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All Journal Smart Techno (Smart Technology, Informatic and Technopreneurship)
Gimnastiar, Rayhan
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Computer Science & IT

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Comparative Analysis of Naïve Bayes and SMOTE-Based Long Short-Term Memory (LSTM) for Electric Vehicle Sentiment Analysis on YouTube Gimnastiar, Rayhan; Lestari, Fania Indah; Prasetyo, Nadhif Daniswara; Prasetyo, Eko; Setyatama, Fardanto
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.230

Abstract

The transition to electric vehicles in Indonesia has generated diverse public opinions on social media. Most previous sentiment analysis studies have tended to employ a single classification method without in-depth comparison and have overlooked the issue of extreme data imbalance, which can introduce bias into classification models. Furthermore, a research gap remains regarding the effectiveness of complex deep learning models compared with simpler statistical models when applied to medium-dimensional Indonesian-language opinion texts. This study aims to address this gap by conducting a comparative evaluation of model performance. The dataset consists of 1,512 unstructured public opinion comments collected from YouTube, distributed across three sentiment classes: 868 negative, 503 positive, and 141 neutral comments. To address the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The study then compares the statistical Naïve Bayes method using TF-IDF weighting with the Deep Learning Long Short-Term Memory (LSTM) method using Word Embedding. Evaluation using a Confusion Matrix demonstrates that Naïve Bayes outperformed LSTM and exhibited greater stability in sentiment classification for this dataset, achieving an accuracy of 63.04%, compared with 57.43% for the LSTM model.
Co-Authors Eko Prasetyo Lestari, Fania Indah Prasetyo, Nadhif Daniswara Setyatama, Fardanto