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Abstract
In Indonesia, infectious diseases are still a persistent problem. Experts have accumulated knowledge regarding the emergence of the disease. In the last ten years, Indonesia is still experiencing the problem of triple burden diseases. Where Indonesia is still hit by infectious diseases, non-communicable diseases (NCDs) and diseases that should have been resolved, apart from that, infectious diseases are also still a big problem that must be faced. Researchers are interested in conducting research on infectious diseases in Central Bengkulu Regency. When analyzing infectious diseases, grouping can be done. Cluster analysis is an approach to looking for similarities in data and placing similar data into groups. There are two grouping methods in cluster analysis, hierarchical methods and non-hierarchical methods. One of the cluster analyzes using hierarchical methods is the average linkage method, while non-hierarchical ones are K-Means and K-Medoids. The variables used in this research are TBC and DHF in 2022. The highest rates of TB and DHF occurred in Pondok Kelapa sub-district, namely 29 and 23 cases. Based on the results of the analysis, it consists of 2 clusters, with cluster 1 consisting of 9 sub-districts, while cluster 2 consists of 2 sub-districts. Based on the results of evaluating the best method using the Calinski-Harabasz Index, it was found that the K-medoids method was the best method with a value of 0.
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Copyright (c) 2025 Desi T. Tasti, Fridz M. Gumay, Ulfianida Aysha, Winalia Agwil, Wingke Y. Pratami
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
N. Afira dan A. W. Wijayanto, “Analisis Cluster Kemiskinan Provinsi di Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarki”, Jurnal Sistem Komputer, Vol. 10, No. 2, 101-109, 2021
Anonim. K-means Clustering. Universitas Raharja. 2020
Irwan, Epidemiologi Penyakit Menular, Yogyakarta, CV. Absolute Media, 2017
J. Han & M. Kamber, Data Mining: Concepts and Techniques, Second Edition, San Francisco, Elsevier Inc, 2006
J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, San Francisco, Morgan Kaufmann Publishers, 2012
E. Luthfi dan A. W. Wijayanto, 2021, “Analisis Perbandingan Metode Hirearchical, K-means, Dan K-medoids Clustering Dalam Pengelompokkan Indeks Pembangunan Manusia Indonesia”, INOVASI: Jurnal Ekonomi, Keuangan dan Manajemen, Vol 17, No 4, 2021
Q. Nafisah dan N. E. Chandra, “Analisis Cluster Average linkage Berdasarkan Faktor-Faktor Kemiskinan di Provinsi Jawa Timur”, Zeta – Math Journal, Vol 3, No 2, 2017
M. Paramadina, Sudarmin, dan M. K Aidid, 2019, “Perbandingan Analisis Cluster Metode Average linkage dan Metode Ward (Kasus: IPM Provinsi Sulawesi Selatan)”, Journal of Statistics and Its Application on Teaching and Research, Vol. 1, No.2, 22-23, 2019
D. N. P. Sari, Analisis Cluster Dengan Metode K-means Pada Persebaran Kasus Covid-19 Berdasarkan Provinsi Di Indonesia, Semarang, Universitas Semarang, 2020
N. Ulinnuh dan R. Veriani, “Analisis Cluster dalam Pengelompokan Provinsi di Indonesia Berdasarkan Variabel Penyakit Menular Menggunakan Metode Complete Linkage, Average linkage dan Ward”, Jurnal Nasional Informatika dan Teknologi Jaringan, Vol. 5, No.1, 2020
R. Veriani, Analisis Cluster Dalam Pengelompokan Provinsi Di Indonesia Berdasarkan Variable Penyakit Menular Menggunakan Metode Complete Linkage, Average linkage Dan Ward, Surabaya, Universitas Islam Negri Sunan Ampel, 2020