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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Eduvest - Journal of Universal Studies
Prabu Kresna Putra
National Research and Innovation Agency, Indonesia
Aerospace Engineering Computer Science & IT Health Professions Neuroscience Social Sciences

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Sentiment Analysis and Topic Modeling of Public Opinion on Indonesia New Capital City Development Policies Michael David Angelo; Reyhan Widyatna Harwenda; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.51234

Abstract

This study investigates public sentiment dynamics and dominant thematic concerns related to Indonesia’s new capital city development project (Ibu Kota Nusantara–IKN), particularly in the context of the political leadership transition from President Joko Widodo to President-elect Prabowo Subianto. Utilizing a dataset comprising 9,451 tweets collected from 2017 to 2025, sentiment analysis and topic modeling were applied to classify sentiment polarity and identify prevailing public discourse themes. Various traditional machine learning models—including Naïve Bayes, Support Vector Machine (SVM), AdaBoost, XGBoost, and LightGBM—were systematically compared with transformer-based deep learning models, specifically IndoBERT, to determine their effectiveness in sentiment classification. Results demonstrated that the IndoBERT model outperformed all traditional classifiers, achieving the highest accuracy, precision, recall, and F1 score, highlighting its superior capability in capturing nuanced linguistic patterns within informal social media texts. Independent samples t-tests revealed statistically significant sentiment shifts between the two political phases, emphasizing the impact of leadership transitions on public sentiment. Topic modeling further identified critical themes such as environmental sustainability, socio-economic implications, transparency, governance, and infrastructure development as central concerns driving public discussions. These findings provide actionable insights for policymakers and stakeholders, underscoring the importance of strategic communication and responsiveness to public sentiment in large-scale government initiatives.
LLM-Assisted Annotation in Comparative Sentiment and Trend Analysis of Indonesian K-12 Education on X Abdurrohhim S. Wahyudi; Rachmat Ramadhiansyah; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16433

Abstract

Indonesia was ranked 69th out of 81 participating nations in the 2022 PISA test results, reflecting persistent challenges in K-12 education quality. Public discourse on these challenges has grown substantially on social media, yet automated sentiment analysis of Indonesian education-related text remains difficult due to informal language and implicit sentiment expression. Prior work on Indonesian education sentiment has focused on short observation windows or a single policy, without comparing transformer-based models directly against traditional classifiers or scaling annotation with large language models. This study aims to compare traditional machine learning classifiers and transformer-based models for sentiment analysis of Indonesian K-12 education discourse on X, as well as the trend of sentiments longitudinally and key terms associated with different sentiments. The dataset comprises 38,957 tweets in the Indonesian language collected between January 2022 and October 2025 that were annotated via a multi-stage, large language model-assisted few-shot classification pipeline with self-consistency sampling tiebreaker, reaching inter-annotator agreement of 82.01% (Cohen’s kappa = 0.672). This study evaluated the accuracy and macro F1 score of four traditional classifiers, including Logistic Regression, Support Vector Machine, Naïve Bayes, and Random Forest, alongside three transformer-based models, namely IndoBERTweet, IndoBERT-base-p1, and IndoBERT-large-p1. Transformer-based models performed better than traditional classifiers based on all metrics. IndoBERTweet reached the best performance with 0.884 accuracy and 0.859 macro F1, compared to Logistic Regression as the best traditional classifier achieved 0.768 and 0.714, respectively. Negative sentiment dominated public discourse, with “kurikulum merdeka” as the most frequent term across all sentiment categories.
Co-Authors Abdurrohhim S. Wahyudi Aris Budi Santoso Aris Budi Santoso Indra Budi Indra Budi Michael David Angelo Rachmat Ramadhiansyah Reyhan Widyatna Harwenda