Customer Review Sentiment Analysis of Alisa Batik Solo E-Commerce on TikTok Using Naive Bayes Algorithm
Abstract
This study analyzes customer sentiment toward Alisa Batik Solo’s TikTok e-commerce using the Naïve Bayes algorithm. A total of 626 customer comments were collected through manual data crawling, cleaned, labeled, and processed using text preprocessing techniques including cleaning, case folding, tokenization, stopword removal, and stemming. The processed data were then transformed using TF-IDF feature weighting and classified with Naïve Bayes to determine the polarity of customer opinions. The evaluation results showed an accuracy of 90.85%, precision of 98.29% for positive sentiment, recall of 95.24%, and an F1-score of 96.72%, indicating that the model performs effectively in classifying Indonesian short-text reviews. The findings reveal that 75.6% of the comments expressed positive sentiment, while 24.4% reflected negative opinions, demonstrating a strong level of customer satisfaction and trust in Alisa Batik Solo’s products and online engagement strategy. This research confirms that the integration of Naïve Bayes with TF-IDF preprocessing provides reliable results in social media sentiment analysis and can serve as a strategic tool for e-commerce businesses to enhance marketing decisions and service quality
Author Biographies
Informatics Engineering, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
Informatics Engineering, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
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