Pengelompokkan Penyakit Tuberkulosis Paru Berdasarkan Penyebabnya Menggunakan Metode Clustering: Studi Kasus : UPT Puskesmas Selesai - Cinta Apriliza; Relita Buaton; Hermansyah Sembiring (Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi, 2025)
Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.