Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.