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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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Journal : bulletin of computer science research

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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.
Co-Authors Arif Siswandi Asep Arwan Sulaeman Nur Suci Rahayu Rizki Muhammad Mukti