CLUSTERIZATION OF DISTRICTS AND CITIES IN JAMBI PROVINCE BASED ON PUBLIC HEALTH INDICATORS USING THE K-MEANS METHOD

Health is an important foundation for Indonesia, and public health is critical to improving the quality of human resources, overcoming poverty, and supporting development. In 2022, the morbidity rate in Jambi Province reached 12.11%, higher than the previous year which was only 7.16%. The morbidity rate of the people of Jambi Province in 2022 increased by around 5% compared to last year. Based on the data on the morbidity rate of the people of Jambi Province above, it can be seen that the health condition of the people of Jambi Province has decreased compared to the previous year. By using the k-means method, this study aims to group the districts and cities of Jambi Province based on their health indicators as an effort to prevent a decline in health status in the coming years. As an effort to set priorities in improving public health in Jambi Province. The research resulted in three health clusters based on health indicator data. Cluster 1, which contains Sungai Penuh City, is at a high level, meaning that health conditions in the city are very good compared to other cities. Cluster 2, which consists of West Tanjung Jabung Regency and Jambi City, is at a medium level, meaning that health conditions in this city are quite good compared to other cities. Cluster 3 Kerinci, Merangin, Sarolangun, Batanghari, East Tanjung Jabung, Tebo, and Bungo are at the lowest level, meaning that health conditions in this cluster are poor and require more attention.

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Publication Details

Journal
Mathline Jurnal Matematika dan Pendidikan Matematika
Published
2025-06-07
DOI
https://doi.org/10.31943/mathline.v10i2.857
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

CLUSTERIZATION OF DISTRICTS AND CITIES IN JAMBI PROVINCE BASED ON PUBLIC HEALTH INDICATORS USING THE K-MEANS METHOD

Nayla Desviona, Marwah Masruroh, Rifki Chandra Utama
Mathline Jurnal Matematika dan Pendidikan Matematika
Data Mining and Machine Learning Applications
article

CLUSTERIZATION OF DISTRICTS AND CITIES IN JAMBI PROVINCE BASED ON PUBLIC HEALTH INDICATORS USING THE K-MEANS METHOD

Nayla Desviona, Marwah Masruroh, Rifki Chandra Utama
article en

Abstract

Health is an important foundation for Indonesia, and public health is critical to improving the quality of human resources, overcoming poverty, and supporting development. In 2022, the morbidity rate in Jambi Province reached 12.11%, higher than the previous year which was only 7.16%. The morbidity rate of the people of Jambi Province in 2022 increased by around 5% compared to last year. Based on the data on the morbidity rate of the people of Jambi Province above, it can be seen that the health condition of the people of Jambi Province has decreased compared to the previous year. By using the k-means method, this study aims to group the districts and cities of Jambi Province based on their health indicators as an effort to prevent a decline in health status in the coming years. As an effort to set priorities in improving public health in Jambi Province. The research resulted in three health clusters based on health indicator data. Cluster 1, which contains Sungai Penuh City, is at a high level, meaning that health conditions in the city are very good compared to other cities. Cluster 2, which consists of West Tanjung Jabung Regency and Jambi City, is at a medium level, meaning that health conditions in this city are quite good compared to other cities. Cluster 3 Kerinci, Merangin, Sarolangun, Batanghari, East Tanjung Jabung, Tebo, and Bungo are at the lowest level, meaning that health conditions in this cluster are poor and require more attention.

Mathline Jurnal Matematika dan Pendidikan MatematikaVol. 10(2)
Universitas Jambi (ID), Jakarta State Polytechnic (ID)
Openalex Percentile: Top 80%
Data Mining and Machine Learning Applications
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