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INDONESIA
JURNAL SISTEM INFORMASI BISNIS
Published by Universitas Diponegoro
ISSN : 20883587     EISSN : 25022377     DOI : -
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
JSINBIS merupakan jurnal ilmiah dalam bidang Sistem Informasi bisnis fokus pada Business Intelligence. Sistem informasi bisnis didefinisikan sebagai suatu sistem yang mengintegrasikan teknologi informasi, orang dan bisnis. SINBIS membawa fungsi bisnis bersama informasi untuk membangun saluran komunikasi yang efektif dan berguna untuk membuat keputusan yang tepat waktu dan akurat. Business intelligence sebagai dasar pengembangan dan aplikasi SINBIS menjadi kerangka kerja teknologi informasi yang sangat penting untuk membuat agar organisasi dapat mengelola, mengembangkan dan mengkomunikasikan aset dalam bentuk informasi dan pengetahuan. Dengan demikian SINBIS merupakan kerangka dasar dalam pengembangan perekonomian berbasis pengetahuan.
Arjuna Subject : -
Articles 438 Documents
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Early Detection of Patient Surge Anomalies in Hospitals: A Comparative Analysis of Gradient Boosting, Random Forest, and SVM Masparudin Masparudin; Marfuah Marfuah; Abdullah Abdullah
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp488-495

Abstract

Unpredictable fluctuations in patient visits often lead to resource unpreparedness and decreased service quality in hospitals. This study aims to develop an early warning system for patient surges across 110 healthcare service units. Unlike conventional approaches utilizing static thresholds, this study proposes a Statistical Anomaly Detection method based on Z-Score for dynamic labeling and applies Synthetic Minority Over-sampling Technique (SMOTE) to address extreme data imbalance. Three classification algorithms—Gradient Boosting Classifier (GBC), Random Forest (RF), and Support Vector Machine (SVM)—were compared using time-series lag features and volatility trends. Experimental results demonstrate that Gradient Boosting outperformed other methods, achieving the highest F1-Score of 37.35% and a Recall of 48.98%. Although the F1-Score reflects the extreme nature of the data imbalance, achieving high recall is explicitly prioritized in healthcare operations to minimize the critical risk of missed surge events. This study concludes that integrating statistical anomaly-based labeling with ensemble boosting algorithms effectively mitigates noise in heterogeneous hospital visit data, thereby serving as a reliable basis for proactive managerial decision-making.
Digital Transformation of the Village Correspondence Information System (SIPDES) in Supporting Good Village Governance: A Case Study of La’bo’ Village Melki Garonga; Semuel Yacobus Padang
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp496-503

Abstract

This research focuses on the implementation of the web-based Village Correspondence Information System (SIPDES) in La’bo’ Village, North Toraja Regency, as a digital solution to address the limitations of conventional public service delivery. SIPDES was designed to transform the village correspondence process by enabling community members to submit letter requests and monitor their application status independently through mobile devices, thereby reducing the need for repeated physical visits to the village office. The system was developed using the structured Waterfall methodology and underwent comprehensive functional testing. Black Box Testing confirmed that all system features functioned as intended and complied with the specified requirements. Furthermore, the User Acceptance Test (UAT) demonstrated a high level of user approval, indicating that SIPDES is perceived as practical, user-friendly, and suitable for real-world implementation. The implementation of SIPDES has proven effective in improving administrative efficiency, enhancing service transparency, and simplifying access to village administrative services. These outcomes indicate that SIPDES contributes meaningfully to the improvement of public service quality and supports the broader objectives of digital governance and Good Village Governance at the village level.
Development Of A Hot-Fit Framework For Hmis: Empirical Study And Technical Implications At A Regional General Hospital Frendy Rumambi; Didik Dwi Prasetya; Anik Nur Handayani
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp535-543

Abstract

The digitalization of the health industry has allowed hospitals to adopt a Hospital Information System (HIS) for enhancing the efficiency, transparency and quality of services. Even though the application of HIS is regulated nationally, the success in its adoption in Indonesia is considered varied, Dr. Samaratulagi Tondano Regional Hospital in Minahasa Regency, North Sulawesi is no exception. This research is focused on the development and application of the novel model, Extended HOT-Fit Framework, by incorporating Data Quality as the new dimension under Technology domain. The study adopted quantitative approach and applied Structural Equation Modeling (SEM) to evaluate the variables’ interrelationships. A survey of 150 respondents, all active users of SIRS, was conducted. The research instrument was designed according to the adapted 2006 HOT-Fit model. The results analysis indicates that Data Quality enhances the ability of the model to explain user satisfaction, the net benefit of the system. Moreover, System Quality and Service Quality dimensions also significantly impact user satisfaction, and organizational factors (Structure and Environment) have indirect impact on net benefits. This study provides empirical and technical contribution to the development of model for SIRS success evaluation in Indonesia. From a practical point of view, findings of this study can assist hospital managers in devising better improvement strategies for data quality and system integration in SIRS implementation.
Real-Time Air Quality Prediction Using IoT Sensor Networks and LSTM Deep Learning Model Ahmad Heru Mujianto; Chamdan Mashuri
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp512-517

