Data Mining Analysis for Clustering the Number of Tb Patients in North Aceh Health Centers Using the Spectral Method Clustering - Khainesya, Khainesya; Darnila, Eva; Risawandi, Risawandi (Malikussaleh University, Aceh, Indonesia, 2025)
Tuberculosis (TB) is one of the infectious diseases that is a significant concern in the world of health, especially in the North Aceh region. Grouping the number of TB patients based on severity and region is very important to support decision-making in further prevention and treatment efforts. This study applies the Spectral Clustering method to cluster the number of TB patients at Baktiya Health Center, Bayu Health Center, and Lhoksukon Health Center to identify patient distribution patterns based on severity categories. The system built is a web-based data mining analysis system using PHP and MySQL as a database. Clustering is done by dividing patients into three categories, low, medium, and high, based on five main criteria, namely age, gender, month of treatment, diagnosis results, and patient address. The results showed that Lhoksukon Health Center had the highest number of TB patients, with 136 patients (37.06%), an average age of 48.6 years, and the most cases occurred in December 2022. Bayu Health Center was at a moderate level with 130 patients (35.42%), most of whom were 45.5 years old, and most cases occurred in November 2023. Meanwhile, Baktiya Health Center had the lowest number of patients, 101 (27.52%), with the most cases occurring in November. From the clustering results, it can be concluded that the Spectral Clustering method can group TB patients well to help medical personnel and related parties develop more effective intervention strategies based on the region and severity of the patient.