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All Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia
I Putu Agus Ari Mahendra
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Computer Science & IT Control & Systems Engineering Engineering

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ANALISIS KOMPARATIF KLASIFIKASI SENTIMEN PENGGUNA APLIKASI INVESTASI MENGGUNAKAN ALGORITMA HYBRID CNN-LSTM, CNN-GRU DENGAN IMPLEMENTASI SMOTE I Putu Agus Ari Mahendra; Kusrini, Kusrini
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2025): Juni 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i1.258

Abstract

Sentiment analysis, a branch of Natural Language Processing (NLP), plays a crucial role in identifying and classifying opinions embedded in text. This study aims to compare the performance of hybrid CNN-LSTM and CNN-GRU models in sentiment analysis of user reviews for investment applications on Google Play Store, utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A total of 15,000 user reviews were collected through web scraping, preprocessed using the TF-IDF method and various text cleaning techniques. The CNN-LSTM and CNN-GRU models were evaluated using an 80%-20% train-test split. The evaluation results showed that CNN-GRU outperformed in terms of precision (91.62%), F1 score (90.45%), and overall accuracy (87.60%), while CNN-LSTM excelled in recall (91.08%) for detecting positive reviews. CNN-GRU was deemed more balanced in detecting both positive and negative sentiments, making it a more reliable choice for sentiment analysis requiring uniform performance
Co-Authors Kusrini, Kusrini