INPATIENT CLUSTER ANALYSIS FOR MEDICAL RESOURCE OPTIMIZATION USING K-MEANS CLUSTERING

Adika May Sari (Universitas Bina Sarana Informatika)
Amas Sari Marthanti (Universitas Bina Sarana Informatika)
Rosmita (Universitas Bina Sarana Informatika)
Dessy Suryani (Universitas Bina Sarana Informatika)
Ahmad Rafik (Universitas Bina Sarana Informatika)

Akrab Juara : Jurnal Ilmu-ilmu Sosial
Vol. 11 No. 2 (2026): Mei

The objective of this study is to analyze inpatient data in order to segment patients based on specific characteristics such as age, type of illness, medical procedures, and length of stay. The K-Means Clustering method is applied to identify patterns or patient segments that can be utilized in decision-making related to bed management and medical staff allocation more efficiently. The analysis was conducted using the Python programming language for data processing and result visualization. The findings indicate the existence of several groups of patients with distinct characteristics, which can serve as a strategic reference for improving service quality and the operational effectiveness of the hospital.