Implementasi Data Mining Dalam Proses Analisa Sentimen Penggunaan Sunan (Sinau Temenanan) E-Learning UMK Sebagai Media Pembelajaran Menggunakan Metode Naïve Bayes Classifier

Abstract

The advancement of information technology has driven the adoption of e-learning platforms, including Sunan (Sinau Temenanan) at Universitas Muria Kudus (UMK). This study aims to analyze students' perceptions and satisfaction with the Sunan platform through sentiment analysis. A total of 300 questionnaire responses were collected, with sentiments categorized into 151 negative and 148 positive. Data Mining techniques, specifically the Naïve Bayes Classifier algorithm, were utilized for sentiment classification. The research process included data collection, preprocessing (case folding, tokenizing, filtering, and stemming), transformation using the TF-IDF method, and model evaluation. The evaluation results demonstrated an accuracy of 88.24%, with precision, recall, and F1-score of 83.33%, 83.33%, and 85.51%, respectively. These findings highlight the algorithm's effectiveness in sentiment analysis and provide valuable insights for improving the Sunan platform to enhance user experience and better meet student needs.

Keywords: Data Mining, Sentiment Analysis, E-Learning, Naïve Bayes.