Clustering Data Pasien Berdasarkan Usia di Puskesmas Menerapkan Metode K-Means - Herlin Lutfiannisa, Alifia; Maimunah, Maimunah; Sukmasetya, Pristi (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
This research aims to perform clustering of disease data based on patient age using the K-Means method at the Tlogomulyo Community Health Center in Temanggung Regency, which faces challenges in managing irregular health data. As a primary healthcare centre, the health facility requires data analysis to identify patterns or groups of diseases based on age. The K-Means clustering method is employed in processing patient data to understand the distribution of diseases and aid decision-making regarding prevention, treatment, and healthcare planning. The clustering results reveal two clusters where the first cluster is dominated by scabies cases in the 10-20 age group, while the second cluster exhibits a high prevalence of Acute Respiratory Infection (ARI) in patients around the age of 51. Evaluation using the Silhouette Coefficient indicates that forming 2 clusters is the most optimal, with a value of 0.44. These findings provide crucial insights for the development of more effective disease management strategies based on the characteristics and health profiles of each cluster at the Tlogomulyo Community Health Center.