Comparison Analysis of Hierarchical Clustering and K-Means Methods in Grouping Provinces in Indonesia Based on Dengue Hemorrhagic Fever (DHF) Cases

Alfidha Rahmah; Nida Faoziatun Khusna; Safril Ahmadi Sanmas; Syifa Aulia; Shinta Amaria; Fatkhurokhman Fauzi

doi:10.30595/juita.v13i2.26131

Authors

Alfidha Rahmah Universitas Muhammadiyah Semarang
Nida Faoziatun Khusna Universitas Muhammadiyah Semarang
Safril Ahmadi Sanmas Universitas Muhammadiyah Semarang
Syifa Aulia Universitas Muhammdiyah Semarang
Shinta Amaria Universitas Muhammadiyah Semarang
Fatkhurokhman Fauzi Universitas Muhammadiyah Semarang

DOI:

https://doi.org/10.30595/juita.v13i2.26131

Keywords:

Clustering, DHF, Hierarchical Clustering, K-Means

Abstract

Indonesia, a tropical country, experiences climate variations that influence the spread of infectious diseases, including Dengue Hemorrhagic Fever (DHF). The increase in DHF cases necessitates clustering provinces based on their vulnerability to design effective mitigation strategies. This study compares two clustering methods: Hierarchical Clustering and K-Means Clustering. Within the hierarchical clustering analysis, five linkage methods were evaluated: Average Linkage, Complete Linkage, Single Linkage, Ward’s Method, and Centroid Linkage. The best linkage method was identified using the cophenetic correlation coefficient, indicating that Average Linkage produced the most representative cluster structure, resulting in three distinct groups. For the K-Means method, the optimal number of clusters was determined using the Silhouette Coefficient, which also indicated three clusters. Clustering performance evaluation revealed that Average Linkage outperformed K-Means, with a higher Silhouette Score of 0.552. The resulting clusters categorized provinces into three risk groups: high-risk areas (e.g., DKI Jakarta), moderate-risk areas (e.g., West Java and East Java), and low-risk areas, comprising the remaining provinces in Indonesia

References

[1] M. Kanan, M. Naffaa, A. Alanazi, F. Nasser, A. A. Alsaiari, M. Almehmadi, A. Assiry, H. Muzafar, H. Katam, A. Arar, S. M. B. Asdaq, Abida, M. Imran, and T. Dzinamarira, “Genetic variants associated with dengue hemorrhagic fever. A systematic review and meta-analysis,” J Infect Public Health, vol. 17, no. 4, pp. 579–587, 2024, doi: 10.1016/j.jiph.2024.02.001.

[2] World Health Organization (WHO), “Dengue and Severe Dengue.” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue

[3] R. Pakaya, D. Daniel, P. Widayani, and A. Utarini, “Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review,” BMC Public Health, vol. 23, no. 1, pp. 1–16, 2023, doi: 10.1186/s12889-023-17185-3.

[4] N. Shahid, “Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance,” Sci Rep, vol. 13, no. 1, pp. 1–11, 2023, doi: 10.1038/s41598-023-32790-3.

[5] L. C. R. Antunes, A Comparison Between Hierarchical and Non-Hierarchical Software Clustering, vol. 1, no. 1. Association for Computing Machinery.

[6] E. Balbi, P. Cianfarra, L. Crispini, S. Tosi, and G. Ferretti, “Hierarchical-agglomerative clustering analysis of geomorphic features applied to tectonic investigation of terrestrial planets: An example from Claritas Fossae, Mars,” Icarus, vol. 420, no. June, p. 116197, 2024, doi: 10.1016/j.icarus.2024.116197.

[7] M. Nazari, A. Hussain, and P. Musilek, “Applications of Clustering Methods for Different Aspects of Electric Vehicles,” Electronics (Basel), vol. 12, no. 4, p. 790, Feb. 2023, doi: 10.3390/electronics12040790.

