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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 950 Documents
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Development of Hybrid K-Means DBSCAN Algorithm for Optimization of Landslide-Prone Area Clusters based on Web-GIS Irmayanti, Dede; Hermanto, Teguh Iman
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5671

Abstract

Landslides represent one of the major geological hazards in West Java Province, posing serious impacts on social life, economic activities, and public infrastructure. A key challenge in landslide mitigation lies in the inaccuracy of spatial and temporal classification of landslide-prone areas, as well as the limitations of single-method approaches in disaster data analysis. This study aims to develop a data-driven classification model for landslide-prone areas using a hybrid clustering approach that combines the K-Means and DBSCAN algorithms. The dataset consists of landslide incident records from 2020 to 2024 and administrative spatial data at the regency/city level. The analysis stages include data integration and normalization, statistical exploration, the application of K-Means clustering as a global segmentation framework, and DBSCAN for identifying local patterns and outliers. Model validation was conducted using internal evaluation metrics, yielding a Silhouette Coefficient of 0.448 and a Davies–Bouldin Index of 0.602, indicating that the hybrid method provides superior performance in terms of cluster compactness and separation. The classification results are visualized through an interactive Web-GIS platform developed using Streamlit and Folium, enabling users to select specific years and classification methods while displaying mitigation strategies based on risk categories. This study concludes that the hybrid clustering approach enhances the accuracy of landslide-prone area classification and makes a significant contribution to the provision of more adaptive and practical spatial information to support mitigation policy decision-making in landslide-vulnerable regions.
Identification of Rice Production Clusters in Central Java Province with K-Means Technique Wibowo, Yudi Wahyu; U, Ihsan Cahyo; Widayat, Widi; S A, Juanita Tria; Nirot A, As'ad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5327

Abstract

Central Java Province stands as a pivotal agricultural region in Indonesia, characterized by high productivity in paddy and rice cultivation. However, substantial production disparities persist across districts, attributed to varying geographical conditions, agricultural infrastructure, and farmers' cropping patterns. Consequently, the identification of production clusters is imperative for elucidating production patterns and formulating targeted policy interventions. This study aims to classify districts in Central Java based on production metrics using the K-Means Clustering technique implemented in the R statistical environment. Production data across various regions were analyzed to determine optimal clustering patterns. The clustering analysis stratified the study area into four distinct agricultural typologies: optimal performance zones (Cluster 3, n=2), land-based volume producers (Cluster 1, n=7), small-scale efficient producers (Cluster 4, n=15), and priority intervention areas (Cluster 2, n=11). These findings underscore the necessity for differentiated policy strategies addressing the disparities in efficiency and production scales.
Novel Genre Classification based on Synopsis using the Random Forest Algorithm Mahanani, Prananing; Fibriani (SCOPUS ID=57192643331), Charitas
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5815

Abstract

Novel genre classification based on synopses presents a significant challenge in text processing, as each genre exhibits distinct lexical characteristics. This study evaluates the performance of the Random Forest algorithm in classifying novel genres under conditions of imbalanced data distribution. The research stages include text preprocessing—comprising case folding, tokenization, stopword removal, and stemming—feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and model training with Random Forest. In addition, manual data balancing was applied by increasing samples in minority classes through simple oversampling. The model was evaluated using accuracy metrics and confusion matrix analysis. The results indicate that Random Forest is able to identify most genres with moderate accuracy, particularly for classes with larger data volumes. The initial model achieved an accuracy of 42.11%, which increased to 46.67% after the application of data balancing. Misclassification primarily occurred in genres with limited samples that share similar vocabulary with dominant genres. These findings demonstrate that Random Forest can still be applied to synopsis-based novel genre classification without fully relying on balancing techniques. However, performance remains uneven across classes, highlighting the need for per-genre analysis to obtain a more comprehensive evaluation.
Expert System for Mental Health Disordes in Women and Children based on Android using the Certainty Factor Method Lasena, Marlin; Gobel, Citra Yustitya; Puspa, Misrawati Aprilyana
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5739

