NLP-Based Analysis of Indonesian Public Opinion on the Nadiem Makarim Chromebook Case Using Instagram and YouTube Comments
DOI:
https://doi.org/10.57152/ijatis.v3i2.3099Keywords:
Chromebook Procurement, Emoji Tokenization, Indonesian NLP, Logistic Regression, Public OpinionAbstract
This study examines Indonesian public sentiment toward the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek)'s Chromebook procurement under Nadiem Makarim and evaluates emoji tokenization in text classification. Between 2021 and 2024, the initiative sparked debate over device specifications, internet connectivity in frontier, outermost, and underdeveloped (3T) regions, and budget accountability. A balanced dataset of 4,794 comments (2,397 from Instagram and 2,397 from YouTube) was analyzed after removing duplicates. Sentiment pseudo-labels (negative, positive, and neutral) were generated using Indonesian RoBERTa fine-tuned on the SmSA IndoNLU benchmark. Two preprocessing scenarios were compared: removing emojis and emoticons (E1) and converting them into descriptive tokens (E2). Unigram and bigram TF-IDF features were classified using class-weighted Multinomial Logistic Regression within a leakage-free pipeline. Negative sentiment dominated (76.93%; 3,688 comments), followed by positive (17.86%; 856 comments) and neutral (5.21%; 250 comments). In the 80:20 holdout evaluation, E2 achieved 0.8352 accuracy and 0.6722 macro F1, while E1 achieved 0.8321 accuracy and 0.6725 macro F1. Five-fold cross-validation showed modest improvements with E2 in mean accuracy (0.8204 vs. 0.8169) and macro F1 (0.6433 vs. 0.6333). Performance was higher on Instagram than on YouTube. Overall, findings indicate predominantly negative public reception and modest benefits from emoji tokenization.
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