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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 424 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 (In Press)
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 (In Press)
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
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025 (In Press)
Publisher : Diponegoro University

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

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 (In Press)
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.

Page 43 of 43 | Total Record : 424

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