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primary topic: Sentiment Analysis of the RUU Perampasan Aset Using a Comparative Approach on Bidirectional Encoder Representations from Transformers (BERT) Models

Sentiment Analysis of the RUU Perampasan Aset Using a Comparative Approach on Bidirectional Encoder Representations from Transformers (BERT) Models
type: Thesis
type: Article
abstract: 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
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
subject: Ilmu Komputer

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Arie Qur'ania
type: Person
given name: Arie
family name: Qur'ania

Resi Alindiana
type: Person
given name: Resi
family name: Alindiana

Heri Bambang Santoso
type: Person
given name: Heri Bambang
family name: Santoso

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

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primary topic: Sentiment Analysis of the RUU Perampasan Aset Using a Comparative Approach on Bidirectional Encoder Representations from Transformers (BERT) Models

Ilmu Komputer
type: Concept