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Dharmasaguna, Made Bayu Brahmanda
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Comparison of LSTM and TCN Models for Customer Churn Prediction Based on Sentiment and Transaction Data Dharmasaguna, Made Bayu Brahmanda; Retnowardhani, Astari
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.979

Abstract

This study investigates the combined use of customer review sentiment analysis and transaction history to predict customer churn on the Balimall Market e-commerce platform. The dataset includes 41,519 reviews labeled with positive and negative sentiments and 48 transaction samples labeled as churn or non-churn based on RFM method. Two deep learning models, Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), are applied in parallel for each analysis path. Data pre-processing includes filtering, cleaning, tokenizing, normalization, sentiment labeling, as well as feature engineering and churn labeling. Evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics shows that TCN excels with 91.55% accuracy on sentiment analysis and 91.67% on churn prediction, while LSTM achieves 86.35% and 86.67% respectively. Segment analysis shows that 47.30 % of users express negative sentiment yet remain active, 51.69 % express positive sentiment and remain active , 0.54 % express negative sentiment and churn, and 0.48 % express positive sentiment and churn. This finding demonstrates that negative sentiment alone does not necessarily lead to churn; instead, the greatest churn risk arises in negative sentiment churners and positive sentiment churners. Expert validation confirmed the reliability of both models, with the recommendation of using a hybrid to combine the advantages of each architecture. The results of this study are expected to help Baliyoni Group design a more targeted customer retention strategy and improve customer satisfaction by examining these segment conditions.
Co-Authors Retnowardhani, Astari