR offers a novel dual-lens perspective that extends existing knowledge. The findings provide practical guidance for managers and policymakers seeking to strengthen organisational systems and structures with human readiness factors in emerging economies.
Expert System for Early Childhood Talent Detection Using Certainty Factor and Dempster Shafer Algorithms Supardi, Supardi; Kirana, Chandra; Ferdian, Ferdian
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.v7i3.1283

Abstract

Early life is a crucial window for recognizing children’s interests and talents that shape later development. This study implements and compares two reasoning algorithms—Certainty Factor (CF) and Dempster–Shafer Theory (DST)—within a rule-based expert system designed to determine early-childhood interests and talents. Observable “symptoms” (behavior, preferences, and responses to stimuli) are mapped to potential talents, including linguistic, musical, logical-mathematical, and kinesthetic intelligences. The CF module computes confidence values from expert-assigned belief weights, yielding a single interpretable score per talent; the DST module aggregates evidence while explicitly representing uncertainty through basic probability assignments over the frame of discernment. We evaluate both methods in the deployed application with respect to accuracy, decision consistency, and response speed. Results show that, for the representative trait set aligning with linguistic indicators, CF produced the highest agreement with expert judgment 84% confidence while DST assigned 65% mass to the same singleton hypothesis, reserving the remainder for competing hypotheses and ignorance. These findings indicate that CF offers a more decisive signal under congruent evidence, whereas DST contributes caution by quantifying residual uncertainty. Together, the dual approach supports transparent and scalable screening of early talents, enabling caregivers and educators to act when support is strong and seek additional observations when uncertainty persists.

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