| dc.description.abstract | The rapid growth of investment applications in Indonesia requires a deep
understanding of user reviews. This study aims to analyze the sentiment and aspect
found in user reviews of the AJAIB application, collected from the Google Play
marketplace, totaling 11,004 data. Aspect identification was conducted using the
Latent Dirichlet Allocation (LDA) method, which successfully extracted three main
topics with a coherence score of 0.465, interpreted as interface, performance, and
transaction. Furthermore, sentiment analysis is performed using the Bidirectional
Encoder Representations from Transformers (BERT) model. In this research, the type
of BERT used is IndoBERT, which is a pre-trained model optimized specifically for
Indonesian. The IndoBERT model is then used to classify sentiment into two
categories, namely positive and negative. The BERT model was trained with a batch
size of 32, a learning rate of 5e-5, and over 3 epochs, achieving an accuracy of 91%,
with a precision of 78%, recall of 88%, and F1-score of 82% for negative sentiment
and a precision of 96%, recall of 92%, and F1-score of 94% for positive sentiment. As
a benchmark, the Support Vector Machine (SVM) model was used as a baseline. The
evaluation results show that the BERT model generally outperforms the SVM model
across nearly all evaluation metrics, except for precision in the positive sentiment
class and recall in the negative sentiment class, where both models achieved equal
scores. These findings indicate that the combination of LDA and BERT approaches
can be effectively used to understand user opinions and needs in online investment
applications. | en_US |
