Sentiment Analysis of Coretax on Social Media X Using Naive Bayes, SVM, and LSTM for Service Improvement - Dermawan, Steven; Ayunda, Afifah Trista (Journal of Applied Informatics and Computing, 2025)
In January 2025, Indonesia’s Ministry of Finance launched Coretax to replace DJP Online. However, the launch triggered widespread dissatisfaction among users, reflecting negative public sentiment. This study aims to analyze public perception of Coretax and evaluate the performance of machine learning models in sentiment classification. A total of 6.036 Indonesian language tweets related to Coretax, posted between January and April 2025, were collected using Tweet Harvest. The dataset consists of 0,83% positive, 51,05% negative, and 48,11% neutral sentiments. The research methodology involved several stages: data crawling, manual labeling, preprocessing (cleaning, case folding, stopword removal, tokenization, normalization, stemming, and specifically for LSTM: conversion of tokens into numerical indices, padding, and embedding), feature representation using TF-IDF for classical models and word embedding for deep learning, data balancing with SMOTE, model implementation (Naive Bayes, Support Vector Machine with various kernels, and LSTM), model evaluation and comparison, and visualization through word clouds. The application of SMOTE succeeded in improving the performance of all algorithms. After applying SMOTE, the SVM with the RBF kernel achieved the best performance with 90,70% accuracy, 91% precision, 90,66% recall, and 90,66% F1-score. Keyword analysis revealed that terms such as “data” and “mudah” dominated positive sentiment, “silakan” and “kakak” were prevalent in neutral sentiment, while “sistem” and “error” frequently appeared in negative sentiment. The findings highlight the urgent need for system infrastructure improvements, user-centered features, responsive technical support, taxpayer training, and continuous updates to enhance Coretax and restore public trust.