<![CDATA[Abstrak - Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang mengoptimalkan pelayanan posyandu lansia melalui penerapan metode K-Means Clustering. Sistem ini dirancang untuk mengklasifikasikan data lansia berdasarkan prioritas pelayanan, yakni lansia prioritas yang memerlukan kunjungan posyandu langsung ke rumah, dan lansia non-prioritas yang dapat datang ke posyandu secara mandiri. Metode K-Means digunakan untuk mengelompokkan lansia ke dalam dua klaster utama berdasarkan beberapa parameter. Proses pengembangan sistem melibatkan pengumpulan data lansia dari Dusun Jetis, Kelurahan Sendangsari, Kecamatan Pajangan, Kabupaten Bantul. Setelah itu, data tersebut diolah menggunakan metode K-Means untuk menentukan klasterisasi lansia. Hasil pengujian menunjukkan bahwa sistem berhasil mengelompokkan lansia dengan akurasi yang memadai, sehingga memudahkan kader posyandu dalam menentukan lansia yang harus diprioritaskan untuk pelayanan rumah. Sistem pendukung keputusan ini diimplementasikan dalam platform berbasis web untuk mempermudah akses dan penggunaan oleh kader posyandu. Dengan adanya sistem ini, diharapkan pelayanan posyandu lansia dapat lebih efisien dan terarah sesuai dengan kondisi kesehatan lansia di wilayah tersebut. Kata kunci: Sistem Pendukung Keputusan, Posyandu Lansia, K-Means Clustering, Prioritas Pelayanan, Clustering Data. Abstract - This research aims to develop a decision support system that optimizes elderly healthcare services at community health posts (Posyandu Lansia) through the application of the K-Means Clustering method. The system is designed to classify elderly data based on service priority, identifying priority elders who require home visits and non-priority elders who can independently attend the Posyandu. The K-Means method is utilized to group the elderly into two primary clusters based on multiple parameters. The system development process involved collecting elderly data from Jetis Hamlet, Sendangsari Village, Pajangan District, Bantul Regency. This data was then processed using the K-Means method to determine the elderly clustering. Testing results indicate that the system effectively clusters the elderly with satisfactory accuracy, assisting Posyandu workers in identifying elderly individuals who should be prioritized for home-based services. This decision support system is implemented on a web-based platform to improve accessibility and ease of use for Posyandu staff. With this system in place, it is anticipated that elderly healthcare services will become more efficient and targeted according to the health conditions of the elderly in the area. Keywords: Decision Support System, Elderly Posyandu, K-Means Clustering, Service Priority, Data Clustering]]>
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