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Submitted
September 12, 2025
Accepted
December 23, 2025
Published
December 25, 2025
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Abstract

The rapid growth of e-commerce in Indonesia presents significant opportunities for micro, small, and medium enterprises (MSMEs), yet the diversity of marketplace platforms complicates the selection of an optimal sales channel. This study addressed this challenge by developing a data-driven recommendation system based on sentiment analysis of user reviews. Utilizing a dataset of 80,000 reviews scraped from four major platforms on the Google Play Store (Shopee, Tokopedia, Lazada, and Blibli), two classification approaches were implemented and compared: support vector machine (SVM) and long short-term memory (LSTM). Both models demonstrated a competitive performance, enabling effective sentiment categorization. Furthermore, multinomial logistic regression was employed to analyze the influence of key variables rating, number of likes, and marketplace brand on sentiment outcomes. The analysis revealed that Shopee yielded the highest probability of receiving positive reviews (97.82%) and showed no significant association with negative sentiment. Consequently, this study recommends Shopee as the primary platform for MSMEs to enhance their digital presence and sales performance. The primary contribution lies in integrating machine learning-based sentiment analysis with statistical modelling to generate actionable, evidence-based marketplace recommendations for MSMEs.

Keywords

Sentiment Analysis Recommendation System E-Commerce Sentiment Mining Machine Learning Customer Reviews

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How to Cite
Adiyana, I., Kurniawan, A., Rahmatika, A. H., Setiono, N. H., & Gumelar, S. F. (2025). Recommending E-Commerce Platforms for MSMEs: A Sentiment Analysis Approach. Enthusiastic : International Journal of Applied Statistics and Data Science, 5(2), 190–200. https://doi.org/10.20885/enthusiastic.vol5.iss2.art8
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