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m azwan
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Computer Science & IT

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Penerapan Algoritma K-Means Clustering dan Correlation Matrix Untuk Menganalisis Risiko Penyebaran Demam Berdarah di Kota Pekanbaru m azwan; Rahmad Kurniawan; Pizaini Pizaini; Fitri Insani
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 6, No 3 (2021): DESEMBER
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37438/jimp.v6i3.353

Abstract

Dengue cases in Pekanbaru in November 2020 reached 2,788 cases and 33 deaths. The government has carried out socialization to eradicate mosquito nests and provided vector control tools and materials. However, the government's efforts were not practical because the applied method has not been able to refer to vector data and information. Machine learning can be used to analyze specific problems such as Dengue. Therefore this study employed a Machine Learning algorithm, i.e., k-means clustering and correlation matrix for dengue risk analysis in Pekanbaru. This study obtained 12 sub-districts and 50 dengue attributes and weather data in 2020. K-means automatically searches for unknown clusters from dengue cases data quickly, which cluster results C1 (Sukajadi, Senapelan), C2 (Tenayan Raya, Tampan), and C3 (Rumbai Pesisir, Rumbai). Based on experimental testing, this study produced a silhouette score is 0.6. Meanwhile, the correlation matrix looks for relevant relationships hidden in the data. The correlation matrix obtained a strong linear relationship between the population (JP) and sufferers (P) of 0.73 for January and 0.93 for February 2020.Keywords— Dengue Fever, K-means, Correlation matrix, Machine learning.
Co-Authors Fitri Insani Pizaini Pizaini Rahmad Kurniawan