cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Usman Ependi
Contact Email
usmanependi@adsii.or.id
Phone
081271103018
Journal Mail Official
usmanependi@adsii.or.id
Editorial Address
Jl AMD, Lr. Tanjung Harapan, Taman Kavling Mandiri Sejahtera B11, Kel. Talang Jambe, Kec. Sukarami, Palembang, Provinsi Sumatera Selatan, 30151
Location
Unknown,
Unknown
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
 53   54   55   56   57 
Contextual ITSM Adoption Across Educational Levels: A University and a Secondary School in Jakarta Marcel, Marcel; Azhar, Nur Chalik
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1081

Abstract

This research investigates contextual ITSM adaptation across educational levels through a comparative case study of a university and secondary school in Jakarta. Using qualitative methodology with interviews, observations, and document analysis, we examined implementation patterns at University X (5,000 students, 12 IT staff) and SMA Y (450 students, 2 IT staff). Results show universities achieved semi-formal ITSM maturity levels 2-3 while secondary schools operated at pragmatic levels 1-2, reflecting resource disparities where universities allocated 4% versus 1.5% of operational budgets to IT services. Key findings reveal persistent perception gaps where academic staff predominantly view IT as "repair function" rather than strategic service, with most service requests still submitted through informal channels instead of standardized procedures. Three primary implementation challenges emerged: resource limitations, structural complexity, and cultural resistance. Based on these findings, we propose a phased transformation model (Stabilization, Standardization, Development) accommodating "layered maturity" - allowing institutions to operate at different maturity levels across ITSM domains rather than uniform advancement. This research contributes a contextual ITSM implementation framework bridging industry standards with educational realities, providing practical guidance for institutions balancing digital transformation aspirations with resource constraints, particularly relevant for developing countries facing similar educational technology challenges.
Abstractive Text Summarization to Generate Indonesian News Highlight Using Transformers Model Putri, I Gusti Agung Intan Utami; Trisna, I Nyoman Prayana; Rusjayanthi, Ni Kadek Dwi
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1082

Abstract

The increasing volume of information has led to the phenomenon of information overload, a condition where individuals struggle to filter and comprehend information efficiently within a limited time. To address this issue, automatic text summarization serves as an essential approach. This research aims to assess effectiveness of two transformer-based models, IndoT5 and mBART, by comparing their ability to generate abstractive summaries (highlight) of Indonesian news articles. The abstractive approach allows models to generate new sentences with more natural language structures compared to extractive methods. Fine-tuning for both models was conducted using a dataset comprising 10,410 news articles from Tempo.co, each containing full news content and a corresponding highlight used as a reference. ROUGE and BERT-Score metrics were employed in the evaluation process to assess structural and semantic correspondence between the references and the generated summaries. Results show that IndoT5 outperformed in terms of ROUGE-1 (0.43087), ROUGE-2 (0.29143), ROUGE-L (0.39224), BERT-Score Recall (0.89130), and F1 (0.87708), indicating its capability to generate complete and relevant news highlight. Meanwhile, mBART achieved a higher BERT-Score Precision (0.86717) but tended to generate less informative outputs. The findings of this research are expected to aid in enhancing the coherence and efficiency of abstractive summarization systems.
Agile Digital Transformation in Local Government: An Extreme Programming Approach to Public Service Mall Applications Andini, Dwi Yana Ayu; Rizki, Fahlul; Yulia, Aviv Fitria
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1083

Abstract

The development of the web-based Public Service Mall (MPP) application aims to enhance the quality, efficiency, and accessibility of public services in Pringsewu Regency. Utilizing the Extreme Programming (XP) methodology, which focuses on iterative and collaborative software development, the application follows five main phases: planning, design, coding, testing, and release. Key features of the application include a service search function, a booking code-based queue system, service history tracking, and a user dashboard for seamless interaction. The implementation results demonstrate that the application significantly simplifies access to various public services, reduces physical queues, and improves transparency throughout the service process. System testing confirms that the application operates according to specifications, with a user satisfaction rate of 87% and a notable improvement in service response times. Therefore, this application serves as an effective digital solution that supports the transformation of modern public services, making them more responsive and accessible to the community's needs.
Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation Simarmata, Penni Wintasari; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1087

