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Tarigan, Mayang Safhira
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Computer Science & IT Electrical & Electronics Engineering

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Clustering Medical Record Data on Diabetes Disease using Divisive Analysis Clustering Method Tarigan, Mayang Safhira; Hasibuan, Muhammad Siddik
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4380

Abstract

Diabetes is a chronic disease characterized by high levels of sugar (glucose) in excess of normal limits, about 1.5 million deaths each year are directly attributed to diabetes. The purpose of this study is to obtain the optimal cluster analysis results on grouping medical record data on diabetes and obtain the silhouette coefficient value of the optimal cluster analysis results on grouping medical record data on diabetes at TK-II Putri Hijau Kesdam I / B Hospital, Medan-North Sumatra by using the divisive analysis algorithm and determining the variables that cause the patient to develop diabetes. The results showed that the optimal cluster using the divisive analysis algorithm was 2 clusters with a value of 0.468582 which stated that the cluster structure formed in this grouping was the right cluster. And for variables that cause patients to develop diabetes, namely age (X2) and blood sugar level (X8). it is because these two variables have the highest average value among the two clusters and all these variables.
Co-Authors Muhammad Siddik Hasibuan