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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Arif Susilo
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Pendekatan Interpretatif dalam Prediksi Persalinan Caesar Menggunakan Decision Tree pada Data Pelayanan Kesehatan Primer Arif Susilo; Asep Arwan Sulaeman
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.919

Abstract

Caesarean delivery is a medical procedure performed under specific conditions to reduce risks for both mother and baby. However, the increasing rate of caesarean deliveries, which is not always based on medical indications, highlights the need to systematically understand the factors influencing delivery methods. This study aims to explore the relationship between clinical variables of pregnant women and delivery methods using a data mining approach based on Decision Tree and Random Forest algorithms. The dataset consists of secondary data collected from three primary healthcare centers (Puskesmas), namely Mranti, Banyuurip, and Bayan, with a total of 390 records. The study follows the Knowledge Discovery in Database (KDD) framework, including data selection, preprocessing, transformation, dataset splitting, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), modeling, and evaluation. The results show that the model achieved an accuracy of 88%, precision of 58.82%, recall of 66.67%, and an F1-score of 62.50%. Although the accuracy appears relatively high, the model’s performance in identifying caesarean cases remains moderate. This indicates that the model is more effective in classifying the majority class than the minority class. This study highlights that data mining applied to primary healthcare data can provide valuable insights for early pattern identification. However, the obtained results are not sufficient for direct clinical decision-making. Future research with larger datasets and more adaptive methods is required to improve model performance.
Identifikasi Indikasi Risiko Depresi pada Unggahan Media Sosial X Menggunakan Natural Language Processing dan Algoritma Random Forest Arif Siswandi; Arif Susilo
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1026

Abstract

Depression among university students has become an important mental health concern due to its potential impact on quality of life and academic performance. Social media platform X, as a text-based communication medium, provides a space for spontaneous expression that may reflect users’ emotional states. This study aims to analyze linguistic patterns associated with indicative depressive expressions in social media posts using a Natural Language Processing (NLP) approach and the Random Forest algorithm. Data were collected through web scraping between January and November 2024 using keywords conceptually derived from the Patient Health Questionnaire-9 (PHQ-9) indicators and adapted to linguistic expressions commonly used in social media communication. From an initial collection of 36,081 posts, several filtering stages were conducted, including duplicate removal, language filtering, and elimination of irrelevant content, resulting in a final dataset of 1,070 posts used in this study. The high filtering rate indicates that many scraped posts did not directly represent relevant emotional expressions. The dataset was manually labeled into three indicative categories of depressive expressions: mild, moderate, and severe. The analytical process included text preprocessing, TF-IDF feature extraction, and classification modeling using the Random Forest algorithm. The evaluation results show an accuracy of 97%. However, this value should be interpreted cautiously because model performance may be influenced by dataset characteristics and the manual labeling process. Therefore, the proposed model should be regarded as an exploratory approach for identifying linguistic patterns associated with emotional expressions in social media text rather than a clinical diagnostic tool for depression.
Clustering Pola Penggunaan Energi pada Smart Home Menggunakan DBSCAN Berbasis Data Time Series Sensor Arif Susilo; Asep Arwan Sulaeman; Nur Suci Rahayu
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10519

Abstract

The increasing adoption of Internet of Things (IoT) devices in smart homes has generated continuous and complex energy consumption data, requiring effective clustering techniques to identify household energy usage patterns. This study aims to cluster household energy consumption patterns using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on sensor time-series data. The study utilized the Smart Home Energy Consumption Dataset, consisting of approximately 90,000 observations with six main variables: Energy Consumption, Peak Hours Usage, Household Size, Average Temperature, Has AC, and Weekday. The research workflow included feature selection, data cleaning, data normalization using StandardScaler, parameter determination through the K-Distance Graph, DBSCAN clustering, and clustering evaluation using the Silhouette Score. Experimental results indicated that the optimal parameters were ε = 0.38 and MinPts = 5, producing 89 clusters, 541 noise observations (0.60%), and a Silhouette Score of 0.0690. Cluster characteristic analysis revealed that energy consumption, peak-hour energy usage, air conditioner ownership, household size, and ambient temperature were the primary factors distinguishing household energy usage patterns. The findings demonstrate that DBSCAN effectively identifies household energy consumption patterns while detecting outliers without requiring the number of clusters to be predefined, making it a promising approach for supporting intelligent energy management systems in smart home environments.
Prediksi Indeks Pembangunan Manusia Menggunakan Support Vector Regression dengan Optimasi Particle Swarm Optimization Arif Siswandi; Arif Susilo; Rizki Muhammad Mukti
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10535

Abstract

The Human Development Index (HDI) is a key indicator for measuring regional development performance and serves as an essential reference for evidence-based policy formulation. Accurate HDI prediction is crucial to support effective development planning and decision-making. This study aims to develop an HDI prediction model using Support Vector Regression (SVR) optimized with Particle Swarm Optimization (PSO) to improve prediction accuracy. The dataset was obtained from Statistics Indonesia (BPS), covering 38 provinces during the 2015–2025 period with a total of 421 observations. The research process consisted of data preprocessing, Min-Max Scaling normalization, an 80:20 train-test split, SVR model development, parameter optimization using PSO, and performance evaluation based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the baseline SVR model achieved an MAE of 0.069187, RMSE of 0.093548, and R² of 0.492454. After PSO optimization, the model performance improved, achieving an MAE of 0.060864, RMSE of 0.084224, and R² of 0.588583. These findings demonstrate that PSO effectively enhances the predictive performance of SVR by identifying optimal parameter combinations. The main contribution of this study is the development and validation of an optimized SVR-PSO framework for HDI prediction using multi-provincial socioeconomic data in Indonesia, providing a more accurate machine learning-based approach to support data-driven human development planning and policy formulation.
Co-Authors Arif Siswandi Asep Arwan Sulaeman Nur Suci Rahayu Rizki Muhammad Mukti