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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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Integrating Human-Centered AI into the Technology Acceptance Model: Understanding AI-Chatbot Adoption in Higher Education Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1316

Abstract

Artificial intelligence (AI) is transforming education by enhancing assessments, personalizing learning, and improving administrative efficiency. However, the adoption of AI-powered chatbots in higher education remains limited, primarily due to concerns about trust, transparency, explainability, perceived control, and alignment with human values. While the Technology Acceptance Model (TAM) is commonly used to explain technology adoption, it does not fully address the challenges posed by AI systems, which require human-centered safeguards. To address this gap, this study extends TAM by incorporating Human-Centered AI (HCAI) principles—explainability, transparency, trust, and perceived control—resulting in the HCAI-TAM framework. An empirical study with 300 respondents was conducted using a structured English questionnaire, and regression analysis was applied to assess the relationships among variables. The model explained 65% (R² = 0.65) of the variance in behavioral intention and 55% (R² = 0.55) in usage behavior. The findings highlight that integrating HCAI principles into TAM enhances user adoption of AI chatbots in higher education, contributing both theoretically and practically.
IoT-Based Smart Fertigation System for Citrus Plants using Fuzzy Logic Control Ali, Ircham; Adrinoviarini, Adrinoviarini; Banaty, Oka Ardiana; Kamil, Mohammad Insan
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1318

Abstract

This study develops and evaluates an IoT-based smart fertigation system for citrus plants using a fuzzy logic control (FLC) algorithm integrated with an ESP32 microcontroller and wireless sensors. The research aims to address the limitations of conventional fertigation practices that rely on fixed schedules without considering real-time soil and climate conditions, which often result in water inefficiency and nutrient imbalance. The developed system integrates sensors for soil moisture, temperature, and air humidity to automatically regulate irrigation duration through triangular membership functions and a fuzzy inference model consisting of 64 fuzzy rule combinations. Over a 30-day observation period, twelve citrus seedlings aged 3-4 months after grafting were organized into four experimental groups: manual fertigation (MF), manual irrigation (MI), smart fertigation (SF), and smart irrigation (SI). Experimental findings indicated that the smart fertigation system-maintained soil moisture within a more stable range of 40-55%, and plants in the SF group experienced approximately 7 cm greater height and a twofold increase in leaf count compared to manually irrigated ones. The smart fertigation treatment also produced more uniform, greener, and healthier foliage, signifying balanced nutrient uptake. Overall, the IoT–FLC integration provides a more adaptive and eco-efficient irrigation model that promotes sustainable management of water and nutrients in tropical citrus cultivation.
Trends of Machine Learning, Cybersecurity and Big Data Analytics in Industry 4.0 Sarker, Md. Mostakim; Jony, Md. Jahid Hasan; Ullah, Md Wali; Begum, Jannat; Naushin, Nusaibah
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1321

Abstract

This research explores the integration of Machine Learning (ML), Cybersecurity, and Big Data Analytics (BDA) in advancing intelligent, secure, and sustainable industrial ecosystems within Industry 4.0. It assesses global research productivity, collaboration patterns, and the connection between intelligent automation, data-driven innovation, and cyber resilience. A PRISMA-based bibliometric review of 1,386 relevant publications from the Scopus database (2020-2025) was conducted, using Biblioshiny visualization tools to map key authors, institutions, countries, and emerging research clusters. Findings show a 7.09% annual growth in publications, reflecting a growing global focus on ML, BDA, and cybersecurity within Industry 4.0 ecosystems. The United States, China, and India were identified as major contributors, with strong cross-continental collaborations fostering innovation. Key research topics include deep learning, digital twins, and the Internet of Things (IoT), while emerging areas such as explainable AI, federated analytics, and edge computing are gaining attention. By mapping global research dynamics and identifying key contributors, this study highlights critical research gaps and offers practical insights for advancing interdisciplinary innovation, aimed at creating secure, intelligent, and sustainable industrial ecosystems in Industry 4.0.
Child Nutrition Prediction for Stunting Prevention Using K-Nearest Neighbor (K-NN) Algorithm Kirana, Chandra; Wahyuningsih, Delpiah; Michael, Louis
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1322

