eprintid: 10657
rev_number: 6
eprint_status: archive
userid: 46
dir: disk0/00/01/06/57
datestamp: 2026-04-18 06:05:36
lastmod: 2026-04-18 06:05:36
status_changed: 2026-04-18 06:05:36
type: thesis
metadata_visibility: show
creators_name: Alindiana, Resi
creators_NPM: 065121081
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/TYD
contributors_name: Qur'ania, Arie
contributors_name: Santoso, Heri Bambang
corp_creators: Universitas Pakuan
corp_creators: Fakultas Matematika dan Ilmu Pnegetahuan Alam
corp_creators: Program Studi Ilmu Komputer
title: Sentiment Analysis of the RUU Perampasan Aset Using a
Comparative Approach on Bidirectional Encoder
Representations from Transformers (BERT) Models
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
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
date_type: published
institution: Universitas Pakuan
department: Fakultas Matematika dan Pengetahuan Alam
thesis_type: Skripsi
thesis_name: Sarjana
citation:   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.