Abstract

This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. This investigation lends itself to the advancement of a proactive identification framework, which hold potential for incorporation within collegiate mental well-being surveillance infrastructures. Future implementations may consider real-time models and expand data sources through digital counseling systems and behavioral analytics
Analysis of Community Sentiment Towards Free Nutrition Meal Programs on Twitter Using Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Ensemble Methods Ati, Gresensia Rosadelima; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1098

Abstract

Meal program free nutritious food that was planned government reap diverse response from society, especially on social media like Twitter. Research This aiming for analyze sentiment public to the program with utilize text mining and machine learning techniques. Data of 1500 tweets was collected through the scraping process using Python. The sentiment in the tweets is classified into three categories: positive, negative, and neutral. In this study, four classification algorithms were used: Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and ensemble, to compare their performance in sentiment analysis. Additionally, a text weighting method, TF-IDF, was tested to examine its impact on classification accuracy. The analysis results show that the Support Vector Machine (SVM) algorithm, when combined with the TF-IDF weighting method, provides the highest accuracy of 95.05%. Other algorithms also showed varied performance, with Ensemble achieving 86.57%, K-Nearest Neighbors 77.03%, and Naïve Bayes 60.42% accuracy. It is expected from results study This can give description general to perception public about the meal program free nutritious an
Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality saka, Hildegardis Kristina; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1099

Abstract

This research applies sentiment analysis to understand user perceptions of the Access by KAI application, especially specific aspects such as speed, payment process, and user interface (UI/UX). User reviews are collected and processed through preprocessing stages, balancing using the SMOTE method, and classified using three machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, and Logistic Regression. The SVM model achieved the highest accuracy of 89.33%, followed by Logistic Regression at 88%, and Decision Tree at 86.67%. Precision, recall, and F1-scores for each model were also evaluated, showing strong performance in detecting negative sentiments but lower performance for neutral and positive sentiments. In addition, keyword-based analysis revealed that negative sentiment was most commonly found in the aspects of the payment process and speed. WordCloud visualization also strengthens the results by showing the dominance of negative words in user reviews. The results of this study provide important suggestions and input for application developers to improve aspects of the service that are considered less satisfactory by users. Thus, this study can be used as a practical guide in making strategic decisions to improve the quality of service and user satisfaction of the Access by KAI application.
Mitigating Cybersecurity Risks in E-Waste: A Study on Secure Disposal Practices in Tanzania’s Public Institutions Mustapha, Athuman; Mgawe, Bonny S; Mvulla, Jaha; Sam, Anael
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1100

Abstract

The growing volume of electronic waste (e-waste) in Tanzanian public institutions poses serious cybersecurity risks, as discarded devices often contain sensitive data vulnerable to unauthorized access. This study examines these risks across 11 public institutions, involving IT staff, e-waste handlers, policymakers, and environmental officers. It applies Routine Activity Theory, a framework that explains risks as arising when cybercriminals exploit unsecured e-waste due to weak regulations. Through interviews and focus group discussions, the research identifies key vulnerabilities: data leakage from improper sanitization, regulatory gaps, and risks from informal disposal methods like auctions. These findings highlight the need for stronger oversight to prevent data breaches. The study proposes a framework that categorizes devices by risk level and integrates secure sanitization protocols, such as data wiping or destruction. Policymakers and institutions must urgently adopt these protocols to protect sensitive data and promote sustainable e-waste management in Tanzania’s public sector.
Taxicab Entrepreneurs’ Attitude to Continue Using e-Hailing Platforms in South Africa Chipangura, Baldreck
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1102

Abstract

Taxicab entrepreneurs who operate on e-hailing platforms in South Africa face challenges such as earning below minimum wage, lacking employment benefits, working long hours, and experiencing victimisation by traditional taxicab operators. The key question is why these entrepreneurs continue using e-hailing platforms despite unfavourable working conditions. This study proposed that technology adoption factors enable entrepreneurs to overcome challenges and encourage them to keep using e-hailing platforms. Based on this assumption, this study investigated the determinants of technology adoption that influence the attitude of taxicab entrepreneurs to continue using e-hailing platforms in South Africa. The researchers gathered quantitative data from 253 entrepreneurs in Johannesburg, South Africa and tested the hypotheses with multiple regression analysis. The results demonstrated that perceived usefulness, benefits, and security strongly influenced entrepreneurs' willingness to continue operating on e-hailing platforms. However, perceptions of convenience, trust, and perceived ease of use did not affect their decision to use e-hailing services. Theoretically, this study pinpointed the factors that drive and hinder the continued use of e-hailing applications. Practically, the results provide insights into understanding long-term usage, user satisfaction, and the success of e-hailing in developing countries undergoing digital transformation, such as South Africa.
A Systematic Literature Review on Machine Learning Algorithms for the Detection of Social Media Fake News in Africa Chukwuere, Joshua Ebere; Montshiwa, Tlhalitshi Volition
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1103