Abstract

Stunting is a significant public health issue in Indonesia, affecting both children's physical growth and cognitive development. This study aims to develop a child nutritional status prediction application using the K-Nearest Neighbor (K-NN) algorithm as an early detection tool for stunting prevention. The model classifies nutritional status into five categories: good nutrition, poor nutrition, undernutrition, overnutrition, and obesity, using anthropometric data such as age, weight, height, and gender. The dataset comprises 49,766 samples of children aged 0–5 years from the Bangka Belitung Islands Provincial Health Office. The data processing included normalization, feature selection, and k-value testing to optimize model performance. Evaluation results showed that K-NN with k = 2 achieved 92% accuracy, with the best precision and recall in the good nutrition category (0.94 and 0.99). However, performance in minority categories like malnutrition remains low due to data imbalance. The weighted averages for precision, recall, and F1-score were 0.90, 0.92, and 0.90, respectively. This research's novelty lies in integrating the K-NN model into a mobile application, enabling real-time nutritional status assessment for health workers, improving fieldwork efficiency, and facilitating early detection and monitoring.
Agile Methodologies as Drivers of Organizational Culture in Digital Transformation Projects Murudi, Tendamudzimu; Khoza, Thulani Lucas
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1323

Abstract

Organisations are increasingly adopting agile methodologies to accelerate digital transformation, yet outcomes remain inconsistent when agile is viewed solely as a delivery technique rather than a cultural mechanism. This study explores how agile practices influence organisational culture during transformation and identifies the conditions under which benefits are realised. Using an explanatory mixed-methods design, the study combined a survey (N = 315) with 18 semi-structured interviews. After ensuring scale reliability, bivariate correlations and linear/multiple regressions were conducted in IBM SPSS, while interview transcripts were thematically coded to explain the quantitative findings. Agile practices were positively linked to a supportive culture (Pearson r ≈ .32), with simple regression indicating that agile significantly predicted culture (R² ≈ 0.10). A multiple regression predicting digital-transformation outcomes from agile and culture revealed a significant model (R² ≈ 0.40), where agile was the stronger predictor and culture made a smaller yet meaningful contribution. Qualitative insights highlighted how cadence, visibility, and iteration normalised collaboration, transparency, and rapid feedback. Practically, managers should focus on culture outcomes in agile roadmaps, institutionalise essential routines, and reduce structural hand-offs for sustained transformation.
Utilizing ROP in a Mobile Based Warehouse Management System for Small Retail Businesses Setiawati, Winni; Beng, Jap Tji; Tony, Tony
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1326

Abstract

ABC Store, a wholesale sock retailer, operates through both physical and online platforms. With increasing online sales, the company has experienced frequent stockouts due to inefficient manual inventory processes. This study aims to address these challenges by developing a mobile-based Warehouse Management System (WMS) integrated with a Reorder Point (ROP) calculation. The system was developed using the V-Shaped model, which involved requirement gathering through interviews and warehouse observations. The application was built using the lightweight Flutter framework and an SQLite local database, with testing including white-box, black-box, and User Acceptance Testing (UAT). Additionally, QR code scanning was implemented to improve document tracking and inventory management. The results indicate that the system functions as expected, with the ROP calculation effectively supporting restocking decisions and minimizing stockouts. This study contributes to the field by demonstrating the practical application of integrating simplified ROP calculation into a mobile-based WMS, highlighting its potential to improve warehouse operations for small businesses with limited infrastructure. The approach offers a scalable solution for managing inventory efficiently and cost-effectively, especially in small-scale retail settings.
The IoT-Based E-Voting System Using Fingerprint Biometrics for School Elections Wibowo, Wahyu Andre; Ujianto, Erik Iman Heri
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1328

