Klasterisasi Pola Penyebaran Penyakit Pasien Berdasarkan Usia Pada Puskesmas Kedungtuban Menggunakan Algoritma K-Means Clustering

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Anggraini, Dyah Selvi (2023) Klasterisasi Pola Penyebaran Penyakit Pasien Berdasarkan Usia Pada Puskesmas Kedungtuban Menggunakan Algoritma K-Means Clustering. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

This study aims to classify the diseases suffered by the people of the Kedungtuban District based on age at the Kedungtuban Health Center with the k-means clustering algorithm using the Ms. Excel and Orange. From calculations using Ms. Excel obtained 3 clusters, namely C1 which is the highest cluster of disease sufferers that are widely experienced by the elderly, namely DM and HT. C2 is a patient with moderate diseases that are mostly experienced by adults, namely gastric disease and tuberculosis. C3 is a disease cluster with the lowest number of sufferers experienced by adolescents and children, namely stroke and neurological diseases. From the calculation results in the Orange Application, the results of C1 are 14 types of diseases which are the diseases that often affect the adult and elderly age groups, namely DM and HT. C2 focuses on adolescents, adults and the elderly, where there are 13 types of diseases. Tuberculosis and gastric disease are diseases that many suffer from at this age. At C3, this cluster focuses on children and adolescents. There are 18 types of disease. CC disease and skin disease are the diseases most commonly suffered by this age group. Clustered data using k-means clustering can be used as a reference for health extension workers in preventing the spread of epidemics and determining preventive measures in various patient clusters and can help determine the amount of drug stock in the future.

Item Type:	Thesis (Undergraduate)
Additional Information:	Dyah Selvi Anggraini NIM : 19083000195
Uncontrolled Keywords:	Clustering, Disease, Age, K-Means, Ms. Excel, Orange
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknologi Informasi > S1 Sistem Informasi
Depositing User:	nata Natassa Auditasi
Date Deposited:	14 Jul 2025 05:03
Last Modified:	14 Jul 2025 05:03
URI:	https://eprints.unmer.ac.id/id/eprint/5444

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