Kajian Perbandingan Algoritma KNN Dan SVM Untuk Prediksi Pengangguran Di Provinsi Lampung

Abstract

Unemployment is a complex social and economic problem in Indonesia, including in Lampung Province, which has a negative impact on poverty and economic imbalance. Indonesia has the second highest unemployment rate in Southeast Asia. Lampung Province also faces unemployment challenges with Bandar Lampung City having the highest in the province. Identifying the factors that influence unemployment and developing an effective classification method is essential. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) algorithms in classifying unemployment in Lampung Province. This research contributes to a better understanding of the factors that influence the unemployment rate and provides recommendations about the most effective algorithms. This study uses modeling and data analysis techniques that are commonly used in various disciplines to overcome these problems. The KNN algorithm classifies based on similarity to nearest neighbors, while SVM uses a kernel-based or linear separation approach. The findings of this study will enhance our understanding of the factors of unemployment and support decision making in addressing the problem.

Keywords: Unemployment, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Classification, Lampung Province.