Abstract
Patient histories who use the services of Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan are stored in medical record data. Each medical record data contains important information that is very valuable and can be processed to explore new knowledge using a data mining approach. This study aims to help Prof. Dr. Tabrani hospital in classifying patient data who use BPJS Kesehatan, so that the pattern of disease spread is known based on class of service. The data used is patient medical record data in 2019 from October to December, the data will be processed using the K-Means Clustering algorithm with a total of 3 clusters. In cluster 0 (H0) there are 3 patients who are dominated by A09.9 disease (Diarrhea / Dysentery) in Class 2 and Class 3, for cluster 1 (H1) there are 5 patients with more diverse types of disease, while for cluster 2 (H2) there are 5 patients who are predominantly K30 disease (Dyspepsia) in Class 1.
Highlights
Patient histories who use the services of Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan are stored in medical record data
This study aims to help Prof
Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering
Summary
Penelitan Asroni dkk (2018) dalam pengelompokkan data calon mahasiswa baru menggunakan algoritma KMeans Clustering, penelitian menunjukkan bahwa KMeans berhasil dalam pengelompokkan data dengan hasil bahwa jurusan Pendidikan Dokter merupakan yang paling favorit dengan persentase 33% [6]. Data yang bersifat baru, dapat dimengerti dan Penelitian Sukamto dkk (2018) dalam penentuan daerah rawan titik api, cluster dibagi kedalam 3 kelompok, yaitu 133 daerah sangat rawan titik api, 101 bermanfaat. Penelitian Aprianti dkk (2018) dalam yang bertujuan mengelompokkan objek-objek kedalam klasterisasi data kecelakaan lalu lintas jalan raya, hasil cluster-cluster. Diantara keunggulan yang dimiliki algoritma K-Means Clustering adalah [18]: dengan potensi sedang, dan Kecamatan a. Menentukan jumlah cluster yang diinisialkan dengan K; Menentukan centroid awal dengan cara random; Menentukan centroid terdekat dari setiap titik data dengan menghitung jarak ke setiap centroid menggunakan rumus Euclidean Distance, seperti pada persamaan (1).
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