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Jeri Wandana
Universitas Putra Indonesia YPTK Padang
Computer Science & IT

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Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means Jeri Wandana; Sarjon Defit; Sumijan Sumijan
Jurnal Informasi dan Teknologi 2020, Vol. 2, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v2i4.73

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.
Co-Authors Sarjon Defit Sumijan Sumijan