Youtube Comment Sentiment Classification System With Naive Bayes TF-IDF Using Laravel IDX - normawati, dwi; Rendy Saputra; Hendrik Fery Herdiatmoko (Universitas Ahmad Dahlan, 2025)
The development of technology has enhanced social interactions through social media platforms like YouTube, making user comments a vital data source for sentiment analysis. One emerging issue is the lack of understanding regarding consumer perceptions of smartphone brands in Indonesia, which can be explored further through YouTube comments. This study aims to build a sentiment classification system for YouTube comments related to smartphone brands in Indonesia in 2024 using the Naïve Bayes Classifier algorithm with TF-IDF weighting and FastText features. Data was collected using the YouTube Data API, followed by preprocessing, labeling, and feature extraction stages. The model was optimized through GridSearchCV and evaluated with a Confusion Matrix, achieving an accuracy of up to 97%. The system was implemented as a Laravel-based web application, providing an interface for dataset management, model training, and sentiment visualization. This research also includes the integration of IDX Projects with Laravel, enabling more efficient data management and interactive presentation of sentiment analysis results. The findings demonstrate the effectiveness of this method in classifying positive and negative sentiments, which can help users understand consumer preferences for various smartphone brands.