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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 780 Documents
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Linking IT Governance to Organizational Performance in Higher Education: The Role of Digital Capability and Organizational Agility Amrullah Amrullah; Khairul Imtihan; Baiq Yulia Fitriyani; Mardi; Muhamad Rodi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1581

Abstract

This study examines how IT governance contributes to organizational performance through information systems (IS) success, digital capability, and organizational agility in higher education institutions. It addresses a critical gap by exploring why improvements in IT governance and system quality do not always translate into higher organizational capabilities and performance. A quantitative survey was conducted with 342 respondents from higher education institutions in Nusa Tenggara Barat (NTB), Indonesia, and analyzed using PLS-SEM. The results show that IT governance significantly improves system, information, and service quality. However, these dimensions do not significantly influence digital capability, and digital capability does not significantly affect organizational agility. Instead, organizational agility is the only construct that significantly enhances organizational performance, while IT governance shows no direct effect. These findings suggest that IT value creation is not linear but depends on the organization’s ability to translate technological resources into adaptive capabilities. This study provides empirical evidence on the indirect role of IT governance and offers a contextual clarification of IS success and digital capability relationships within NTB higher education institutions.
Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection Kharisma, Devi Nova; Prastya, Ifnu Wisma Dwi; Saida, Ita Aristia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1473

Abstract

Sleep disorders are health problems that can affect quality of life and have the potential to increase the risk of various chronic diseases. Therefore, a computational approach is needed to accurately and efficiently classify sleep disorders. The ANN model used has a two-layer hidden architecture with 128 and 64 neurons, respectively, and uses the ReLU activation function, equipped with a dropout layer to reduce overfitting. Three neurons with a softmax activation function make up the output layer, which produces probabilities for every class. To improve model performance, three feature selection methods were compared, namely Chi-Square, Information Gain, and Pearson Correlation. The test results showed that the ANN model without feature selection produced an accuracy of 89.3%. After feature selection, the model's performance improved significantly. The Chi-Square method produced 8 selected features with the highest accuracy of 97.3%, followed by Information Gain with 5 features and an accuracy of 97.3%, and Pearson Correlation with 3 features and an accuracy of 88.0%. The results of this study demonstrate that selecting appropriate features can significantly enhance an ANN's ability to categorize sleep problems. The proposed approach is expected to be a reference in the development of a more accurate sleep disorder diagnostic aid system.
Coraza-Based WAF with OWASP CRS for SQL Injection in Multi-Domain Web System Zaedil, Muhammad; Syamsuddin, Irfan; Utomo, Muhammad Nur Yasir
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1475

Abstract

This research aims to design and implement a Web Application Firewall (WAF) based on the OWASP Core Rule Set (CRS) to enhance web application protection against SQL Injection attacks. The study was conducted in the web environment of the State Polytechnic of Ujung Pandang, which has more than 80 active subdomains with uniform server configurations, mostly using vulnerable CMSs such as WordPress. The proposed solution integrates Coraza, a Go-based WAF engine, into the Nginx reverse proxy system. The system includes a web-based control panel, JSON-formatted logging, and Redis support for efficient traffic mapping and storage, enabling flexible management of multiple domains. A key contribution of this study is the implementation of a centralized WAF management approach capable of securing more than 80 subdomains within a unified configuration environment. Tests were carried out using five SQL Injection scenarios: URL parameters, form-data, x-www-form-urlencoded, JSON API, and automated tools such as SQLMap. Without WAF, all attacks successfully penetrated the system, whereas with WAF activated, all tested payloads were successfully blocked, manual and automated, was effectively blocked, indicating a significant improvement in defense capability. These results demonstrate that the developed WAF system provides strong protection against SQL Injection attacks and indicate strong potential for enhancing web application security against SQL Injection attacks.
A Dependency- and Trust-Aware Task Scheduling Framework for Efficient Internet of Things Edge Systems Mamidza, Fulufhelo Hopewell; Isong, Bassey
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1489

