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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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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach Aabaah, Iven; Wiredu, Japheth Kodua; Batowise, Bakaweri Emmanuel; Seidu, Nelson Abuba
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1018

Abstract

The field of Nursing and Midwifery Informatics (NMI) aims to equip healthcare professionals with the skills to efficiently use emerging technologies in their practice. This research assessed NMI educational programs in Ghana using machine learning techniques to analyze key factors influencing student performance, engagement, and satisfaction. Data was gathered from 1,500 students across C.K. Tedam University of Technology and Applied Sciences, Bolgatanga Nursing and Midwifery Training College, Regentropfen University College, Tamale Nursing and Midwifery Training College, and University for Development Studies. The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. The Gradient Boosting model achieved the highest predictive accuracy at 95%, identifying student engagement and curriculum satisfaction as the most influential predictors of academic success. Additionally, multiple regression analysis revealed that institutional differences significantly influenced academic outcomes, with students at Tamale Nursing and Midwifery Training College outperforming their counterparts at C.K. Tedam University of Technology and Applied Sciences (β = 3.85, p = 0.021), likely due to better alignment between their curriculum and instructional methods. These findings offer actionable insights for curriculum development and healthcare policy planning in resource-constrained settings, advocating for the integration of machine learning tools into academic evaluations. The study presents a scalable predictive model that can be adapted to enhance digital health education in similar low-resource settings worldwide, offering a pathway to more effective and inclusive healthcare education systems.
Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques Panjaitan, Febriyanti; Ce, Win; Oktafiandi, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Putra, Vito Hafizh Cahaya
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1019

Abstract

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.
Implementing IT-Based Succession Planning in University IT Units: Enhancing Operational Continuity Yualinda, Sherla; Adi, Taufik Nur; Fakhurroja, Hanif; Yualinda, Sherli
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1023

Abstract

One of the accredited universities in Indonesia is committed to quality education through the use of information technology. However, the university's IT unit often experiences vacancies in key positions due to high employee turnover, which impacts workload and business processes, especially in handling Request for Change (RFC). While application X supports performance appraisals, it has not been optimized for succession planning. This study explores the potential of application X as a tool for succession planning by integrating the Rothwell and Integrated Talent Management models. The design includes identifying key positions, assessing candidate competencies, preparing development plans, and establishing a structured knowledge transfer system to sustain organizational leadership. Additionally, integrating Large Language Models (LLMs) like ChatGPT is expected to enhance assessment objectivity, provide individual development recommendations, and ensure a more effective leadership transition. The system's role in improving assessment objectivity is vital for unbiased, data-driven decisions, while its contribution to leadership transitions ensures a smoother, more systematic process for maintaining leadership continuity. With features such as candidate search and staff assessment, the system is expected to help organizations select the right replacement and maintain university operations.
Developing an IT-Based Knowledge Sharing System for University IT Units: Integrating Large Model Language Yualinda, Sherli; Adi, Taufik Nur; Fakhurroja, Hanif; Yualinda, Sherla
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1024

Abstract

Information technology companies in Indonesia face the challenge of high employee turnover, which leads to the loss of important knowledge and has an impact on productivity and innovation. This research aims to develop conceptual knowledge sharing and knowledge sharing systems in university IT units, which do not yet have an integrated system for documenting knowledge. Observations show that the ticketing system used can be optimized as a long-term knowledge sharing platform. The designed model includes strengthening the culture of sharing, utilizing social networks within the organization, applying information technology, reward systems, and the SECI model approach. In addition to knowledge repository features, role systems, documentation automation, search, and collaboration modules, the integration of Large Language Models (LLM) such as ChatGPT is expected to improve information search, documentation automation. LLMs play a crucial role in enhancing user interactions by enabling natural language queries, improving search accuracy, and automating knowledge classification. Moreover, they facilitate knowledge extraction from unstructured data, assist in summarizing key insights, and provide adaptive learning capabilities. By leveraging LLMs, the system can increase efficiency, reduce the time required to find relevant information, and ensure knowledge continuity within the organization.
Mapping Trends in Air Quality Research in South Africa: A Bibliometric Analysis, 1998-2024 Sekwatlakwatla, Sello Prince; Malele, Vusi; Toona, Priscilla; Tshilongo, James; Mkhohlakali, Andile; Letsoalo, Refiloe; Mabowa, Happy; Ntsasa, Napo
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1025

Abstract

The foundation of South Africa is the Constitution, which guarantees every citizen access to a safe and healthy environment. Despite a wealth of research on lower-income households, the effects of burning wood for cooking, heating, and comfort in South African homes are also affecting the air quality; even if the government is working very hard to put measures in place to improve air quality, it will be very difficult to accommodate every household in South Africa. South Africa's low-income urban settlements focus on air quality monitoring for policy formulation and strategy building and Lack of garbage removal services and systems is another characteristic of low-income communities that exacerbates ambient air pollution levels. Based on the quantity of South African publications and citations in air quality that are listed in the Scopus and Web of Science databases, the study used bibliometric analysis to look at the country's air quality and the factors that affect it. Data was collected from 1998 to 2024; the results show that air pollution, nitrogen dioxide and emissions are causing a risk to children, and also having a high impact in causing diseases like asthma, respiratory health and climate change is playing a critical role in increasing the risk. Moreover, the word cloud reflects a growing emphasis on certain air pollutants, including NO₂, PM2.5, black carbon, and SO₂. NO₂ has been linked to substantial health implications, including respiratory disorders, asthma aggravation, and cardiovascular issues.
Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture Pratomo, Yudha; Roseno, Muhammad Taufik; Syaputra, Hadi; Antoni, Darius
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1026

Abstract

This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
Clustering of High School Students Academic Scores Using K-Means Algorithm Azzahra, Chairunisa; Sriani, Sriani
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1029

