Utilization of Rapidminer using the K-Means Clustering Algorithm for Classification of Dengue Hemorrhagic Fever (DHF) Spread in Banda Aceh City - Sanusi; Juniana Husna (LPPM Politeknik Aceh Selatan, 2020)
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.