Abstract

Fake news has been around in history before social media emerged. Social media platforms enable the creation, processing, and sharing of various kinds of content and information on the Internet. While the mediums of information and content shared across social media platforms are hard for users to authenticate, if users are tracking fake information or fake content, it can harm individuals, society, or the world. Fake news is increasingly becoming a worrisome issue, especially in Africa, because it's difficult to identify and stop the distribution of fake news. Due to languages and diversity, it is difficult for humans to understand and subsequently identify fake news on social media platforms, so high-level technological strategies, such as machine learning (ML), would be able to tell if the content is false material. As such, this study sought to identify effective ML classifiers to detect fake news on social media platforms, and the systematic literature review followed the PRISMA standard. The study identified 14 effective ML classifiers to manage fake news on social media platforms, including Random Forest, Naive Bayes, and others. Four research questions guided the study focused on the effectiveness of the classifiers, their applicability for detecting different forms of false news, the features of the dataset size and features, and the metrics that were created to assess the metrics. A conceptual framework known as the Information Behavioral Driven Social Cognitive Model (IBDSCM) was proposed in a bid to affect the fake news detection on social media platforms. Overall, this study establishes a contribution to understanding the ML algorithms for detecting false news in Africa and allows for a conceptual base for future studies.
Technology Acceptance Model TAM using Partial Least Squares Structural Equation Modeling PLS- SEM Latif, Imam Sofarudin; Saputro, Rujianto Eko; Barkah, Azhari Shouni
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1104

Abstract

The rapid advancement of digital technologies necessitates a deeper focus on user acceptance and satisfaction, particularly within the framework of the Technology Acceptance Model (TAM), analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This systematic literature review examines 36 articles published between 2020 and 2025, revealing that factors such as trust, system quality, perceived enjoyment, service quality, and technological self-efficacy significantly influence user satisfaction. These external variables enhance the explanatory power of TAM, providing a richer understanding of user interactions with digital platforms such as e-commerce, e-learning, and mobile banking. PLS-SEM's ability to manage model complexity, non-normal data distributions, and interrelated constructs further validates its suitability for this research. The findings suggest that integrating these external factors improves both the theoretical and practical aspects of TAM in the context of technology adoption. Future research could explore additional industry-specific applications for emerging technologies.

Page 55 of 66 | Total Record : 653

 53   54   55   56   57 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol 7 No 4 (2025): December Vol 7 No 3 (2025): September Vol 7 No 2 (2025): June Vol 7 No 1 (2025): March Vol 6 No 4 (2024): December Vol 6 No 3 (2024): September Vol 6 No 2 (2024): June Vol 6 No 1 (2024): March Vol 5 No 4 (2023): Journal of Information Systems and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics Vol 5 No 1 (2023): Journal of Information Systems and Informatics Vol 4 No 4 (2022): Journal of Information Systems and Informatics Vol 4 No 3 (2022): Journal of Information Systems and Informatics Vol 4 No 2 (2022): Journal of Information Systems and Informatics Vol 4 No 1 (2022): Journal of Information Systems and Informatics Vol 3 No 4 (2021): Journal of Information Systems and Informatics Vol 3 No 3 (2021): Journal of Information Systems and Informatics Vol 3 No 2 (2021): Journal of Information Systems and Informatics Vol 3 No 1 (2021): Journal of Information Systems and Informatics Vol 2 No 2 (2020): Journal of Information Systems and Informatics Vol 2 No 1 (2020): Journal of Information Systems and Informatics Vol 1 No 2 (2019): Journal of Information Systems and Informatics Vol 1 No 1 (2019): Journal of Information Systems and Informatics More Issue