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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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Developing a Cloud-Native Internship Management Platform: Enhanced Efficiency and Integration through Object-Oriented Architecture Setiaji, Pratomo; Adiyono, Soni; Prayogo, Adhie; Kurniasari, Nita
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.v7i1.1036

Abstract

The Internship Program Fieldwork Practice plays a crucial role in bridging academic learning and industry experience. However, traditional internship management faces challenges such as inefficient supervision, inconsistent attendance tracking, and lack of standardized performance evaluations. This study proposes a cloud-native internship management platform utilizing Object-Oriented Design (OOD) to enhance efficiency and accuracy in administrative processes. Developed using the Rapid Application Development (RAD) methodology, the system provides real-time monitoring, automated attendance tracking, and centralized performance assessment. User acceptance testing involving 35 participants, including students, lecturers, and industry supervisors, revealed significant improvements in administrative efficiency, student engagement, and data security. The platform ensures scalability, role-based access control, and secure data encryption. Findings highlight the need for standardized, technology-driven internship management solutions. Future research should explore AI-driven analytics and machine learning for optimizing internship experiences.
Clustering Library Loan Books Using K-Means Clustering Tanjung, Mawar Indah; 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.v7i1.1037

Abstract

Optimal library collection management requires an understanding of book borrowing patterns to align availability with user needs. Without proper analysis, less popular books may remain in large quantities, while popular books may experience shortages. This study employs the K-Means Clustering method to group borrowed books at the Saintek UINSU Medan Library. The dataset consists of 290 loan records with attributes including book type, borrowing frequency, and the number of individuals borrowing each book. The data was converted into a numerical format and normalized using Min-Max Scaler. The Elbow Method was applied to determine the optimal number of clusters, which was found to be two. This study aims to classify books based on borrowing patterns to provide insights into library collection management. The clustering results can assist in decision-making regarding book procurement and distribution. Cluster C0 consists of popular books with high borrowing frequency and a large number of borrowers, while Cluster C1 includes books with lower borrowing rates. These findings offer a deeper understanding of borrowing trends, aiding libraries in developing acquisition strategies and organizing collections more effectively to meet user needs. These findings provide valuable insights for strategic decision-making in library collection development and maintenance, ensuring that popular books are adequately stocked while minimizing the accumulation of less-demanded titles.
Sentiment Analysis of 'Free Lunch for Children' Policy on Social Media X Using Random Forest Algorithm Anies, Anies; Ikhsan, Muhammad
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.v7i1.1039

Abstract

The concept of a welfare state emphasizes the main role of the government in providing protection and improving welfare such as health and education to its people. The free lunch program proposed by Prabowo Subianto and Gibran Rakabuming Raka aims to improve the nutritional quality of school children while driving the national economy. The public's reaction to Prabowo Subianto's work program plan, on free school lunch program and nutritional support for Indonesian students, is very diverse in perspective on X. The Random Forest algorithm proved to be quite effective in classifying public sentiment regarding the policy of “Free Lunch for Children.” With an overall accuracy of 73%, the model was able to categorize public opinion into positive, negative, and neutral categories. To improve the performance of the model, upsampling was performed to balance the classes in the dataset as well as hyperparameter tuning. After applying these techniques, the accuracy of the model increased significantly to 80%.
Harnessing SVM for Sentiment Analysis: Insights from Gojek's Instagram Engagement Savero, Muhammad Juan; Ibrahim, Ali; Utama, Yadi; Lestari, Endang
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.v7i1.1041

Abstract

The development of digital technology has changed the transportation industry, including online services such as Gojek. Understanding customer sentiment is key in improving user experience and designing more effective business strategies. This research analyzes Gojek user sentiment on Instagram using Support Vector Machine (SVM). Data is obtained through web scraping, then processed through text cleaning, tokenization, common word removal, and stemming. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) before being classified with SVM. The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. Most positive comments highlight the convenience and efficiency of the service, while negative comments are more related to high tariffs, application constraints, and less responsive customer service. These findings provide insights for Gojek to improve marketing strategies, optimize customer service, and adjust fare policies based on user feedback. In addition, this analysis can help in predicting real-time customer satisfaction trends through sentiment monitoring on social media. As a development step, this research recommends further exploration with deep learning and Aspect-Based Sentiment Analysis (ABSA) to improve accuracy and understand the service aspects that have the most influence on customer satisfaction.
Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients Nanda, Dika Kurnia; Rini, Dian Palupi
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.v7i1.1042

Abstract

The Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately.
Deploying a GIS for Enhancing Clinic Accessibility in Indonesia: An Agile QGIS Approach Prananingrum, Lely; Sari, Ilmiyati; Harahap, Robby Kurniawan
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.v7i1.1043

