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Ghanim Kanugrahan
Satu University

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Statistical comparison of MLP and LSTM for mobile health sentiment analysis Ghanim Kanugrahan; Win Ce; Vito Hafizh Cahaya Putra; Yudi Ramdhani; Febriyanti Panjaitan
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp818-826

Abstract

This study investigates user sentiment towards the Mobile JKN public health application by applying text classification models based on deep learning. Two approaches were compared: a multi-layer perceptron (MLP) with TF IDF features and long short-term memory (LSTM) with Word2Vec embeddings. The dataset consists of 114,364 Indonesian-language user reviews collected from the Google Play Store. To address class imbalance, we applied random oversampling. Each model was evaluated using 5-fold stratified shuffle split cross-validation. The results showed that MLP models achieved higher accuracy (up to 83.90%), while LSTM models demonstrated better recall and precision on minority classes such as neutral sentiment. However, statistical validation using the Wilcoxon signed-rank test revealed that the performance differences between models were not statistically significant (p > 0.05). These findings suggest that both models are viable for sentiment analysis, with trade-offs depending on the evaluation metric of interest. Future work may explore hybrid architecture and larger datasets for improved performance and statistical confidence.
Co-Authors Febriyanti Panjaitan Hafizh Cahaya Putra, Vito Win Ce Yudi Ramdhani