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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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Improving IT Governance Maturity at Universitas Sebelas Maret Using COBIT 2019 Setyawan, Haidar Hendri; Khadija, Mutiara Auliya; Budianto, Aris
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.v7i3.1200

Abstract

This study evaluates and improves the IT governance maturity of the Directorate of ICT at Universitas Sebelas Maret using the COBIT 2019 framework. The evaluation was driven by increasing IT complexity, resource inefficiencies, and low risk management capability. A case study approach applied COBIT 2019 domains to assess practices and identify gaps, with data gathered through interviews, observations, and document analysis. Significant deficiencies were found in six key processes. The highest gap score is APO12 (Managed Risk) at 1.89, followed by DSS04 (Managed Continuity) at 1.88, DSS01 (Managed Operations) at 1.75, APO14 (Managed Data) at 1.74, DSS05 (Managed Security) at 1.57, and the lowest is APO01 (Managed I&T Framework) at 1.27, with all domains targeting a maturity level of 3. Results indicate current maturity scores fall below expectations, highlighting the need for systematic improvement. A phased strategic plan was developed for short, medium, and long-term priorities, aligned with resources and organizational needs. The study demonstrates that structured implementation of COBIT 2019 can enhance governance alignment, improve risk control, and ensure sustainable ICT performance, providing a roadmap for future IT governance at the university.
Sentiment Analysis of Public Service Using Naïve Bayes Classifier Purnama, Arga Aditia; Sipayung, Yoannes Romando
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.v7i3.1207

Abstract

Public administrative service quality is a crucial factor in citizen satisfaction. This study analyzes sentiment in public service reviews using a text mining approach with the Naïve Bayes Classifier method. The dataset was collected from citizen feedback on online platforms regarding public administrative services. Preprocessing steps included tokenization, case folding, stopword removal, and stemming. The Naïve Bayes algorithm with Laplace smoothing was applied for classification, and performance was evaluated using accuracy, precision, recall, and F1-score. The experiment resulted in an accuracy of 91.2%, precision of 90.3%, recall of 89.7%, and F1-score of 90.0%. The analysis revealed that Service Speed obtained an average score of 3.21, indicating a moderate level of citizen satisfaction in that aspect. These findings suggest that while the Naïve Bayes method is effective for sentiment classification, its greatest value lies in providing actionable insights for public service improvement. Specifically, policymakers can prioritize addressing delays in service speed through simplified procedures, improved staffing, and digital innovation, while maintaining strengths such as officer politeness and effective complaint handling. By leveraging sentiment analysis, public institutions can continuously monitor citizen feedback, identify problem areas, and implement evidence-based strategies to enhance service quality and strengthen public trust.
Development of an Expert System for Vehicle Breakdown Assistance Agostino, Josephat Benard; Mrindoko, Nicholaus
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.v7i3.1210

Abstract

Vehicle breakdowns are a growing problem worldwide, often caused by overheating, oil leaks, battery problems, flat tires, fuel system failures, and other issues. These incidents frequently result in delays, safety hazards, and costly repairs. Existing systems mainly focus on locating nearby mechanics but lack self-diagnostic capabilities. This study presents a mobile-based expert system that offers step-by-step repair instructions, troubleshooting flowcharts, and safety guidelines. The system integrates ensemble machine learning models and rule-based inference to empower users to independently diagnose and resolve minor vehicle faults. The system is designed with offline capability and a user-friendly interface, this tool ensures accessibility and reliability, especially in remote areas. Initial testing demonstrated a classification accuracy of 88% in diagnosing common faults, confirming the system’s effectiveness and potential for real-world deployment.
Detecting Data Leakage in Cloud Storage Using Decision Tree Classification Harahap, Parlindungan; Hasibuan, Muhammad Siddik
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.v7i3.1215