Abstract

Health is a fundamental necessity for all living beings, and clinics represent one of the most accessible healthcare facilities for communities. The spatial distribution of clinics can be effectively analysed and visualized through a Geographic Information System (GIS). This study proposes the development of a web-based clinic GIS for Indonesia using Quantum GIS (QGIS) software, implemented through the Agile methodology. The integration of Agile practices ensures that the system is accurate, adaptable, and responsive to evolving user needs. The resulting GIS website was successfully developed and tested, achieving a usability score of 88.76%, with effectiveness, efficiency, and satisfaction ratings of 91%, 90%, and 85%, respectively. The platform aims to support policymakers and healthcare providers in gaining a deeper understanding of health service distribution, ultimately promoting more equitable, data-driven decision-making in healthcare planning and resource allocation.
Enhancing User Satisfaction and Loyalty in MSMEs: The Role of Accounting Information Systems Imtihan, Khairul; Mardi, Mardi; Tantoni, Ahmad; Bagye, Wire; Zulkarnaen, Muhammad Fauzi
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.v7i1.1044

Abstract

This study examines the impact of Accounting Information System (AIS) attributes on User Satisfaction, Decision-Making, and Loyalty among Micro, Small, and Medium Enterprises (MSMEs). The research evaluates how Content, System Quality, Information Quality, and User Characteristics influence satisfaction and decision-making, ultimately shaping user loyalty. A quantitative approach utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze responses from 62 MSME operators in East Lombok, Indonesia. The findings indicate that Content and System Quality significantly enhance User Satisfaction and Decision-Making, which in turn mediate their effects on User Loyalty. Contrary to conventional expectations, Information Quality has a minimal impact, suggesting that MSMEs prioritize system usability and functionality over informational attributes. The study reinforces the critical mediating roles of satisfaction and decision-making, highlighting how system attributes influence behavioral outcomes. In practical terms, AIS should be designed with simplicity, reliability, and relevance to MSME needs, ensuring ease of adoption and operational efficiency. Policymakers are encouraged to promote digital literacy and provide affordable AIS solutions to accelerate adoption among MSMEs. Additionally, the study suggests that future research explore broader cultural and organizational dynamics affecting AIS adoption and employ mixed-method approaches for deeper insights into user behavior.
Advancing Diversity in Recommendation Systems Through Collaborative Filtering: A Focus on Media Content Pardede, Chandro; Togatorop, Parmonangan R.; Panjaitan, Permana Gabriel
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.v7i1.1045

Abstract

A recommendation system provides suggestions based on user preferences, interests, or behavior. However, a major challenge is its tendency to generate monotonous recommendations, reducing diversity and limiting new user experiences. Therefore, increasing diversity is essential to enhance user experience and satisfaction while maintaining recommendation accuracy. This research proposes to apply collaborative filtering method, which focuses on item-based filtering using KNN. This method focuses on item similarity using cosine similarity. To enhance diversity, the system filters results based on similarity and rating thresholds. The evaluation results confirm that applying a similarity threshold increases recommendation diversity, as indicated by consistently higher individual diversity values. Clustering further enhances individual diversity. Findings show that the highest individual diversity with clustering reaches 0.5719, compared to 0.5706 without clustering. These improvements suggest potential applications in domains such as e-commerce and music recommendation systems.
Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning Ferdiansyah, Ferdiansyah; Ependi, Usman; Tasmi, Tasmi; Haikal, Muhammad; Mikko, Mikko
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.v7i1.1047

Abstract

Blockchain technology has revolutionized data security and transaction transparency across various industries. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. This study aims to analyze anomalies in blockchain systems using machine learning approaches. Various anomaly detection techniques, including supervised and unsupervised methods, are evaluated for their effectiveness in identifying irregularities. The results indicate that machine learning models can detect anomalies with high accuracy, providing insights into potential threats and system vulnerabilities. The findings of this research contribute to improving blockchain security and developing more robust monitoring systems.
Social Media Management System for Educational Promotion Singgalen, Yerik Afrianto; Kartikawangi, Dorien; Winayu, Birgitta Narindri Rara
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.v7i1.1052

Abstract

Educational institutions, particularly tourism study programs, face significant challenges in managing fragmented and inefficient social media promotion strategies that hinder student recruitment and weaken institutional visibility. These problems arise from inconsistent content delivery, lack of stakeholder coordination, and limited performance monitoring and analytics capacity. To address these challenges, this research employs the Rapid Application Development (RAD) methodology through four stages: Requirements Planning, User Design, Construction, and Cutover. The requirement planning phase involved gathering aspirations from all stakeholders within the study program to ensure alignment in designing creative and effective promotional content. The resulting system integrates automated content workflows, scheduling algorithms, demographic-based audience targeting, and real-time performance analytics. The findings indicate substantial improvements in resource efficiency, precision of outreach, enrollment conversion rates, and institutional branding consistency. This research provides a comprehensive framework for transforming academic promotional practices through digital system integration, specifically tailored to the operational needs of educational institutions.

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