Abstract

The rapid growth of the Internet of Things (IoT) has significantly increased the number of connected devices, generating massive volumes of data and placing substantial demands on edge and fog computing infrastructures. Traditional resource management approaches often overlook task dependencies, which can lead to inefficient resource utilization, increased execution delays, reduced reliability, and potential security risks in distributed IoT environments. To address these challenges, this paper proposes an improved dependency-aware task scheduling framework designed to operate between edge devices and edge servers. The framework employs directed acyclic graph (DAG) modeling to represent task dependencies and execution order, trust-aware node selection to avoid malicious, overloaded, or unreliable nodes, and Particle Swarm Optimization (PSO) to support adaptive resource allocation under dynamic and heterogeneous workloads. Experimental results demonstrate that the proposed framework achieves an average latency of 50 ms, throughput of approximately 500 transactions per second (tps), and a task completion rate of 98%. These findings indicate that the proposed approach outperforms conventional scheduling methods by improving latency, throughput, reliability, security, and overall task execution efficiency in IoT-enabled edge computing environments.
Comparative Quality of Services and Resource Utilization Analysis of Free5GC and Open5GS in Resource-Constrained Private 5G Networks Lutfianto, Naufal Hanan; Prasetya, Budi; Monita, Vivi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1513

Abstract

This research compares the performance of two widely used open-source 5G core (5GC) platforms, Free5GC and Open5GS, in a resource-constrained private network environment. While previous studies have mainly focused on feature comparison or large-scale deployments, performance under limited computational resources has received less attention, particularly for small-scale enterprise use cases. In this work, both platforms are integrated with UERANSIM to emulate end-to-end 5G communication and evaluated under dynamic user equipment (UE) scaling. Each 5GC instance and simulator component is allocated one CPU core and 2 GB of memory. Performance is assessed using key Quality of Service (QoS) metrics, including throughput, latency, packet loss, and resource utilization (CPU and memory), under both TCP and UDP traffic. The results show that Open5GS consistently provides better performance than Free5GC. It achieves up to 10.58 Mbps throughput compared to 9.22 Mbps and maintains lower latency around 0.72–0.73 ms, while Free5GC reaches up to 1.20 ms as the number of UEs increases. In addition, Free5GC reaches high CPU utilization earlier under increasing load. These differences are mainly related to its microservice-based architecture, which introduces additional processing overhead.
Optimizing Stroke Prediction Using Backward Elimination and SMOTE with C4.5 and K-Nearest Neighbors Pratama, Imam Bagus; Fanani, Ahmad Zainul; Soeleman, M. Arief; Kumalasari, Via Indriani
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1521

Abstract

Early prediction of stroke risk is crucial for reducing mortality and the burden on the healthcare system, but class imbalance and irrelevant features often compromise model reliability. This study analyzes the impact of Backward Elimination and SMOTE on the performance of the C4.5 and K-NN algorithms in stroke prediction. The study used a fixed working subset of 1,239 data points and evaluated four modeling scenarios using Stratified 10-Fold Cross Validation. Model performance was measured using accuracy, precision, recall, F1-score, and AUC. The results showed that Backward Elimination improved model performance on the analyzed subsets. For C4.5, accuracy increased from 70.94% to 73.05%, stroke recall from 83.94% to 85.14%, and AUC from 0.776 to 0.806. For K-NN, accuracy increased from 72.31% to 74.82% and precision from 39.91% to 42.73%, while stroke recall remained relatively stable at 74.30%. These findings indicate that although the improvements are small numerically, the results remain practically relevant as they enhance the balance between sensitivity and class discrimination capability. In the context of stroke screening, reducing false negatives is more important because it helps minimize undetected high-risk cases, although false positives still need to be considered as a consequence of further testing. Overall, C4.5 with Backward Elimination demonstrates more balanced performance, although the results are still limited to the analyzed subset.
Mapping the Research Domains of Digital Monitoring: A Systematic Literature Review and Taxonomy Astuti, Yuli; Purwanto; Susanty, Aries
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1523

Abstract

Digital monitoring is increasingly central to modern information systems, enabling continuous observation of assets, processes, and services through real-time data collection. Although advances in analytics and machine learning support data-driven decisions, monitoring, analytics, and decision-making are still often developed in isolation, limiting effective integration. This study maps digital monitoring research, classifies monitoring characteristics, and identifies gaps in linking monitoring with decision-making. Using a PRISMA-based Systematic Literature Review of Scopus-indexed journal articles published between 2020 and 2025, 97 studies were selected and analysed through thematic synthesis. The review shows that digital monitoring spans nine major domains, with infrastructure, environmental, and manufacturing applications most dominant. The study’s main contribution is a multidimensional taxonomy that classifies monitoring approaches by monitoring object, mode, analytics type, application domain, and information system orientation. This taxonomy also positions digital monitoring within the evolution of information systems toward decision intelligence. Findings indicate that current research remains largely technical, relying mainly on descriptive and predictive analytics, while integration with decision intelligence is still limited. A notable gap appears in digital service contexts, especially proactive user-experience monitoring in Internet Service Providers (ISP).
Mapping the Global Landscape of Electronic Supply Chain Management (e-SCM): A Bibliometric and Visual Analysis Kalua, Aditya Lapu; Wibowo, Mochamad Agung; Latumakulita, Luther Alexander
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1525

