Comparative Analysis of Explainable AI Methods LIME, SHAP, and ELI5 on Random Forest Based Indonesian E-Commerce Sentiment Classification
DOI:
https://doi.org/10.52436/1.jutif.2026.7.2.5642Keywords:
Explainable AI, Product Reviews, Random Forest, Sentiment AnalysisAbstract
The rapid growth of e-commerce platforms in Indonesia has generated a massive volume of product reviews, making sentiment classification essential for understanding customer perceptions and supporting data-driven decision making. This study aims to develop a sentiment classification model for Indonesia e-commerce product reviews while enhancing model transparency through Explainable Artificial Intelligence (XAI). The proposed approach employs a Random Forest classifier eith Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The dataset consists of 23,194 product reviews from the fashion and electronics categories, classified into positive, negative, and neutral sentiment. Model performance is evaluated using accuracy, precision, recall, and F1-Score metrics. Experimental results show taht the Random Forest model achieves an accuracy of 93.74%, with the best performance observed in the postive sentiment class. To improve interpretability, three XAI methods-LIME, SHAP, and ELI5-are applied. The analysis indicates that LIME is effective for local explanations, SHAP provides consistent global and local feature importence, and ELI5 offers concise and computationally efficient global explanations. This study contributes to the field of computer science by demostrating how comparative XAI analysis can bridge the gap between high-performing black-box models and interpretable sentiment classification in high-dimensional extual data, thereby supporting transparent and accountavle AI system in e-commerce applications.
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