[8] V. Michalakopoulos, E. Sarmas, I. Papias, P. Skaloumpakas, V. Marinakis, and H. Doukas, “A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs,” Appl Energy, vol. 361, p. 122943, May 2024, doi: 10.1016/j.apenergy.2024.122943.

[9] J. Wijaya and M. S. Manurung, “Regency / City Clustering in North Sumatra Based on Food Crop Productivity,” Journal of Analytical Research, Statistics and Computation, vol. 2, no. 2, pp. 36–51, 2023.

[10] A. M. Sroyer, S. A. Mandowen, and F. Reba, “Analisis Cluster Penyakit Malaria Provinsi Papua Menggunakan Metode Single Linkage Dan K-Means,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 7, no. 3, pp. 147–154, Jan. 2022, doi: 10.25077/TEKNOSI.v7i3.2021.147-154.

[11] I. Indra, N. Nur, Muh. Iqram, and N. Inayah, “Perbandingan K-Means dan Hierarchical Clustering dalam Pengelompokan Daerah Beresiko Stunting,” INOVTEK Polbeng - Seri Informatika, vol. 8, no. 2, p. 356, Nov. 2023, doi: 10.35314/isi.v8i2.3612.

[12] A. Mahmudan, “Clustering of District or City in Central Java Based COVID-19 Case Using K-Means Clustering,” Jurnal Matematika, Statistika dan Komputasi, vol. 17, no. 1, pp. 1–13, Aug. 2020, doi: 10.20956/jmsk.v17i1.10727.

[13] C. Wongoutong, “The impact of neglecting feature scaling in k-means clustering,” PLoS One, vol. 19, no. 12, p. e0310839, Dec. 2024, doi: 10.1371/journal.pone.0310839.

[14] T. Zhu and Y. Han, “Enhancing Beer Recommendations through Clustering: A Comparison of Hierarchical and K-means Clustering Methods on Normalized Data,” in Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China, EAI, 2023. doi: 10.4108/eai.26-5-2023.2334332.

[15] Iis, I. Yahya, Gusti Ngurah A. Wibawa, Baharuddin, Ruslan, and L. Laome, “Penggunaan Korelasi Cophenetic Untuk Pemilihan Metode Cluster Berhierarki Pada Mengelompokkan Kabupaten/Kota Berdasarkan Jenis Penyakit di Provinsi Sulawesi Tenggara Tahun 2020,” Prosiding Seminar Nasional Sains Dan Terapan (Sinta), no. April, pp. 1–16, 2022.

[16] K. Pratama Simanjuntak and U. Khaira, “Hotspot Clustering in Jambi Province Using Agglomerative Hierarchical Clustering Algorithm,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 1, no. 1, pp. 7–16, 2021.

[17] I. F. Ashari, E. Dwi Nugroho, R. Baraku, I. Novri Yanda, and R. Liwardana, “Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta,” Journal of Applied Informatics and Computing, vol. 7, no. 1, pp. 89–97, 2023, doi: 10.30871/jaic.v7i1.4947.

[18] Y. S. Amelinda, R. A. Wulandari, and A. Asyary, “The effects of climate factors, population density, and vector density on the incidence of dengue hemorrhagic fever in South Jakarta Administrative City 2016-2020: an ecological study,” Acta Biomedica, vol. 93, no. 6, 2022, doi: 10.23750/abm.v93i6.13503.

[19] S. Istiqamah, A. Arsin, and S. Sirajuddin, “Average rainfall effect on dengue hemorrhagic fever in Kendari City, Indonesia in 2014-2018,” EAS Journal of Parasitology and Infectious Diseases, vol. 1, no. 5, pp. 98–102, 2019, doi: 10.36349/EASJPID.2019.v01i05.003.

[20] A. Azzahra and A. W. Wijayanto, “Comparison of Agglomerative Hierarchical and K-Means in Grouping Provinces Based on Maternal Health Services,” SISTEMASI, vol. 11, no. 2, p. 481, May 2022, doi: 10.32520/stmsi.v11i2.1829.