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Implementation off Fuzzy C-Means (FCM) and Fuzzy Possibilistic C-Means (FPCM) for Clustering District/City Based on Health Services and Infectious Diseases in North Sumatera

Year: 2025

Type: article

Abstract: The study aims to compare the Fuzzy C-Means (FCM) and Fuzzy Possibilistic C-Means (FPCM) algorithms and to profile the results of district / city clusters in North Sumatra based on health services and infectious disease sufferers. The method used is descriptive quantitative research using annual data obtained from the North Sumatra Provincial Health Office for the period 2023. The data was collected and then analyzed using both Clustering algorithms to find the most optimal results. The results showed that Fuzzy Possibilistic C-Means proved to be a better algorithm compared to Fuzzy C-Means in this study. The number of clusters formed is 3 with Cluster 3 being the highest level of health urgency in North Sumatra province. The findings of this study can help the government in equalizing the control of the number of health services and infectious disease patients.

Source: ZERO Jurnal Sains Matematika dan Terapan

DOI: 10.30829/zero.v8i2.23480

Authors: Feronika Paska Purba, Klause Roder, Elmanani Simamora, Hamidah Nasution

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4407769962

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