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JUSIFO : Jurnal Sistem Informasi
Published by Universitas Islam Negeri Raden Fatah Palembang
ISSN : 2460092X     EISSN : 26231662     DOI : -
Core Subject :
JUSIFO (Jurnal Sistem Informasi) is an Information System Journal that published by Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang. JUSIFO (Jurnal Sistem Informasi) publishes numerous research articles concerning the articles that integrate technological disciplines with Information System Audit, Software Engineering, Decision Support System, Artificial Intelligence, Application of Information Technology, Social Informatics. JUSIFO (Jurnal Sistem Informasi) is published twice a year; in June and December. In this journal, reviewers will review any submitted paper. Review process employs a double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa. We hope that the articles published by JUSIFO (Jurnal Sistem Informasi) can make a real contribution and have a widely impact.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 153 Documents
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Does Economic Motivation Determine the Continuance of Social Media Use? A Technology Continuance Theory Perspective: A Case of Facebook Fidelia Serli Korey; Dedi Iskandar Inan; Ratna Juita; Muhamad Indra
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.27438

Abstract

This study investigates whether economic motivation—specifically through the Facebook Reels monetization feature “Stars”—significantly influences users’ continued engagement with the platform. Drawing on Technology Continuance Theory (TCT), this research integrates financial and social drivers including remuneration motive, recognition, and reciprocal benefits to examine their effects on attitude, continuance usage intention, and word-of-mouth (WoM) behavior. Data were collected from 174 active Facebook Reels users in Manokwari, Indonesia, and analyzed using partial least squares structural equation modeling (PLS-SEM). Results reveal that network exposure significantly enhances remuneration motive, recognition, and reciprocal benefits. However, only reciprocal benefits positively influence user attitude, which in turn predicts both continued usage and WoM intention. The model’s relatively low R-square values suggest that future research should consider broader psychological and platform-related variables. These findings underscore the need for a holistic approach in designing monetization systems that balance economic rewards with social engagement to ensure sustainable user retention.
A Content-Based Thesis Supervisor Recommendation System Based on Research Interest Clustering and Cosine Similarity Alfina Damayanti; Fenny Purwani; Muhamad Kadafi
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.27605

Abstract

The assignment of thesis supervisors is a critical academic decision that directly affects research quality and completion outcomes. However, supervisor selection in many higher education institutions remains reliant on subjective judgment and manual inspection of lecturers’ research profiles. This study proposes a content-based thesis supervisor recommendation system that integrates research interest clustering and cosine similarity to support more objective and transparent supervisor assignment. Lecturers’ research interests are derived from publication titles and abstracts collected from Google Scholar and represented using TF–IDF weighting. K-means clustering is applied to model dominant research interest themes, while cosine similarity is used to match students’ thesis proposal texts with clustered publication data. The proposed approach was implemented as a web-based decision-support system and evaluated using publication data from 21 lecturers comprising 469 records. The results indicate that research interest clustering provides a structured and interpretable representation of academic expertise, enabling contextually relevant supervisor recommendations. The system demonstrates practical value by enhancing transparency, consistency, and efficiency in academic decision-making. This study contributes to applied research on academic recommendation systems by extending publication-based approaches through cluster-level modeling of research interests.
Forecasting and Raw Material Planning in Traditional Songkok Production Using ARIMA and Simple Exponential Smoothing Sayyidah Nafisah; Abdul Rezha Efrat Najaf; Prasasti Karunia Farista Ananto
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.27833

Abstract

This study investigates the applicability of time series forecasting models—Autoregressive Integrated Moving Average (ARIMA) and Simple Exponential Smoothing (SES)—for optimizing raw material planning in traditional songkok production. Utilizing monthly production data from a small-scale manufacturer in East Java, Indonesia (July 2020–August 2024), the ARIMA(1,1,1) model demonstrated superior forecasting performance, particularly under weak and irregular seasonality. Compared to SES, ARIMA yielded lower MAE, MSE, and MAPE values, enabling more precise production planning. The forecasts were translated into raw material requirements, resulting in improved inventory precision and operational efficiency, with monthly material usage gains ranging from 2.05% to 2.18%. These improvements are especially critical for micro-enterprises constrained by limited resources and seasonally driven demand cycles. While the univariate approach is a limitation, the findings provide a foundation for integrating contextual data in future multivariate models. The study offers practical insights for digital transformation in artisanal sectors and contributes to the broader discourse on data-driven production planning in culturally embedded industries.
Sentiment Analysis on WeTV Application Reviews Using Naïve Bayes: A Study of Preprocessing, Balancing, and Model Performance Wilis Brawijaya; Khothibul Umam; Siti Nur'aini; Maya Rini Handayani
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.27925