Abstract

The clustering of student subject scores in senior high school is conducted using the K-Means Clustering algorithm. The issue addressed in this study is how to optimally group students based on their academic scores to help schools understand the distribution of student abilities. This clustering is essential as a foundation for evaluating and improving the learning system. The research methodology includes data collection and preprocessing, determining the optimal number of clusters using the Davies-Bouldin Index (DBI), and applying the K-Means Clustering algorithm. The analysis results indicate that the optimal number of clusters is three, with an average DBI value of 1.226. Cluster 0 is categorized as "very good" (46 students), Cluster 1 as "good" (70 students), and Cluster 2 as "less good" (51 students).The clustering results can be utilized for more targeted learning interventions and curriculum adjustments. Schools can implement remedial programs or additional classes for students in the "less good" cluster to improve their academic performance. Meanwhile, students in the "very good" cluster can be provided with advanced learning materials or opportunities to participate in academic competitions. Additionally, clustering outcomes provide valuable insights for refining teaching strategies, allocating resources more effectively, and personalizing learning approaches to suit each student's needs. Furthermore, these clustering results support academic decision-making by enabling educators and administrators to identify student performance trends and address potential learning gaps. This data-driven approach helps schools enhance overall educational quality by adapting teaching methods and policies based on empirical findings.
Enhancing Tourist Experience at Ponot Waterfall with a Mobile E-Ticket Application Using FAST Methodology Nisa, Khairrun; Yahfizham, Yahfizham
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1032

Abstract

Technology played a crucial role in tourism management, particularly through mobile-based e-commerce, which facilitated transactions. This sector supported national development by creating business opportunities, employment, and promoting cultural preservation and environmental sustainability. Ponot Waterfall, located in Tangga Village, Aek Songsongan, Asahan, became a tourist destination due to its natural beauty and conservation efforts. However, the manual ticketing system often led to long queues and inconvenience, especially during holiday seasons. Additionally, the use of paper-based tickets posed environmental pollution risks. As the number of tourists increased, an application-based e-ticketing system emerged as a solution for accessing ticket information, making purchases, and processing online payments. This study aimed to develop a mobile e-ticketing application for Ponot Waterfall using the FAST method to optimize user needs and enhance the visitor experience in e-ticketing system development. The goal was to improve purchasing efficiency, reduce queues, and enhance the overall visitor experience. The application facilitated information access, supported online payments, improved tourism management, and minimized the environmental impact of paper-based ticketing. The results of this study indicated that the mobile-based e-ticketing application at Ponot Waterfall successfully enhanced transaction efficiency, reduced long queues, eliminated the use of paper tickets, and simplified access to tourism information. The majority of users utilized online ticket booking and digital payment methods as the primary features, proving that the system effectively addressed the challenges associated with the previous manual system.
Innovating Cybersecurity in Tanzanian Academia: A Mobile Tool for Combatting Social Engineering Threats Mjema, Lucas Hosea; Mgawe, Bonny Said; Dida, Mussa Ally
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1034

Abstract

Social engineering attacks, including phishing, smishing, and vishing, pose significant threats to higher learning institutions, especially in regions with limited cybersecurity awareness and weak incident reporting mechanisms. This study introduces a novel mobile tool that combines real-time threat detection, streamlined reporting, and personalized training to address these vulnerabilities. Using a mixed-methods approach, we gathered survey data from 395 participants, conducted interviews with 10 IT professionals, and ran a pilot test with 20 users. The proposed tool provides instant scanning of emails/SMS for social engineering content and instant incident reporting alongside interactive, bilingual (English/Swahili) training modules. Results show a substantial improvement in user awareness, 85% of users reported a better understanding of social engineering threats after using the app, and high user satisfaction, with 90% expressing approval of the intuitive interface. The integration of real-time threat analysis and immediate reporting with tailored education distinguishes our tool from existing solutions. We discuss how bilingual support broadened engagement and how personalized learning paths reinforced retention of security best practices. Our findings demonstrate that a mobile-based, user-centric approach can significantly bolster cybersecurity awareness and incident response in academic environments. Future work will integrate machine learning for enhanced threat detection and voice-guided features for accessibility, aiming to continuously adapt to evolving attack strategies. This research provides insights for policymakers on incorporating such tools into broader institutional cybersecurity strategies.
Evaluating the Efficacy of AI Tools in Systematic Literature Reviews: A Comprehensive Analysis Mogoale, Phumzile Dorcus; Pretorius, Agnieta Beatrijs; Mogase, Refilwe Constance; Segooa, Mmatshuene Anna
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1035

Abstract

Artificial Intelligence (AI) tools can revolutionize literature review practices by transforming the research landscape towards more efficient and reliable review processes. While conducting literature can be challenging and time-consuming, there is a plethora of AI powered tools which uncover potential solutions to the challenge. AI tools may reduce the time spent on repetitive tasks, allowing scholars to focus more on critical analysis and interpretation. Due to the rising abundance of AI tools, it is difficult to decide which AI tools are best for individual research problems or projects. While concerns exist around the ethical and quality consequences of using AI. The study aims to explore the usage of AI tools on the systematics literature review process, specifically focusing on their effectiveness in various stages and ethical concerns. IEEE and MDPI Journal papers from 2020 to 2024 were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RobotReviewer, Covidence and EPPI-Reviewer are AI tools commonly used. These AI tools are designed to support different aspects of the systematic literature review process by offering capabilities such as problem formulation, literature search, inclusion screening and quality assessment. AI tools demonstrate improved effectiveness of literature searches, screening processes and data extraction. Language and content presentation, incorrect citation and plagiarism, grammar and spelling errors may be ren when utilizing AI. Concerns related to data quality, biases, and the need for human oversight were identified.

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