Optimized Indonesia Language Preprocessing Framework for Gojek Reviews Sentiment Analysis Using Linear Support Vector Machine - Jahda Rusti Putri; Hendra Wijaya; Ali Ibrahim; Ahmad Heryanto (Indonesian Journal of Artificial Intelligence and Data Mining, 2026)
This study analyzes sentiment in Gojek application user reviews using Natural Language Processing (NLP) and machine learning techniques to classify sentiments into positive, negative, and neutral categories. A dataset of 8,091 Bahasa Indonesia reviews from Kaggle (Gojek version 4.8) was processed, yielding 5,685 valid instances after cleaning, with sentiment distribution of neutral (52.7%), positive (26.8%), and negative (20.3%). An optimized Indonesian preprocessing pipeline was developed, incorporating text cleaning, slang normalization using a curated 1,247-word mapping dictionary, tokenization, stemming via Sastrawi, and stopword removal. Feature extraction employed TF-IDF Vectorizer (max_features=10,000; n-gram=(1,2)). Four algorithms, Naïve Bayes, Linear SVM, Logistic Regression, and Random Forest (tuned) were evaluated using stratified 80:20 split. Linear SVM and Random Forest achieved the highest accuracy at 93% (weighted F1-score: 93%), followed by Logistic Regression (92%) and Naïve Bayes (68%). Ablation study confirmed that slang normalization contributed the greatest performance gain (4.8%). Keyword-based aspect extraction on negative reviews identified three priority improvement areas: customer service responsiveness (38%), pricing transparency (32%), and application stability (24%).