Abstract

This study proposes an Internet of Things (IoT)-based e-voting system to address the limitations of traditional paper-based student council elections, which are prone to errors, inefficiency, and data manipulation. The system is developed using the ADDIE model for Research and Development (R&D), incorporating a Laravel-based administrative dashboard, a Flutter-based mobile voting interface, and a biometric authentication device built with an ESP32 microcontroller and JM-101B fingerprint sensor. Evaluation involved 20 participants who completed six functional test scenarios, achieving a 100% success rate across 120 instances. Usability testing revealed a notable comfort difference, with 30% comfort on mobile phones and 90% on tablets. Performance testing showed a fingerprint scan time of 669.6 ms and a vote submission latency of 437.1 ms, indicating good system responsiveness. The results suggest the system improves security, transparency, and efficiency in the election process. However, the study is limited by a small sample size and evaluation within a single institution. Future work could explore cloud integration, multi-school deployment, and additional authentication methods to enhance scalability and support broader adoption.
Web Information System for E-Sport Arena Community with OWASP-Based Cybersecurity Using XP Method Aufan, Afgha; Purwadi, Purwadi; Pritama, Argiyan Dwi
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1344

Abstract

The rapid development of the e-sports ecosystem in Purwokerto has encouraged the emergence of digital communities such as the Esport Arena Community. However, the management of member data, event information, and merchandise transactions is still carried out manually through social media, resulting in inefficiency and limited-service reliability. This study aims to develop a web-based information system that integrates member management, jersey evolution documentation, and secure online merchandise transactions. The system was developed using the Extreme Programming (XP) method, which supports iterative development and continuous refinement. Security measures were implemented based on OWASP Top 10 recommendations, including prepared statements, input validation, CSRF protection, and Role-Based Access Control (RBAC). System evaluation using the System Usability Scale (SUS) produced a score of 88, categorized as Excellent, indicating high user satisfaction and strong usability performance. The results demonstrate that the system operates securely, reliably, and effectively improves operational efficiency for the Esport Arena Community.
Stock Price Prediction Using Backpropagation ANN: Case Study of ADMR (2023–2025) Khozin, Muhammad; Abidin, Zainal; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1347

Abstract

This study develops an Artificial Neural Network (ANN) backpropagation model for predicting stock prices using ADMR stock data from 2023 to 2025, obtained from Yahoo Finance. Given the inherent volatility and unpredictability of stock prices, accurate forecasting plays a crucial role in investment decision-making. ANN models are particularly effective for capturing complex, non-linear relationships and patterns in financial data, which traditional statistical models may fail to address. In this research, various configurations were tested by adjusting the number of hidden neurons (5, 10, and 15) and learning rates (0.1, 0.3, and 0.5). The optimal model architecture was found to be 3-10-1, consisting of three input neurons, ten hidden neurons, and one output neuron, which achieved the best prediction performance with a Mean Absolute Percentage Error (MAPE) of 2.26%. This model was trained with a learning rate of 0.3 and completed in 915 iterations. However, the model's predictive capabilities are constrained by its reliance on historical stock prices alone, excluding external factors such as macroeconomic indicators, market sentiment, or trading volume, which may improve its generalization and overall accuracy. Future work could integrate these variables for better robustness and predictive power.
Utilizing Random Forest Method for Predicting Student Dropout Risk in Madrasah Environments Mahsun, Muhammad; Hariyadi, M. Amin; Harini, Sri
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1364

Abstract

The phenomenon of school dropout represents a crucial issue with negative impacts on educational institution performance, social stability, and national development. Consequently, the early detection of high-risk students constitutes a strategic preventive measure. This research aims to develop an accurate predictive model using a Machine Learning approach. The study employed a comparative evaluation using classification algorithms, with the primary focus being the performance analysis of the Random Forest Classifier. The dataset utilized, comprising 1,763 student records, underwent a rigorous data pre-processing phase, including data cleaning, variable transformation, and class imbalance handling, to ensure high-quality input. The model was trained using a Random Seed configuration of 75 to guarantee experimental reproducibility and consistency in evaluation results. Experimental findings indicate that the Random Forest algorithm provided the best performance, achieving an accuracy of 82.0% and a precision of 83.8%. This superior performance confirms the model's effectiveness in identifying the key determinants of dropout, stemming from both students' internal and external factors. Based on these results, the research recommends the application of Random Forest as a Decision Support System instrument to facilitate targeted interventions, including medical support, economic assistance, and academic counseling. Future research is advised to integrate historical counseling data to further enhance the prediction sensitivity of the model.

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