Abstract

Data leakage in cloud storage systems poses a significant security threat, potentially leading to unauthorized access, loss of sensitive information, and operational disruptions. This research proposes a classification model for detecting potential data leakage incidents using the Decision Tree algorithm. The dataset, obtained from the Kaggle public repository, contains user activity logs representing both normal and anomalous behaviors in cloud storage environments. Several preprocessing steps were applied to improve model quality, including handling missing values, removing outliers, and converting categorical data into numerical form. Hyperparameter optimization was performed using GridSearchCV to determine the best configuration for the Decision Tree classifier. Experimental results demonstrate that the optimized model achieved high classification performance, with an accuracy of 70,84%, a precision of 55% for the data leakage class, and an F1-score of 40%. The analysis also highlights the significance of certain features, such as multi-factor authentication usage and access to confidential data, in predicting potential leakage events. This study provides a theoretical contribution by \establishing a robust methodology for applying Decision Tree algorithms to a novel cloud security dataset, offering a scalable and interpretable framework for automated threat detection.
Designing a Zero Trust Architecture for Securing API Gateways in Digital Banking Systems Sitorus, Riama Santy; Hutagaol, B. Junedi
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.v7i3.1219

Abstract

In the era of digital banking transformation, Application Programming Interfaces (APIs) are essential for system integration and customer-facing innovations but also increase exposure to cyber security risks such as credential theft, API abuse, data breaches, and unauthorized access. This research proposes a conceptual Zero Trust Architecture (ZTA) model specifically designed to secure API Gateways in digital banking systems. Adopting a conceptual design methodology comprising literature review, component identification, architectural modelling, standards-based evaluation, and recommendation development the study introduces a framework that integrates core Zero Trust principles. Strong identity verification counters credential misuse, dynamic access control mitigates unauthorized access, encryption protects sensitive financial data, continuous monitoring identifies abnormal traffic, and real-time behavioral analytics prevents API abuse. Each component is mapped to relevant industry standards, ensuring resilience and regulatory compliance. Beyond the conceptual design, the findings highlight practical implications: applying ZTA at the API Gateway strengthens cyber security defenses against modern API threats, supports regulatory readiness, and provides banks with a structured roadmap for secure digital services. The study concludes that the proposed model delivers a comprehensive foundation for secure API communication in digital banking and actionable guidance for future implementation and research.
Smartdesa Application for Hamparan Perak Village Using Crowdsourcing for Community Reporting Tambunan, Salwa Alipia Fadillah; Alda, Muhamad
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.v7i3.1223

Abstract

The development of digital technology presents opportunities to improve public services at the village level. Hamparan Perak Village faces challenges in delivering information, which still relies on bulletin boards, long wait times at the village office for mail administration and limited communication channels for residents to submit reports or complaints. To address these challenges, the multiplatform Smartdesa application was developed with a crowdsourcing reporting feature. Residents can submit reports and vote on other reports to prioritize their handling. The application was built using React Native for Android and Next.js for the web admin system, with Express.js as the backend, MySQL as the database, and JavaScript as the programming language. Testing results show that the application accelerates report processing and encourages active community participation in digital village management. Several obstacles were also identified, such as limited digital literacy among some users and sometimes unstable internet connections. Nevertheless, the Smartdesa application shows great potential for implementation in other villages as part of e-Government or smart village initiatives.
Public Opinion Sentiment Analysis Towards Government Budget Efficiency Policy on Twitter (X) Using the Naïve Bayes Classifier Algorithm Ritonga, Rizki Ramadani; Sriani, Sriani
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.v7i3.1234

Abstract

The government’s budget efficiency program, mandated through Presidential Instruction No. 1 of 2025, represents a strategic initiative to maximize the effectiveness of national (APBN) and regional (APBD) spending while minimizing waste. This policy has triggered diverse public responses, particularly on Twitter (X), which serves as one of the most widely used platforms in Indonesia for expressing opinions openly. This study investigates public sentiment toward the policy by applying the Multinomial Naïve Bayes Classifier algorithm. A total of 1,000 tweets were collected through crawling between January and March 2025 using the keywords “government budget efficiency” and “APBN savings.” The analytical process involved several steps, including text preprocessing, automatic labeling with the Indonesian InSet lexicon-based dictionary, and TF-IDF weighting. The dataset was divided into 80% training data and 20% testing data. Labeling results identified 703 positive tweets and 297 negative tweets. Model performance evaluation using a confusion matrix achieved an accuracy of 77%, precision of 57.14%, recall of 82.76%, and an F1-score of 67.6%. Although this study focuses only on binary sentiment classification (positive and negative), the findings demonstrate that the proposed method is sufficiently effective in classifying public sentiment related to the government’s budget efficiency policy. The results also provide significant insights into public opinion and can serve as a reference for policymakers as well as for future research on social media-based sentiment analysis.
Evaluating Digital Readiness and Teachers’ Perceptions of a Digital Based Performance Appraisal System in Secondary Schools Karuniawan, Heru; Mutawalli, Lalu; Imtihan, Khairul
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.v7i3.1212

