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primary topic: ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA

ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA
type: Thesis
type: Article
abstract: The rapid growth of e-commerce in Indonesia has increased the use of product reviews as an important consideration for consumers before making purchases. However, the emergence of fake reviews has become a serious issue because it can affect consumer trust and reduce the credibility of e-commerce platforms. This study aims to compare the performance of Multilingual BERT and IndoBERT models in detecting fake reviews written in Indonesian. This research employed an experimental method consisting of data collection, labeling, preprocessing, model training, evaluation, and result comparison. The dataset was obtained from Kaggle and consisted of 18,242 Indonesian e-commerce product reviews. Approximately 10% of the dataset was manually labeled and then used in a pseudo-labeling process with the Multinomial Naive Bayes algorithm to generate additional training data. Both Multilingual BERT and IndoBERT were trained using the same parameters to ensure a fair comparison. Model evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that IndoBERT achieved the best performance with an accuracy of 87.12%, precision of 85.81%, recall of 83.01%, and F1-score of 84.39%. Meanwhile, Multilingual BERT achieved an accuracy of 86.30%, precision of 83.23%, recall of 84.31%, and F1-score of 83.77%. These findings indicate that IndoBERT is more effective in understanding the characteristics of the Indonesian language, especially informal e-commerce review texts. This study also demonstrates that the pseudo-labeling approach can help increase the amount of training data, although the quality of additional labels greatly affects model performance. Based on the research findings, IndoBERT is more recommended for implementing fake review detection systems on Indonesian e-commerce platforms compared to Multilingual BERT.
date: 2026-06-03
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
has document: ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA (Text)
has version: ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA (Text)
has published: ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA (Text)
subject: 004 Pemrosesan Data, Ilmu Komputer, Teknik Informatika

ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA (Text)
type: Document
has file: 22106050035_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
has part: 22106050035_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf

ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA (Text)
type: Document

UIN SUNAN KALIJAGA YOGYAKARTA
type: Organization

FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
type: Organization

NIM.: 22106050035 Marsha Kamila
type: Person
given name: NIM.: 22106050035
family name: Marsha Kamila

HTML Summary of #76862 ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA
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primary topic: ANALISIS PERBANDINGAN KINERJA BERT DAN INDOBERT DALAM MENDETEKSI ULASAN PALSU PRODUK E-COMMERCE BERBAHASA INDONESIA

004 Pemrosesan Data, Ilmu Komputer, Teknik Informatika
type: Concept