Penerapan Data Mining Untuk Prediksi Peningkatan Jumlah Orang Dengan Gangguan Jiwa (ODGJ) Di Kota Padang Menggunakan Metode K-Means - yogi ilhamdi (Jurnal Sains Informatika Terapan, 2025)
This study aims to apply the K-Means algorithm to predict the risk of an increase in the number of People with Mental Disorders (ODGJ) at Prof. Dr. HB. Saanin Mental Hospital. The focus is on clustering patient data based on primary diagnoses such as biological, psychological, and social factors to identify patterns that can predict low, medium, and high-risk increases. The research is motivated by the difficulties faced by relevant parties in analyzing ODGJ data in Padang City. The method used is data mining, specifically the K-Means algorithm, to identify patterns of increased ODGJ risk. The analyzed data includes inpatient and outpatient records for the year 2024. The results show that the K-Means algorithm can effectively cluster sub-districts in Padang City based on their risk of increase, with high-risk areas identified as Koto Tangah, Kuranji, and Lubuk Begalung. Testing using RapidMiner yielded results consistent with manual analysis. The implementation of a developed web-based system provides convenience for administrators in efficiently managing patient data, with features for recording the number of patient admissions. The findings of this study are expected to contribute to the development of information systems in the health sector and serve as a reference for future research.