Pengelompokan Daerah Penyebaran Demam Berdarah Dengue Alam Dengan Menggunakan Algoritma K-Means Di Kota Makassar

Zulkifli Rais(1*), Misveria Villa Waru(2),

(1) Prodi Statistika, FMIPA, Universitas Negeri Makassar
(2) STIMIK Lamappoleonro
(*) Corresponding Author




DOI: https://doi.org/10.35580/variansiunm25657

Abstract


This study proposes the k-means method to map the endemic areas of dengue fever in the city of Makassar. Data were obtained from the health department based on the number of patients affected by dengue hemorrhagic fever (DHF) in every sub-district in Makassar City. The k-means method has mapped the area into 3 groups. These results indicate that group 1, which is the area that has the highest number of DHF sufferers, is Rappocini, Panakukang, and Manggala villages. Furthermore, Tamalate and Biringkanaya villages are members of group 2. And group 3 is an area that has a low number of dengue patients, namely Mamajang, Makassar, Tamalanrea, Mariso, Ujung Pandang, Bontoala, Tallo, Ujung Tanah, Wajo.

 

Keywords: k-means, dengue hemorrhagic fever (DHF)


Full Text:

PDF

References


A. Candra, “Demam Berdarah Dengue: Epidemiologi, patogenesis dan faktor risiko penularan,” Aspirator, vol. 2, no. 2, pp. 110-119, 2010.

U. K. Hadi, S. Soviana dan D. D. Gunandini, “Aktivitas nokturnal vektor demam berdarah dengue di beberapa daerah di Indonesia,” Jurnal Entomologi Indonesia, vol. 9, no. 1, pp. 1-6, 2012.

M. Rahayu, T. Baskoro dan B. Wahyudi, “Studi Kohort Kejadian Penyakit Demam Berdarah Dengue,” Jurnal Berita Kedokteran Masyarakat, vol. 26, no. 4, pp. 163-170, 2013.

M. R. Ridha, N. Rahayu, N. A. Rosvita dan D. E. Setyaningtyas, “Hubungan Kondisi lingkungan dan kontainer dengan keberadaan jentik nyamuk Aedes aegypti didaerah endemis demam berdarah dengue di kota Banjarbaru,” Jurnal Epidemiologi dan Penyakit Bersumber Binatang, vol. 4, no. 3, pp. 133-137, 2013.

Jonshon dan Wichern, Applied Multivariate Statistical Analysis, New Jersey: Pearson Prentice Hall, 2007.

Y. Li dan H. Wu, “A Clustering Method Based on K-Means Algorithm,” Physics Procedia, vol. 25, no. -, pp. 1104-1109, 2012.

Y. P. Pandit, Y. P. Badhe dan B. Sharma, “Classification of Indian power Coals Using K-means Clustering and Self Organizing Map neural network,” Fuel, Vol. %1 dari %2-, no. 90, pp. 339-347, 2011.

R. D. Paris, C. V. Quevedo, D. D. A. Ruiz and O. N. d. Souza, "An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features," PLOS ONE, pp. 1-25, 2015.

E. Prasetyo, Data Mining: Konsep dan Aplikasi Menggunakan Matlab, Yogyakarta: ANDI Yogyakaarta, 2012.

J. Kumar, R. T. Mills, F. M. Hoffman dan W. W. Hargrove, “Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets,” Procedia Computer Science, vol. 4, p. 1602–1611, 2011.

G. Di Fatta, F. Blasa dan S. cafiero, “Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks,” Journal of Parallel Distribution Computing, vol. 73, no. -, pp. 317-329, 2013.

P. H. Thah dan I. S. Sitanggang, “Contextual outlier detection on hotspot data in Riau Province using k-means algorithm,” Procedia Environmental Sciences, vol. 33, p. 258 – 268, 2016.

H. Rehioui, A. Idrissi, M. Abourezq and F. Zegrari, "DENCLUE-IM: A New Approach for Big Data Clustering," Procedia Computer Science, vol. 83, pp. 560-567, 2016


Article Metrics

Abstract view : 163 times | PDF view : 72 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Zulkifli Rais, Misveria Villa Waru

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstracted/Indexed by:

SINTADimensions

 

 

VARIANSI: Journal of Statistics and Its Application on Teaching and Research is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)