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All Journal The Indonesian Journal of Computer Science
Barirotut Taqiyyah
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Sentiment Classification of Instagram Poster Comments on the Documentary Film "Pesta Babi": A Comparative Study of SVM, Random Forest, and Naive Bayes with a Keyword-Based Exploration of Sociocultural Themes Barirotut Taqiyyah; Julianti Damiar Rakhmawati damiar; Aurallia Titania Agnes Syafa’i; Faisal Fahmi
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i4.5213

Abstract

This study analyzes public sentiment in 704 Instagram comments responding to the poster of the Indonesian documentary "Pesta Babi: Kolonialisme di Zaman Kita," which triggered national debate in 2026. Comments were collected through total sampling and sentiment-labeled with AI assistance (partially via an Excel-integrated AI tool, partially via an AI-generated Python script executed in PyCharm), yielding an imbalanced distribution of 514 Positive and 190 Negative comments. After text preprocessing (regex-based tokenization and lowercasing) and binary Bag-of-Words feature extraction with L1 regularization, three algorithms Support Vector Machine, Random Forest, and Naive Bayes were compared using 10-fold cross-validation on the Orange platform. Random Forest achieved the highest overall accuracy (0.786) and AUC (0.858), while Naive Bayes showed the strongest balanced performance on the minority Negative class (MCC = 0.398). A supplementary keyword-based analysis found only 19.7% of comments referenced religious, artistic, or ethical-normative themes, indicating public discourse was dominated by other issues, notably political and funding-related accusations.
Co-Authors Aurallia Titania Agnes Syafa’i Fahmi, Faisal Julianti Damiar Rakhmawati damiar