Abstract

This study focuses on the limited access to mental health services in Gorontalo and the social stigma surrounding mental health disorders, which discourages women and children from seeking help. The main problem addressed in this research is the management of mental health disorders, particularly the complexity of the initial diagnostic process. The study aims to develop an expert system using the Certainty Factor method to assist in the early diagnosis of mental health disorders in women and children, specifically Depression, Anxiety, and Stress disorders. The research employs a Research and Development (R&D) approach with qualitative methods, including interviews with experts such as psychologists to obtain a knowledge base comprising symptoms, Measure of Belief, and Measure of Disbelief values. The expert system is implemented on the Android platform, facilitating user access to early diagnostic services. Results from the Certainty Factor calculations on user data indicate that the early diagnosis confidence levels are 97.04% for Depression, 63.11% for Anxiety, and 59.72% for Stress. The highest value is observed for Depression, suggesting that the symptoms selected by users most strongly indicate this disorder, with the highest confidence level across all Depression symptoms. Both manual calculations by experts within the system and Black Box testing confirm that the Certainty Factor method can effectively support early diagnosis of mental health disorders. The study concludes that the expert system using the Certainty Factor method is effective and can be implemented as an early mental health detection tool. The strength of this research lies in the integration of qualitative expert knowledge with mobile technology implementation, providing a practical and easily accessible solution for the community.
Comparative Analysis of User Experience in SeaBank and Bank Jago using the User Experience Questionnaire Hendriyani, Shinta Dwina; Lattu, Arny; Rosita, Moneyta Dholah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5542

Abstract

The rapid development of information technology has driven a shift in banking services from conventional systems toward more efficient and flexible digital platforms. Generation Z, as digital natives, has become the primary user group of these services due to their strong preference for convenience and ease in financial transactions. According to data from the Google Play Store, the SeaBank and Bank Jago applications have each been downloaded more than 10 million times, indicating strong public interest in digital banking services. Despite having a similar number of downloads, there is a notable difference in user ratings: SeaBank has received a rating of 4.9 out of 5, while Bank Jago has obtained a rating of 4.5. However, research that directly compares user experience across popular digital banking platforms in Indonesia remains limited. Therefore, this study contributes empirical insights into the factors influencing Generation Z’s satisfaction when using digital banking services. This study aims to analyze and compare the user experience of the SeaBank and Bank Jago applications using the User Experience Questionnaire (UEQ) method. Research data were collected through questionnaires distributed using purposive sampling, targeting Generation Z respondents aged 17–28 years. The collected data were analyzed using the UEQ Data Analysis Tool. The results indicate that both applications received positive evaluations from respondents. Bank Jago outperformed SeaBank in the Attractiveness dimension, reflecting stronger visual and emotional appeal. Meanwhile, SeaBank showed superior performance across the other five dimensions: Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. These findings suggest that SeaBank provides a more efficient, clear, engaging, and innovative user experience compared to Bank Jago.
Predict Airline Customer Satisfaction using a Machine Learning Model Suwito, Yoel Dinata; Susetyo, Yeremia Alfa
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5868

Abstract

Customer satisfaction is a strategic factor for the sustainability of airline businesses amid increasingly intense competition in the aviation industry. This study aims to predict airline customer satisfaction using an Artificial Neural Network (ANN) approach by leveraging a publicly available Kaggle dataset containing 22 airline service features. Two ANN architectures were developed, differing primarily in the number of hidden layers, the number of neurons, and the application of Batch Normalization and LeakyReLU in the second model. The experimental results show that the first ANN model achieves an accuracy of 92.31%, while the second model attains significantly higher performance, with an accuracy of 95.75% on the test dataset. The second model also demonstrates a strong balance between precision and recall (0.94–0.97), with an average F1-score of 0.95–0.96 and a minimal number of misclassifications. These results confirm that employing a more complex ANN architecture can deliver highly accurate predictions of customer satisfaction. The implementation of ANN-based predictive models not only enhances passenger experience quality but also strengthens customer loyalty and helps airlines maintain long-term competitiveness.
Development of an Intelligent Platform for Drug and Food Interaction Analysis using a Combination of Fuzzy Logic and Certainty Factor Apriliany, Fitri; Alfiansyah, Muhammad Wisnu; Afni, Zahratul Hayatil Laila
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5767

