Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Naive Bayes
TL;DR
Findings confirm that Naive Bayes with comprehensive Indonesian text preprocessing is effective for automated customer satisfaction monitoring in digital electricity services.
Abstract
The rapid growth of PLN Mobile users has generated massive volumes of review data that are impractical to analyze manually, creating an urgent need for automated customer satisfaction monitoring. This study aims to develop an accurate sentiment classification model for PLN Mobile user reviews from the Google Play Store using the Naive Bayes algorithm. A dataset of 171,000 reviews was collected via web scraping, labeled into positive (rating 5) and negative (rating 1–2) classes, and balanced using an undersampling technique. Indonesian text preprocessing included text cleaning, repeated character removal, slang word normalization, negation handling, intensifier handling, stopword removal, and stemming using the Sastrawi library. Feature representation employed the Bag of Words method with CountVectorizer considering unigrams and bigrams. The Multinomial Naive Bayes model achieved an accuracy of 95.31% on the test set, with precision of 93% for the negative class and 97% for the positive class, recall of 98% for negative and 93% for positive, and an F1-score of 95% for both classes. These findings confirm that Naive Bayes with comprehensive Indonesian text preprocessing is effective for automated customer satisfaction monitoring in digital electricity services.