Perbandingan Algoritma Naive Bayes dan K-Nearest Neighbors dalam Analisis Sentimen Ulasan Aplikasi Gojek - Rousyati Rousyati; Dany Pratmanto; Fandhilah Fandhilah (METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, 2026)
Sentiment analysis of mobile app reviews helps understand public perceptions of digital service quality. This study compares two machine learning algorithms, Naive Bayes (NB) and K-Nearest Neighbors (KNN), for classifying sentiment in Gojek app reviews from the Google Play Store. The dataset includes 5,000 reviews (1-star as negative and 5-star as positive), processed through Indonesian text preprocessing steps: case folding, tokenization, stopword removal, stemming with PySastrawi, and TF-IDF feature extraction using unigrams and bigrams. After cleaning, 4,685 valid reviews remained, split into 80% training and 20% testing, producing 3,322 features. Results show that Naive Bayes (MultinomialNB, α = 1.0) outperforms KNN, achieving 89.43% accuracy, 90.09% precision, 89.43% recall, and 89.34% F1-score, with a 5-fold cross-validation score of 91.22%. Meanwhile, KNN (k = 7, cosine metric) achieves 86.77% accuracy, 86.78% precision, 86.77% recall, and 86.75% F1-score, with a cross-validation score of 87.83%. Overall, Naive Bayes proves more effective for high-dimensional Indonesian text classification using TF-IDF.