Utilization of Rapidminer using the K-Means Clustering Algorithm for Classification of Dengue Hemorrhagic Fever (DHF) Spread in Banda Aceh City
Abstract
Dengue Hemorrhagic Fever (DHF) is still a serious problem in Banda Aceh City. The grouping of dengue disease distribution areas can use data mining techniques through the K-Means Clustering Algorithm by involving several factors that influence it such as population density, rainfall, air humidity and temperature. The purpose of this study is to try to create a distribution cluster group that is included in the high category (C1), medium (C2), and low (C3) in 9 sub-districts in the city of Banda Aceh. The data used in this study are secondary data during the period 2010 to 2017, which includes population density data obtained from the Banda Aceh City BPS office, rainfall, humidity, temperature were obtained from the BMKG Indrapuri Aceh Besar office and data on dengue cases were obtained from the Banda Aceh City Health Office. The results showed that up to 4 iterations of K-Means Clustering was good enough for the classification of dengue case data. The high cluster group (C1) is Baiturrahman, Kuta Alam and Syiah Kuala sub-districts, the medium cluster group (C2) is Jaya Baru, Banda Raya and Ulee Kareng sub-districts, then the low cluster group (C3) is Meuraxa and Kuta Raja.
Downloads
References
Nugroho, GS., Nugroho, D., Hasbi, M. 2013. Geographic Information System Penyebaran DBD Berbasis Web di Wilayah Kota Solo, ISSN: 2338-4018.
Wahyuningsih, NE., Suhartono., Sufia. 2014. Hubungan Kondisi Lingkungan Rumah dan Perilaku Keluarga dengan Kejadian Demam Berdarah Dengue Di Kabupaten Aceh Besar. Jurnal Kesehatan Lingkungan Indonesia Vol. 13 No. 1
Nainggolan, F. 2007. Epidemiology and Clinical Pathogenesis of Dengue in Indonesia; presented at Seminar on Management of Dengue Outbreaks; University of Indonesia; Jakarta; November 22
Depkes – Departemen Kesehatan, Republic of Indonesia. 2007. CDC and EH Yearly Report; Jakarta
InfoDatin. 2018. Pusat Data dan Informasi Kementerian Kesehatan Republik Indonesia. April 22. ISSN. 2442-7659
Elvin, SD., Mulyadi., Kamil, H. 2016. Tugas Kesehatan Keluarga Dalam Pencegahan Demam Berdarah Dengue Dengan Pendekatan Health Belief Model The Family Health Task In Prevention Of Dengue Hemorrhagic Fever With Health Belief Model Approach
Neha., Chaudhary, N., Singh, T. 2014. A Comprehensive Review on k-Means Clustering Algorithm in Neural Networks. International Journal of electronIcs & communication technology (IJECT) Vol. 5, issue 3 spl - 1, ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print).
Kou, G., Peng, Y., Wang, G. 2014. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences. 275: p. 1-12.
Bamer, M. 2007. Principles of Data Mining. ISSN 1863-7310. Springer-Verlag London Limited 2007.
Kaur N, Sahiwal KJ, kaur N. 2012. Efficient K-Means Clustering Algorithm Using Ranking method in Data Mining. International Journal of Advanced Research in Computer Engineering & Technology. Volume 1, Issue 3. ISSN: 2278-1323
Li, Y., Wu, H. 2012. A Clustering Method Based on K-Means Algorithm. International Conference on Solid State Devices and Materials Science. Sciverse ScienceDirect. Physics Procedia 25. 1104 – 1109.
