Classification of Sentiment in Reddit Forum Comments Using Fine-Tuned IndoBERT (Case Study: Free Nutritious Meal Program)
Klasifikasi Sentimen Komentar Forum Reddit Menggunakan Fine Tuned IndoBERT (Studi Kasus : Program Makan Bergizi Gratis)
DOI:
https://doi.org/10.21070/ups.10480Keywords:
Analysis Sentiment, Natural Language Processing, Deep Learning, Reddit, IndoBERTAbstract
This study analyzes Reddit public opinion on Indonesia's Free Nutritious Meals Program (MBG) using a fine-tuned IndoBERT model on 6,295 comments from 2024–2025. Preprocessing included normalizing colloquial language, converting emojis, and translating to Indonesian. A balanced 3,000-comment dataset was manually annotated (positive, negative, neutral) and split 80:20 for training/testing. The model achieved 99.00% accuracy, F1-score, and precision. On the full dataset, sentiments were mostly neutral (59.44%), followed by positive (34.11%) and negative (6.45%), with a mean confidence of 0.8502 and throughput of 137.93 samples/second ideal for real-time policy monitoring.
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References
B. Auxier and M. Anderson, "Social Media Use in 2021," Pew Research Center, 2021. [Online]. Available: www.pewresearch.org.
K. Pham, K. C. Rao Kathala, and S. Palakurthi, "Reddit Sentiment Analysis on the Impact of AI Using VADER, TextBlob, and BERT," Procedia Computer Science, vol. 258, pp. 85-94, 2025.
A. Kiftiyah et al., "Program Makan Bergizi Gratis (MBG) dalam Perspektif Keadilan Sosial dan Dinamika Sosial-Politik," Pancasila: Jurnal Keindonesiaan, vol. 5, no. 1, pp. 45-62, 2025.
R. A. Munir, "Analisis Sentimen Cuitan di Media Sosial X tentang Program Makan Bergizi Gratis dengan Metode NLP," Jurnal Informatika Dan Teknik Elektro Terapan, vol. 13, no. 1, pp. 123-135, 2025.
V. Agustina, A. Herliana, and E. P. Korespondensi, "Analisis Sentimen Publik atas Kebijakan Efisiensi Anggaran 2025 dengan Text Mining dan Natural Language Processing," Jurnal Sistem Informasi dan Teknologi, vol. 7, no. 2, pp. 78-89, 2025.
B. Boe, "PRAW: The Python Reddit API Wrapper," GitHub Documentation, 2023. [Online]. Available: https://praw.readthedocs.io/en/stable/index.html
M. Rayhan Nur, Y. Wibisono, and R. Megasari, "Analisis Sentimen dan Pemodelan Topik pada Post tentang Merek Teknologi di X Menggunakan Fine-tuning IndoBERT dan BERTopic," Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI), vol. 4, no. 2, pp. 45-58, 2025.
Babanejad, N., Agrawal, A., An, A., & Papagelis, M. (2020). A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks. https://github.com/NastaranBa/
M. Faza and M. Taufik, "Penerapan Model IndoBERT Untuk Deteksi Potensi Sumber Stres Dalam Teks Media," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 3, pp. 567-578, 2025.
A. Kunaefi, Z. Abidin, and R. Kusumawati, "Klasifikasi Berita Hoaks Bahasa Indonesia Menggunakan IndoBERT Fine-Tuning Dengan Pendekatan Focal Loss pada Data Tidak Seimbang," JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), vol. 10, no. 2, pp. 89-102, 2025.
D. Prasetia et al., "Analisis Sentimen Pengguna Aplikasi MyBluebird Dengan Algoritma Naïve Bayes Di Playstore," Jurnal Informatika Dan Teknik Elektro Terapan, vol. 13, no. 2, pp. 145-156, 2025.
N. Proferes et al., "Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics," Social Media and Society, vol. 7, no. 2, pp. 1-14, 2021.
W. Wang et al., "IndoBERT: Pre-trained Model for Bahasa Indonesia," IndoNLP Research Group, 2020. [Online]. Available: https://huggingface.co/indobenchmark
Z. Liu et al., "Improving Sentiment Analysis Accuracy with Emoji Embedding," Journal of Safety Science and Resilience, vol. 2, no. 4, pp. 289-298, 2021.
M. Arif Afandi and I. Suri, "Effectiveness of Government Communication in Delivering Public Policy on Social Media," Jurnal Komunikasi Pemerintah, vol. 9, no. 1, pp. 23-38, 2025.
N. Ratnaswari, N. C. Wibowo, and D. S. Y. Kartika, "Analisis Sentimen Menggunakan Metode Lexicon-Based Dan Support Vector Machine pada Presiden dan Wakil Presiden Indonesia Periode 2024–2029," Jurnal Informatika Dan Teknik Elektro Terapan, vol. 13, no. 1, pp. 67-78, 2025.
S. S. Berutu et al., "Data preprocessing approach for machine learning-based sentiment classification," JURNAL INFOTEL, vol. 15, no. 4, pp. 317-325, 2023.
H. T. Duong and T. A. Nguyen-Thi, "Preprocessing techniques and data augmentation for sentiment analysis," Computational Social Networks, vol. 8, no. 1, pp. 1-15, 2021.
R. Srinivasan and C. N. Subalalitha, "Sentimental analysis from imbalanced code-mixed data using machine learning approaches," Distributed and Parallel Databases, vol. 41, no. 1-2, pp. 37-52, 2023.
A. Ari Putra Wibowo and Nurul Hidayat, "Eksplorasi Linguistik Komputasional dalam Analisis Bahasa Alami untuk Mengungkap Evolusi Dialek Digital di Era Media Sosial Global," Journal of New Trends in Sciences, vol. 1, no. 3, pp. 45-52, 2023.
Nurjoko and A. Rahardi, "Model Indo-BERT untuk Identifikasi Sentimen Kekerasan Verbal di Twitter," IJCCS (Indonesian Journal of Computer and Cybernetics), vol. 18, no. 2, pp. 156-168, 2024.
F. A. Wagay and Jahiruddin, "Classification of Mental Illnesses from Reddit Posts Using Sentence-BERT Embeddings and Neural Networks," Procedia Computer Science, vol. 258, pp. 234-243, 2025.
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