Abstract

This study maps the intellectual structure of global electronic supply chain management (e-SCM) research through a bibliometric analysis of Scopus-indexed publications published between 2015 and 2025. The retrieval workflow began with the Scopus query TITLE-ABS-KEY ("supply chain management") and was followed by structured interface-based refinement using pub- lication period, subject area, and document type constraints to construct the final analytical corpus. Bibliometric performance indicators were analyzed using the Bibliometrix R- package, while science mapping and network visualization were conducted using VOSviewer. The findings show that the e-SCM literature is organized around five major thematic concen- trations: sustainability in supply chain management, environmental and circular-economy integration, operational decision support and risk analytics, sectoral and stakeholder coordi- nation, and the recent acceleration of blockchain, Industry 4.0, and digital transformation. Co-authorship and country-level mappings indicate a multicentric global research structure led by China, India, and the United Kingdom, while temporal overlay visualization shows a marked shift toward digitally enabled governance and resilience-oriented research during 2022–2023. These results provide an evidence-based synthesis of the evolution of the field and a replicable bibliometric foundation for future sector-specific studies in sustainability- sensitive supply networks.
AI-Based Assignment Marking in African Open and Distance e-Learning Institutions: A Systematic Review Mafuhure, Tirivashe
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1530

Abstract

The rapid growth of student enrolment in African Open and Distance e-Learning (ODeL) institutions has intensified pressure on assessment systems, particularly in assignment marking, moderation, and feedback provision. Artificial Intelligence (AI) offers a promising solution for improving the scalability, consistency, and timeliness of assessment processes. However, evidence on the implementation, effectiveness, and governance of AI-assisted assessment in African ODeL institutions remains fragmented. This study synthesised literature published between 2019 and 2025 to evaluate the extent to which African ODeL institutions have utilised AI techniques in assignment marking. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of major academic databases identified 18 studies that met the inclusion criteria. The review examined AI techniques used, assessment types, evaluation methods, and reported challenges. Findings show that Machine Learning (ML), Natural Language Processing (NLP), and generative AI are the most frequently applied techniques, mainly in text-based assessments such as essays and short-answer responses. Although studies report gains in grading efficiency, consistency, and feedback generation, adoption remains constrained by infrastructural limitations, fairness concerns, linguistic diversity, weak governance frameworks, and limited empirical validation. Sustainable implementation requires standardised human-AI workflows, robust evaluation frameworks, and clear ethical and regulatory guidelines.
Information System Audit in a Spare Parts Distribution Company Using the ITIL V3 Service Strategy Framework Lee, Francka Sakti; Purnomo, Yunianto; Honni; Jusuf, Christian Kurniadi; Winata, Samuel; Saputra, Steven
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1532

Abstract

Information system audits play an important role in evaluating the effectiveness, governance, and strategic alignment of IT services within organizations. This study assesses the capability level of IT service strategy implementation in a spare-parts distribution company using the ITIL V3 framework, with a focus on three Service Strategy sub-processes: Service Portfolio Management, Financial Management, and Demand Management. A qualitative case study approach was employed through semi-structured interviews and document analysis involving key personnel responsible for IT service operations. Capability assessment used a binary scoring method, where Fully Achieved was rated as 1 and Not Achieved as 0, and the aggregated results were mapped to capability levels. The findings show that the organization operates between Level 2 (repeatable) and Level 3 (defined), indicating that core processes have been documented and partially implemented, but are not yet fully integrated across functions. Key gaps were identified in internal coordination, management information, monitoring practices, and customer interface mechanisms. The study recommends strengthening service governance through clearer policies, better cross-functional coordination, and more integrated monitoring to improve IT service performance and business alignment.

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