Predicting Regional Sanitation Conditions Using Support Vector Machines and Neural Networks

Abstract

Sanitation conditions are very important for an area. This is what makes an area comfortable and free from various diseases, one of which is dengue fever. This is because sanitation conditions are one of the important factors in reducing the number of dengue fever patients. This study aims to predict the level of sanitation conditions in an area using Neural Network and Support Vector Machine (SVM). This study is part of a study to determine dengue fever in the Bangka Belitung area of Indonesia. The parameters used are a learning rate of 0.1, momentum 0.1 and learning is carried out for 500 epochs. The results show that using the Neural Network method, an accuracy of 95% was obtained and the SVM method obtained an accuracy of 96%. Based on the results of the system, it can be concluded that the system has been able to run well. The contribution of this study is that it can be a Decision Support System in determining the sanitation level of an area, and related parties can immediately take follow-up action.