Abstract

This study investigates the application of the Naïve Bayes classification algorithm for sentiment analysis of user-generated reviews on the WeTV application available on the Google Play Store. A structured methodology was employed, consisting of data scraping, sentiment labeling based on heuristics, multi-stage preprocessing, class balancing using Synthetic Minority Over-sampling Technique (SMOTE), and performance evaluation through standard metrics. Prior to balancing, the model exhibited strong performance on the dominant class but underperformed on the minority class. The introduction of SMOTE led to improved F1-scores, particularly for positive sentiment, increasing from 61% to 64%, while maintaining overall accuracy around 71%. These findings confirm that Naïve Bayes, when supported by effective preprocessing and data balancing, can deliver robust and interpretable classification results in text mining tasks. This research contributes to the growing literature on machine learning for opinion mining and provides practical implications for developers aiming to extract structured insights from large-scale user reviews.
Support Vector Machine for Classifying Heart Failure, Hypertension, and Normal Heart Condition Surya Amando Bangun; Elvis Sastra Ompusunggu; Wilson Wilson; Eppi Kriawati Harefa
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.28113

Abstract

Cardiovascular diseases, particularly heart failure and hypertension, remain among the leading causes of global mortality, underscoring the urgent need for accurate early diagnosis. This study proposes a classification model based on the Support Vector Machine (SVM) algorithm to distinguish among heart failure, hypertension, and normal heart conditions using real-world clinical data. The dataset was preprocessed through normalization and nominal-to-numerical conversion and validated by medical experts to ensure data quality. K-Fold Cross Validation (K=10) was employed to ensure model robustness and mitigate overfitting. The SVM classifier utilized a linear kernel and achieved high performance in terms of accuracy, precision, and recall. The results demonstrate the effectiveness of the proposed model in classifying multiple cardiovascular conditions with clinically relevant input features. This research contributes to the advancement of intelligent diagnostic tools and supports the integration of machine learning into clinical decision-making processes.
Visual Attention Segmentation of Genshin Impact Characters: An Eye-Tracking and Hierarchical Clustering Analysis of First-Time Players Farhan Atriza Siregar; Rico Maykel Erawanto; Ranvika Adityansah; Randy Alexandros Purba; Dennis Jusuf Ziegel; Evta Indra
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.28487

Abstract

This study investigates the visual attention patterns of first-time players toward character designs in anime-style role-playing games, using Genshin Impact as the research context. An eye-tracking experiment was conducted with 60 participants to capture gaze behavior during exposure to four-character stimuli. The analysis focused on heatmaps, dwell time, and first fixation points, consistently revealing a dominant focus on the character’s body region, regardless of character type or visual variation. Hierarchical clustering further segmented participants into three distinct gaze profiles: lateral scanning, peripheral attention to symbolic elements, and centralized body-centric focus. These findings underscore the importance of adaptive character design strategies that prioritize the body region for conveying emotional and narrative cues while enhancing peripheral elements to improve engagement. The study contributes to the fields of game user experience and visual attention research by integrating eye-tracking data with clustering techniques, offering actionable insights for game developers and interface designers.
Does Gender and Faculty Background Determine the Sustainability of GenAI Adoption in Higher Education? A Revised UTAUT Perspective Mizhael Parubak; Ratna Juita; Dedi Iskandar Inan; Muhamad Indra; Nanes Fitri Rahmawati
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.30354

