Optimizing KNN Algorithm Using Elbow Method for Predicting Voter Participation Using Fixed Voter List Data (DPT)

Abstract

The purpose of this study is to produce maximum predictions for election participation rates. KNN (K-Nearest Neighbors) is one of the machine learning algorithms used to classify or regress data. The KNN algorithm works by finding the closest K training data from test data to be classified. Although the KNN (K-Nearest Neighbors) algorithm has advantages such as being easy to implement and being able to handle non-linear data, this algorithm also has several weaknesses, one of which is the determination of the value of K which is very ordinary and subjective. Therefore in this study optimization of the value of K on KNN using the Elbow method. The dataset used is the Fixed Voters List (DPT) in the 2019 General Elections in Karawang Regency. The final results of the experiments in this study, the highest achievement was obtained with a Mean Squared Error (MSE) value of 0.0018, a Root Mean Squared Error (RMSE) value of 0.0422, and a Mean Absolute Percent Error (MAPE) value of 6.36%. The highest accuracy produced in this study was 95.63% and the lowest was 93.64% with an average accuracy of 95.02%.