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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Asep Arwan Sulaeman
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.
Analisis Tingkat Sentimen Opini Publik Terhadap Kebijakan TV Digital di Platform X Menggunakan Multinomial Naïve Bayes Asep Arwan Sulaeman; Candra Naya; Muhtajuddin Danny; M. Makmun Effendi
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.v6i2.951

Abstract

The migration from analog to digital television broadcasting is part of the transformation of the broadcasting system aimed at improving broadcast quality and spectrum efficiency. However, the implementation of the digital television policy has generated diverse public responses, ranging from support to criticism. This study aims to analyze public opinion on the digital television policy in Indonesia using social media data from platform X. A quantitative approach was employed using text mining and supervised machine learning techniques. Data were collected through a crawling process using the keyword “tv digital”, resulting in 1,855 tweets. After data selection and cleaning, 789 tweets were obtained as the final dataset. The analysis stages included text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), and sentiment classification using the Multinomial Naïve Bayes algorithm. The results indicate that positive sentiment dominates public opinion, with 478 tweets (60.58%), while negative sentiment accounts for 311 tweets (39.42%). Model performance evaluation shows an accuracy of 79.21%, precision of 82.45%, and recall of 85.06%, indicating that the model performs well and consistently in classifying sentiment. These findings demonstrate that social media–based sentiment analysis can serve as an empirical approach to understanding public perceptions of digital television policy.
Prediksi Konsentrasi CO(GT) Menggunakan Long Short-Term Memory pada Data Sensor Kualitas Udara IoT Asep Arwan Sulaeman; Candra Naya; Ahmad Turmudi Zy; Riyadi Riyadi
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1162

Abstract

Air quality deterioration has become a major challenge for public health and environmental management in urban areas. Internet of Things (IoT)-based monitoring systems continuously generate sensor data that can be exploited for air quality prediction; however, these datasets commonly contain missing values, noise, and temporal dependencies that may reduce prediction accuracy. This study proposes a Long Short-Term Memory (LSTM)-based model to predict carbon monoxide (CO(GT)) concentrations using the Air Quality UCI dataset, which consists of 9,357 observations and 15 attributes. During preprocessing, -200 values were identified as missing-value indicators, followed by invalid-data handling, Min-Max normalization, and sequence generation using a sliding-window approach with a window size of four. The processed data were divided into training and testing sets using an 80:20 ratio. The prediction model employs a single LSTM layer with 50 hidden units and a Dense output layer and is trained using the Adam optimizer for 50 epochs. Experimental results achieved a Mean Absolute Error (MAE) of 0.0389 and a Root Mean Squared Error (RMSE) of 0.0567, indicating that the proposed model effectively captures temporal patterns in air quality observations with relatively low prediction errors. These findings are consistent with previous studies reporting the effectiveness of LSTM for air quality forecasting and demonstrate its potential to support continuous IoT-based environmental monitoring systems. Future work may incorporate hyperparameter optimization and comparative evaluations with alternative deep learning architectures to further improve predictive performance.
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.
Co-Authors Ahmad Turmudi Zy Arif Susilo Candra Naya M. Makmun Effendi Muhtajuddin Danny Nur Suci Rahayu Riyadi Riyadi