Abstract

The rapid integration of generative artificial intelligence (GenAI) in higher education has transformed learning practices, yet the sustainability of its adoption remains uneven across student groups. This study examines the determinants of sustained GenAI adoption in university settings, with particular attention to the roles of gender and faculty background. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the study employs a quantitative approach using survey data collected from 184 university students. Partial least squares structural equation modelling (PLS-SEM) is applied to evaluate the proposed relationships. The results indicate that performance expectancy, facilitating conditions, attitude toward use, and behavioural intention significantly influence sustained ChatGPT usage. In contrast, effort expectancy and social influence show limited direct effects. Multi-group analysis further reveals notable differences across gender and faculty background, with female students and those from exact science faculties demonstrating higher levels of sustained GenAI adoption. These findings extend the applicability of UTAUT to GenAI contexts and highlight the importance of demographic and disciplinary factors in designing inclusive and sustainable GenAI adoption strategies in higher education.
Development of SEKAPAI: An AI-Based Scaffolding Platform for Programming Education Rizki Hikmawan; Dedi Rohendi; Jaka Septiadi; Muhamad Akda Fathul Barri
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.30467

Abstract

The rapid adoption of generative artificial intelligence in programming education has raised concerns regarding student over-dependence and the erosion of computational thinking skills. This study presents the design and internal validation of SEKAPAI, an AI-based scaffolding platform developed to support computational thinking while promoting responsible use of generative AI. Using an Agile-oriented Research and Development approach, SEKAPAI integrates three adaptive scaffolding modules—Solution Assessment, Code Assessment, and Free Interaction—to deliver context-aware feedback without providing direct solutions. System requirements were derived through stakeholder analysis and translated into a modular, web-based architecture supported by GPT-based services. Internal validation was conducted using comprehensive black-box testing to evaluate functional correctness, feedback behavior, and alignment with computational thinking components. The results indicate that SEKAPAI operates reliably across core system features and consistently implements progressive scaffolding strategies that regulate AI assistance. This study demonstrates how pedagogical scaffolding principles can be operationalized within AI-assisted learning systems and provides a technically feasible reference model for responsible AI integration in programming education.
Clustering-Based Identification of Student Support Needs in Higher Education Transition Mochamad Welly Rosadi; Nenden Siti Fatonah; Gerry Firmansyah; Habibullah Akbar
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31031

Abstract

The transition from secondary to higher education represents a critical phase influenced by both academic readiness and socio-economic conditions. This study proposes a clustering-based approach to identify student support needs during this transition by analyzing multidimensional student profiles. Using secondary data from 1,226 senior high school students, three unsupervised clustering algorithms—K-Means, DBSCAN, and BIRCH—were applied to academic performance and socio-economic variables. Cluster quality was assessed using internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. The results indicate that clustering-based methods provide richer insights than traditional rule-based approaches by capturing heterogeneous student profiles and revealing atypical cases. Among the evaluated algorithms, BIRCH demonstrated the most balanced performance in terms of cluster compactness and separation, while K-Means offered stable and interpretable results, and DBSCAN was effective in identifying outliers. Interpreted within the college readiness framework, the identified clusters highlight differentiated student support needs, enabling more targeted and equitable intervention strategies. These findings underscore the potential of educational data mining to support data-driven decision-making in facilitating students’ transition to higher education.
Artificial Intelligence for Precision Livestock Farming: A Systematic Review of Applications, Models, and Evaluation Metrics Widyatasya Agustika Nurtrisha; Luthfi Ramadani; Riska Yanu Fa’rifah; Faqih Hamami; Nur Ichsan Utama
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31179

Abstract

The increasing demand for animal-based food products has intensified the need for efficient, data-driven livestock management practices. Artificial Intelligence (AI) has emerged as a key enabling technology within Precision Livestock Farming (PLF), supporting automated monitoring, prediction, and decision-making processes. This study presents a Systematic Literature Review (SLR) of AI applications in livestock farming, focusing on application domains, AI models, and evaluation metrics. Following the PRISMA 2020 guidelines, relevant studies published between 2013 and 2024 were systematically identified, screened, and assessed across major scholarly databases, resulting in 20 eligible articles for qualitative synthesis. The findings indicate that AI is primarily applied to animal identification, body weight estimation, disease detection, behavior analysis, and feed management. Deep learning models, particularly Convolutional Neural Networks, dominate image-based tasks, while traditional machine learning approaches remain effective for structured sensor and tabular data. Common evaluation metrics include accuracy, precision, recall, R², and Mean Absolute Error. Despite promising results, the review reveals substantial heterogeneity in datasets, evaluation protocols, and livestock sector coverage, which limits cross-study comparability. This review highlights methodological trends, identifies key research gaps, and provides insights to guide future AI-driven PLF research and implementation.

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