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All Journal Jurnal Geografi
Nur Mohammad Farda
Program Studi Magister Penginderaan Jauh, Departemen Sains Informasi Geografi, Fakultas Geografi, Universitas Gadjah Mada
Earth & Planetary Sciences Education Environmental Science Social Sciences Other

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ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS Rahmat Azul Mizan; Prima Widayani; Nur Mohammad Farda
JURNAL GEOGRAFI Vol 13, No 2 (2021): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v13i2.21019

Abstract

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image
Co-Authors Prima Widayani Rahmat Azul Mizan