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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
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Sentiment Analysis of the Merah Putih Movie Using Naïve Bayes and Support Vector Machine Sancoko, Sulistyo Dwi; Nafiah, Ulfah; Manda, Yudit; Mukti, Novera Sari
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3823

Abstract

Public engagement on YouTube provides a valuable source for examining audience responses to film productions; however, sentiment classification of Indonesian-language comments remains methodologically challenging due to informal expressions, noisy text, and imbalanced class distributions. This study evaluates the robustness of a classical machine learning pipeline for sentiment classification of YouTube comments on the trailer of the Indonesian animated film Merah Putih: One for All. A total of 5,469 comments were collected using the YouTube Data API v3. After preprocessing and lexicon-based pseudo-labeling, 5,192 comments were retained, consisting of 4,006 negative and 1,186 positive instances. Text features were represented using TF-IDF, while SMOTE was applied only to the training set after a stratified 80:20 split to prevent data leakage. Two classifiers were compared under identical experimental conditions: Multinomial Naïve Bayes and linear Support Vector Machine. The SVM model achieved 81.59% accuracy, 83% precision, 82% recall, and 82% F1-score on the original held-out test set, outperforming Naïve Bayes, which obtained 76.82% accuracy. The findings suggest that margin-based classification is more suitable than probabilistic classification for sparse, high-dimensional Indonesian YouTube comments, particularly when feature independence assumptions are likely violated. The study contributes a leakage-controlled evaluation of classical sentiment classification under imbalanced social-media conditions and highlights the methodological implications of pseudo-labeling and synthetic oversampling in Indonesian film-related opinion mining.
Text-Based Sentiment Analysis of Online Reviews: Evidence from Indonesia’s Muslim Women’s Fashion Sector Nurcahyanie, Yunia Dwie; Saraswati, Sabrina Nur
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia’s Muslim women’s fashion market has expanded rapidly alongside e-commerce growth, generating massive volumes of online product reviews (OPRs) that remain underutilized for systematic product development. This study addresses a gap in the literature: while sentiment analysis can classify review polarity, term-level classification alone cannot translate consumer feedback into actionable design attributes for fashion products, a domain where tacit knowledge, material properties, and aesthetic judgment are central. A two-layer hybrid approach is proposed that combines computational sentiment extraction with expert semantic translation. In the first layer, 2,050 OPRs from three Indonesian Muslim fashion brands on Shopee were preprocessed and classified using a maximum entropy (MaxEnt) model, achieving 84.11% accuracy, 90.09% precision, and an F1 score of 89.95% on test data. In the second layer, ten experienced designers interpreted the MaxEnt output through structured interviews, translating raw sentiment features into design-relevant categories. Positive sentiment features clustered around product quality, material comfort, and design authenticity, while negative features concentrated on product-image discrepancies, poor fabric quality, sizing mismatches, and color inaccuracy. Designer interpretation uncovered semantic dimensions invisible to the classifier, yielding eight major product performance categories. This study contributes methodologically by demonstrating the necessity of a human-in-the-loop expert validation layer for sentiment-based consumer insight extraction in design-intensive domains, and practically by providing a framework for converting OPR data into product development inputs.

Page 65 of 65 | Total Record : 642

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