Abstract

This study investigates the determinants influencing the adoption of digital based performance appraisal systems in secondary schools by integrating the Technology Acceptance Model (TAM) with the Technology Organization Environment (TOE) framework. A quantitative approach was employed, involving 103 teachers from junior and senior secondary schools, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance Performance Map Analysis (IPMA). The results show that perceived ease of use strongly affects both perceived usefulness (β = 0.377, p = 0.007) and behavioral intention (β = 0.678, p < 0.001). Environmental readiness (β = 0.495, p < 0.001) and technological readiness (β = 0.357, p < 0.001) are significant predictors of perceived ease of use, while organizational readiness (β = 0.269, p = 0.013) significantly influences perceived usefulness. Conversely, the direct effect of perceived usefulness on behavioral intention was not significant (p = 0.142). The IPMA results emphasize that although environmental and technological readiness exhibit relatively high performance, they still present opportunities for improvement to enhance perceived ease of use. These findings highlight that strengthening usability, infrastructure, and organizational support is crucial for increasing teacher acceptance and ensuring the sustainable implementation of digital based performance appraisal systems in schools.
Enhancing Web Application Security with Open-AppSec WAF on CDN Infrastructure Al Imran, Andi Yusdar; Utomo, Muhammad Nur Yasir; Yusri, Iin Karmila
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.v7i3.1218

Abstract

The increasing number of cyberattacks targeting web applications has made security a critical concern, with vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), Broken Authentication, and Cross-Site Request Forgery (CSRF) remaining prevalent in the OWASP Top 10. These threats can lead to data breaches, service disruption, and reputational damage if not properly mitigated. To address this issue, an infrastructure combining Open-AppSec Web Application Firewall (WAF) and Varnish Cache Content Delivery Network (CDN) was implemented on a Moodle-based e-learning platform within a virtualized Proxmox VE environment. Security testing was conducted using OWASP ZAP and Burp Suite under two scenarios: without WAF and with WAF. In the first scenario, OWASP ZAP detected multiple vulnerabilities, and Burp Suite confirmed successful exploitation with 200 OK responses. In the second scenario, all vulnerabilities were eliminated, and all simulated attacks returned 403 Forbidden responses, indicating complete mitigation. Performance tests revealed a manageable overhead, with throughput reaching 115.4 req/sec at 1000 concurrent users, accompanied by a slight increase in response time and latency. These results demonstrate that integrating Open-AppSec with CDN infrastructure can effectively protect against application-layer attacks while maintaining optimal content delivery performance. Limitations of this study include testing within a simulated environment; therefore, future work could validate these findings on larger-scale systems and with real-world traffic to assess broader generalizability.
Impact of NLP Algorithms on Sentiment Analysis Efficiency and Accuracy Triawan, Puas; Tahyudin, Imam; Purwadi, Purwadi
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.v7i3.1222

Abstract

Sentiment analysis plays a crucial role in understanding user perceptions of products and services in the digital era. However, its implementation is still constrained by the need for high computational resources. This research aims to evaluate the impact of implementing transformer-based Natural Language Processing (NLP) algorithms—such as BERT, RoBERTa, and ELECTRA—on the quality and efficiency of sentiment analysis, especially in multilingual and real-time data contexts. This study uses a Systematic Literature Review (SLR) approach with the PRISMA protocol to assess the performance, challenges, and solutions offered by various NLP models. The study results show that transformer-based models consistently outperform traditional approaches; BERT and RoBERTa can achieve accuracy above 95% with F1-scores ranging from 0.92–0.95, while ELECTRA records the highest accuracy up to 98.09% with average precision and recall above 0.90 on e-commerce data. Furthermore, the transfer learning approach has been proven to reduce training time by 50–70% compared to conventional methods, without compromising analysis quality. Nevertheless, the need for large computational power remains a major obstacle. Several strategies, such as model distillation and data augmentation, have proven effective in reducing computational load while maintaining high performance. These findings confirm that transformer-based NLP technology not only improves the quality of sentiment analysis but also opens up innovation opportunities for cross-language and cross-domain applications. This research recommends optimizing models for resource-constrained languages and developing real-time systems to achieve inclusivity and efficiency in modern data processing.

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