relation: http://eprints.unpak.ac.id/10657/
title: Sentiment Analysis of the RUU Perampasan Aset Using a  Comparative Approach on Bidirectional Encoder  Representations from Transformers (BERT) Models
creator: Alindiana, Resi
subject: Ilmu Komputer
description: Sentiment Analysis of the RUU Perampasan Aset Using a  Comparative Approach on Bidirectional Encoder  Representations from Transformers (BERT) Models  Resi Alindiana1*, Arie Qur'ania2  , Heri Bambang Santoso3  1 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia  2 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia  3 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor, Indonesia  * Correspondence: [065121081@student.unpak.ac.id]  Abstract: The RUU Perampasan Aset has existed since 2003 but has not yet been enacted and continues to attract  public attention, particularly on the social media platform X (Twitter), alongside the increasing discussion of  corruption cases. Therefore, this study aims to analyze public perception of the RUU Perampasan Aset from a  linguistic perspective. Sentiment analysis is conducted by comparing three BERT-based models—mBERT,  IndoBERT, and IndoBERTweet which are capable of understanding bidirectional textual context. Sentiment  classification is divided into positive and negative categories. Positive sentiment represents supportive opinions  expressed in polite and ethical language, while negative sentiment represents supportive opinions conveyed in  a sarcastic and pessimistic manner. The research process follows the Knowledge Discovery in Databases (KDD)  methodology. Evaluation results using variations of epochs and batch sizes show that IndoBERT achieves the  best performance with an accuracy of 92%, followed by mBERT at 91% and IndoBERTweet at 90%. These  findings indicate performance differences among the three models, suggesting that model effectiveness is  influenced not only by architectural design but also by dataset characteristics .  Keywords: RUU Perampasan Aset, Sentiment Analysis, X (Twitter), mBERT, IndoBERT, IndoBERTweet.
date: 2025-06-16
type: Thesis
type: NonPeerReviewed
identifier:   Alindiana, Resi  (2025) Sentiment Analysis of the RUU Perampasan Aset Using a Comparative Approach on Bidirectional Encoder Representations from Transformers (BERT) Models.  Skripsi thesis, Universitas Pakuan.