<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Sentiment Analysis of the RUU Perampasan Aset Using a&#13;
Comparative Approach on Bidirectional Encoder&#13;
Representations from Transformers (BERT) Models</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Resi</mods:namePart><mods:namePart type="family">Alindiana</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Sentiment Analysis of the RUU Perampasan Aset Using a&#13;
Comparative Approach on Bidirectional Encoder&#13;
Representations from Transformers (BERT) Models&#13;
Resi Alindiana1*, Arie Qur'ania2&#13;
, Heri Bambang Santoso3&#13;
1 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia&#13;
2 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia&#13;
3 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia&#13;
* Correspondence: [065121081@student.unpak.ac.id]&#13;
Abstract: The RUU Perampasan Aset has existed since 2003 but has not yet been enacted and continues to attract&#13;
public attention, particularly on the social media platform X (Twitter), alongside the increasing discussion of&#13;
corruption cases. Therefore, this study aims to analyze public perception of the RUU Perampasan Aset from a&#13;
linguistic perspective. Sentiment analysis is conducted by comparing three BERT-based models—mBERT,&#13;
IndoBERT, and IndoBERTweet which are capable of understanding bidirectional textual context. Sentiment&#13;
classification is divided into positive and negative categories. Positive sentiment represents supportive opinions&#13;
expressed in polite and ethical language, while negative sentiment represents supportive opinions conveyed in&#13;
a sarcastic and pessimistic manner. The research process follows the Knowledge Discovery in Databases (KDD)&#13;
methodology. Evaluation results using variations of epochs and batch sizes show that IndoBERT achieves the&#13;
best performance with an accuracy of 92%, followed by mBERT at 91% and IndoBERTweet at 90%. These&#13;
findings indicate performance differences among the three models, suggesting that model effectiveness is&#13;
influenced not only by architectural design but also by dataset characteristics .&#13;
Keywords: RUU Perampasan Aset, Sentiment Analysis, X (Twitter), mBERT, IndoBERT, IndoBERTweet.</mods:abstract><mods:classification authority="lcc">Ilmu Komputer</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2025-06-16</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas Pakuan;Fakultas Matematika dan Pengetahuan Alam</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mods:mods>