Abstract

Interactions between drugs and food are a critical public health issue, as they can cause unwanted side effects or reduce the effectiveness of treatments. Unfortunately, awareness of these potential interactions among the Indonesian population remains low, while existing platforms generally focus only on drug–drug interactions. This study aims to develop an intelligent platform for analyzing drug–food interactions by combining fuzzy logic and certainty factor (CF) methods. Fuzzy logic is employed to handle uncertainty in interaction data, while the certainty factor enhances confidence levels based on clinical literature and expert knowledge. Drug–food interaction data were collected from validated sources and modeled using fuzzy membership functions, IF–THEN rule-based reasoning, defuzzification processes, and integration with CF. The web-based system was evaluated through accuracy testing and usability assessment using the System Usability Scale (SUS). Accuracy tests conducted on 50 interaction scenarios demonstrated a 100% match with clinical references, while the SUS evaluation involving 100 respondents yielded an average score of 77.44, falling into the “Acceptable” category and approaching “Good Usability.” These results indicate that the platform has the potential to serve both as an educational tool and as a practical aid for the public to enhance self-management of health, while also supporting government health programs.
Prototype of IoT-based Gas and Temperature Monitoring System for Genset Room with ESP8266 Pramono, Agus; Ulhaq, Muhammad Zhiya; Mukti, Gilang Dely
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5711

Abstract

Generator rooms require strict environmental monitoring to ensure operational safety, as uncontrolled temperature and gas concentration conditions may pose risks of fire and workplace accidents. This study aims to develop an Internet of Things (IoT)–based generator room monitoring system capable of automatically and real-time monitoring temperature, humidity, and gas levels, with notifications delivered via a Telegram bot. The research method adopts a systematic approach through prototype development. The system utilizes DHT22 and MQ-2 sensors, a NodeMCU ESP8266 microcontroller, a buzzer, and is integrated with the Telegram Bot API. The testing results demonstrate that the device is able to responsively monitor environmental parameters, transmit real-time data to Telegram, activate the relay with a response time of 4–5 seconds, and provide automatic notifications when gas concentrations exceed safe thresholds with a delay of 1–2 seconds. Evaluation of the monitoring functionality and sensor data visualization through the Telegram application indicates that all sensor information is successfully displayed via the Telegram bot interface. Although data transmission from the sensors to the Telegram application is highly dependent on network connection stability, the experimental results show that the system is capable of delivering data with optimal response time.
Functional Evaluation of the Virtual Batik and Mask Museum Application using Test Scenario Based Ibrahim, Fajar Maulana; Astuti, Ika Asti
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5371

Abstract

The development of digital technology has created new opportunities to introduce and preserve local culture through interactive media, one of which is virtual museums. This study develops a 3D virtual museum application aimed at introducing and preserving batik and mask cultural heritage in response to the need for technology-based educational media. The functionality of the application was evaluated using a scenario-based testing method involving 10 main test scenarios. The testing results indicate an overall excellent performance, with a success rate of 85%. Four activities—including 3D object interaction (mask), wall collider functionality, background sound, and lighting—achieved very good performance. The main weakness was identified in the notification trigger feature, where four users failed to complete the assigned task. The total time required by 10 users was 167.8 seconds, with an average of 16.78 seconds per task. Overall, these results demonstrate that the application has high functional stability and is suitable for use as an innovative and interactive medium for cultural learning.
Systematic Review of the Use of the MIT-BIH Polysomnography Database for the Detection and Classification of Sleep Disorders Akbar, Akbar; Utami, Ema
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5819

Abstract

The MIT-BIH Polysomnography Database (SLPDB) is a widely adopted benchmark for the development of automated methods for sleep disorder detection and sleep stage classification. This study presents a Systematic Literature Review of 35 articles that utilize the SLPDB, examining research focus areas, types of physiological signals employed, and the computational approaches applied. Five major methodological categories were identified: Sleep Apnea Detection, Sleep Staging, Signal Processing Enhancement, Multichannel Fusion Methods, and Interpretable Artificial Intelligence, with the first two categories being the most dominant. Four groups of physiological signals—EEG, ECG, respiratory signals, and multichannel data—form the basis for model development, where EEG is predominantly used for sleep staging and ECG for sleep apnea detection. Deep learning approaches, particularly CNNs, LSTMs, and hybrid models, are the most frequently employed techniques. Reported model accuracies range from 78% to over 99%, depending on the signal modality and modeling strategy. Future research should prioritize the development of more interpretable hybrid models and broader clinical validation to enhance reproducibility and implementation readiness.

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