IMPLEMENTATION OF THE LOCAL OUTLIER FACTOR MODEL FOR ANOMALY DETECTION IN KPU SUKABUMI REGENCY VOTER DATA

General elections are one of the most significant political activities in the life of a nation, necessitating accurate and reliable voter data. Inaccurate or unreliable voter data can lead to various issues, such as electoral fraud. One primary cause of inaccurate voter data is the presence of anomalies, which are data points that do not match the actual conditions. Anomalies in voter data can arise from several factors, including data entry errors, fraud, or system faults. To detect anomalies in voter data, various methods can be employed, with the Local Outlier Factor (LOF) method being one notable example. LOF is an unsupervised learning method in machine learning that identifies anomalies by measuring the distance between data points and their nearest neighbors. This study aims to implement the LOF method to detect anomalies in the voter data of the Sukabumi Regency Election Commission. The voter data used in this research was obtained from the Sukabumi Regency Election Commission for the year 2024.