Perbandingan Algoritma Klasifikasi Sentimen pada Ulasan Aplikasi Mobile JKN - Maulana, Ramadhoni Gibran; Budiman, Muhammad Shidiq; Prasetyo, Tegar Hardiansyah; Karimuddin, Muhammad Fadhlan; Adriansyah, Rizqy; Hendrian, Yayan; Kinanti, Shynde Limar (Universitas Harapan Bangsa, 2025)
Sentiment classification plays an important role in evaluating public response to digital services such as BPJS Kesehatan's Mobile JKN application. This study aims to compare the performance of three machine learning algorithms-Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) for classifying user sentiment based on reviews in the Google Play Store. A total of 10,000 user reviews were collected using Python and processed on Google Colab. The research process includes text pre-processing, sentiment labeling based on ratings, data splitting, and model training. Evaluation was conducted using accuracy, precision, recall, F1 score, and confusion matrix metrics. The results show that the SVM algorithm provides the best accuracy of 90.9%, followed by Naive Bayes (90.3%) and KNN (86%). These findings prove that SVM is the most effective model for sentiment classification in the context of public services and provide important insights for government policy evaluation and digital service improvement.