Abstract

The increasing number of motor vehicles in urban areas has contributed to declining air quality, affecting both public health and the environment. This condition becomes more critical at road intersections with high traffic density, particularly during morning rush hours. This study aims to develop a real-time air quality prediction system based on Internet of Things and the Long Short-Term Memory (LSTM) method. Air quality data were collected using IoT-based sensors installed at a road intersection with a traffic density of approximately 200 motor vehicles per minute between 06:30 and 07:30 AM. The observed parameters included Air Quality Index (AQI), temperature, humidity, and air pollutant concentrations. The research stages consisted of sensor data acquisition, data preprocessing using Min-Max normalization, time-series dataset construction using a sliding window approach, and LSTM model training for air quality forecasting. Experimental results showed that the LSTM model was capable of predicting air quality effectively based on temporal sensor data patterns. The evaluation results produced a Mean Absolute Error (MAE) value of 2.046 and a Root Mean Square Error (RMSE) value of 2.076. The findings demonstrate that the integration of IoT and LSTM is effective for real-time air quality monitoring and forecasting. The novelty of this study lies in the use of real-time sensor data collected from a high-traffic road intersection and the integration of monitoring and forecasting systems within a single deep learning-based platform. The proposed system has the potential to support smart environmental monitoring and early warning systems in urban areas.
Assessing User Experience in Indonesian Fashion Mobile Applications Using a Usability Testing Approach Maryani Maryani; Benny Jabriel; Emmanuel Daniel W
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp518-524

Abstract

The rapid growth of mobile applications in the fashion industry has transformed how consumers interact with brands, browse products, and make purchasing decisions. While previous studies have examined consumer attitudes and behaviors toward fashion mobile applications, limited attention has been given to measuring and quantifying the overall user experience (UX), particularly in emerging markets such as Indonesia. This study aims to address this research gap by assessing the user experience of fashion mobile applications through questionnaire-based usability evaluation. The research employs design principles and user interface (UI) elements as analytical frameworks to evaluate how effectively fashion mobile app designs influence engagement, navigation ease, and purchase intention among Indonesian users. Data was collected from a sample of active fashion app users and analyzed to identify key usability factors that contribute to positive or negative user experiences. The findings reveal that interface aesthetics, interaction simplicity, and personalized recommendations have a significant impact on user satisfaction and in-app purchasing behavior. The results underscore the importance of user-centered design in developing fashion mobile applications tailored to the expectations and digital habits of Indonesian consumers. This study contributes to the growing body of literature on UX evaluation in mobile commerce by providing insights into how design-driven usability influences user engagement and business outcomes in the fashion retail context.
A Novel LSTM-TCN Deep Learning Model for Stock Price Forecasting in Indonesia Azkario Rizky Pratama; Bagus Rakadyanto Oktavianto Putra; Ascariena Rafinda
Jurnal Sistem Informasi Bisnis Vol 16, No 1 (2026): Volume 16 Number 1 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol16iss1pp%p

Abstract

Making predictions about stock prices is challenging since financial markets are always changing and dynamic. Our study involves analyzing how Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and their combination work in predicting stock prices. We obtained raw data from Yahoo Finance on the stock prices of five major banks and then prepared it for our analysis through imputation, normalization and using a sliding window. The Adam optimizer was used to fine-tune the learning rates when training the baseline models LSTM and TCN. According to our results, TCN performs better than LSTM when forecasting the prices of stocks. However, using LSTM-TCN as a hybrid model results in the lowest errors among all metrics, including MSE and MAE. It is demonstrated that combining LSTM with parallel TCNs improves the accuracy of predictions. From the study, it can be seen that the LSTM-TCN hybrid model could be a valuable tool for forecasting stock prices.
Evaluation of the User Experience of Shopee and TikTok Shop Among Generation Z Muhammad Galih Wonoseto; Dodi Saputro; Zahra Zakila Anindha Rahmanti; Eko Hadi Gunawan; Adriel Devara Sandji
Jurnal Sistem Informasi Bisnis Vol 16, No 2 (2026): Volume 16 Number 2 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol16iss1pp%p

Abstract

Digital transformation has accelerated the emergence of social commerce, a business model that integrates social interaction with online transactions. Although Shopee and TikTok Shop are among the leading digital commerce platforms in Indonesia, empirical studies comparing their user experience among Generation Z remain limited. This study aims to compare the user experience of Shopee and TikTok Shop among Generation Z users in Indonesia using the User Experience Questionnaire (UEQ). A comparative quantitative approach was employed, involving 60 respondents aged 18–27 years, consisting of 30 Shopee users and 30 TikTok Shop users selected through purposive sampling. Data were analyzed using UEQ score calculation, benchmark analysis, and independent sample t-tests. The results show that TikTok Shop achieved higher mean scores across all UEQ dimensions, particularly in Dependability (+0.47) and Stimulation (+0.33). UEQ benchmark analysis classified most Shopee dimensions as Below Average, whereas all TikTok Shop dimensions were categorized as Above Average. However, no statistically significant differences were found between the two platforms (p > 0.05), indicating that both provide relatively comparable user experiences. The novelty of this study lies in its comparative evaluation of a conventional e-commerce platform and a social commerce platform using the UEQ framework within the context of Generation Z users in Indonesia. The findings contribute to the growing body of social commerce literature by providing empirical evidence regarding how entertainment-oriented features and social interaction mechanisms shape user experience perceptions. These insights may assist platform developers in designing more engaging and user-centered digital commerce experiences.
A Qualitative Study on The Use and Role Of Learning Management System (LMS) Technology At SMP Islam Al-Azhar 20 Muhammad Yasir Rizqi; Riswan Septriayadi Sianturi; Hariz Farisi
Jurnal Sistem Informasi Bisnis Vol 16, No 1 (2026): Volume 16 Number 1 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol16iss1pp%p

