<![CDATA[In the past two decades, the rapid growth of e-commerce has significantly transformed global business practices. E-commerce has not only revolutionized the retail industry but also positively impacted businesses and consumer experiences. The ease of online shopping enables users to select products at more competitive prices. Amidst these changes, human-computer interactions have increasingly evolved toward natural conversations through Natural Language Processing (NLP). This study aims to develop a chatbot utilizing Long Short-Term Memory (LSTM) technology as a medium for e-commerce customer service. The dataset used for chatbot development is in JSON format and consists of 580 entries spanning 38 categories or classes. Data processing involves several preprocessing stages, including case folding, lemmatization, tokenization, and padding. The model is developed using a bidirectional LSTM and GRU architecture, followed by regularization techniques to enhance performance. Evaluation results show the model achieves 90% training accuracy and 63% validation accuracy with an F1-score of 62%. While there are indications of overfitting, the observed differences are not statistically significant, indicating the model remains capable of providing reliable responses. Additionally, the model is integrated into a Flask-based web application with an interactive interface to facilitate user access. This study demonstrates that LSTM is effective in addressing vanishing gradient problems.]]>
