Analisis Clustering Penyakit Menular pada Manusia di Jakarta Timur Menggunakan Algoritma K-Means - Ramadhan, Muhammad Arya; Poerwandono, Edhy; Akbar, Yuma; Hidayat, Aditya Zakaria (Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 2025)
Humans are highly susceptible to various diseases without realizing their causes. The high incidence of infectious diseases in East Jakarta requires an analysis of distribution patterns to determine intervention priorities. This study aims to identify clusters of infectious diseases in East Jakarta, helping authorities plan effective prevention and treatment strategies. Data on infectious disease cases were obtained from the Central Statistics Agency of DKI Jakarta. The K-Means algorithm was used to cluster data based on variables such as period, region, type of disease, and number of cases. The results indicate several main clusters with distinct characteristics that can serve as a foundation for targeted strategies. From 2018 to 2021, diarrhea was predominant, making up 84.14% of cases in 2018 and 81.97% in 2019, pneumonia accounted for 32.92% in 2020, and TB Paru 33.63% in 2021. In conclusion, the K-Means algorithm effectively clusters infectious disease data and provides useful insights into disease distribution in East Jakarta, improving the impact of data-driven health programs.