Abstract

This study analyzes teachers’ acceptance and use of Learning Management System (LMS) technology at SMP Islam Al-Azhar 20 using the UTAUT2 framework. The study employed a non-implementative descriptive qualitative approach through semi-structured interviews analyzed using thematic analysis with NVivo, supported by a Likert-scale questionnaire based on UTAUT2 constructs for descriptive statistical analysis. The participants consisted of 19 teachers selected through total sampling. The findings indicate a generally positive acceptance across most UTAUT2 constructs. Teachers perceive the LMS as useful for supporting teaching performance, relatively easy to use, strongly encouraged by institutional expectations, and supported by adequate facilities. The user experience is also considered comfortable and engaging, resulting in a high intention to continue using the LMS. However, actual use in teaching practice is not fully uniform because habit formation and use patterns still vary among teachers. This suggests the need to strengthen habitual use, improve feature mastery, and enhance implementation support to achieve more stable and optimal LMS use in daily teaching practice.
Improving Low Birth Weight Prediction in Indonesia Through Hybrid Resampling and XGBoost Class Weighting with SHAP Interpretation Bramono Triprastowo; Adi Wibowo; Helmie Arif Wibawa
Jurnal Sistem Informasi Bisnis Vol 16, No 2 (2026): Volume 16 Number 2 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

Newborns are classified as having low birth weight (LBW) if they weigh less than 2,500 grams (5.5 pounds), which poses risks for stunted growth, developmental delays, and long-term health issues such as cardiovascular disease. This study develops an XGBoost based machine learning model for low birth weight classification using the 2017 Indonesia Demographic and Health Survey (IDHS). To address severe class imbalance, various resampling techniques including undersampling, oversampling, hybrid methods, and algorithm level class reweighting are applied, along with a modified random oversampling method (M-ROS). SHAP and XGBoost’s built in feature importance are then used to identify key predictors of low birth weight in the Indonesian context. A total of 16,340 records with 42 preprocessed features were used in this study. The dataset was split into 70 percent for training and 30 percent for testing, and stratified k-fold cross-validation with random search was employed to identify the optimal hyperparameters. Among the resampling strategies evaluated, random oversampling, M ROS, ENN with class reweighting, and class reweighting without any resampling were able to improve recall. Of these approaches, class reweighting without resampling achieved the highest recall at 36.63 percent, while ENN combined with class reweighting produced the highest accuracy at 84.72 percent. Key predictors influencing the classification of low birth weight included the number of antenatal care visits, multiple pregnancy, and place of delivery. The findings demonstrate that machine learning can be effectively utilized to predict low birth weight and to identify influential contributing factors.
Comparative Evaluation of E-Learning Satisfaction: Divergence between SUS, PIECES, and EUCS Frameworks Rahmad Hidayat
Jurnal Sistem Informasi Bisnis Vol 16, No 1 (2026): Volume 16 Number 1 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol16iss1pp%p

Abstract

The deployment of e-learning systems in educational institutions is more concerned with speed than with the system's usability. In this study, we focused on the satisfaction and usability of Jimbaran Bali Global SMK elearning system. We applied the System Usability Scale (SUS) and End User Computing Satisfaction (EUCS) models alongside the PIECES framework. These methods have generally been applied separately, resulting in a limited understanding of the relationship between usability, system performance, and user satisfaction. Furthermore, studies that integrate and compare SUS, PIECES, and EUCS within a single evaluation framework, particularly in the Indonesian educational context, remain limited. The comparison of these methods is necessary because SUS focuses on usability, PIECES evaluates system and operational performance, and EUCS measures end-user satisfaction. Therefore, this study aims to evaluate the e-learning system of SMK TI Bali Global Jimbaran by comparing and integrating these three methods to provide a more comprehensive assessment of e-learning effectiveness. Having conducted both validation and reliability on the instrument, we analyzed data from 116 respondents, The results showed a discrepancy in usability and satisfaction, Satisfaction with PIECES and EUCS was positive, whereas the SUS score of 57.39 (Marginal–Low) is indicative of a significant problem in system usability. The findings show that users appreciated the system, but lacked the ability to use the system. These findings show that the system responsiveness, interface, and service quality must be improved to increase the positive perceptions of users and achieve sustainable e-learning.

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