KLASIFIKASI SENTIMEN PENGGUNA PADA ULASAN APLIKASI E-WALLET DI GOOGLE PLAY STORE: PENDEKATAN MACHINE LEARNING

Authors

  • Ramadhanu Ramadhanu Universitas Islam Syekh Yusuf
  • ramadhani ramadhani Universitas Islam Syekh Yusuf

DOI:

https://doi.org/10.26418/justin.v13i4.94761

Keywords:

sentimen pengguna, e-wallet, machine learning, Optuna, Passive Aggressive, chi-square

Abstract

Pertumbuhan aplikasi e-wallet di Indonesia mendorong pentingnya analisis sentimen untuk memahami ulasan pengguna secara otomatis. Penelitian ini bertujuan mengklasifikasikan sentimen ulasan aplikasi e-wallet di Google Play Store dengan membandingkan kinerja tiga algoritma machine learning: Extra Trees, Passive Aggressive, dan Ridge Classifier. Sebanyak 47.626 ulasan dikumpulkan melalui web scraping dan diberi label menggunakan InSet Lexicon. Data diproses melalui tahap case folding, cleaning, stopword removal, dan lemmatization. Fitur diekstraksi menggunakan TF-IDF dan diseleksi dengan Chi-Square. Optimasi hyperparameter dilakukan menggunakan Bayesian Optimization dengan pustaka Optuna, dan evaluasi dilakukan dengan Stratified 5-Fold Cross-Validation. Hasil menunjukkan bahwa Passive Aggressive Classifier memiliki kinerja terbaik dengan akurasi 95,08%, precision 92,97%, recall 89,47%, dan F1-score 90,91% pada data uji. Model ini unggul dalam hal stabilitas dan akurasi dibandingkan dua algoritma lainnya. Temuan ini menunjukkan bahwa model yang efisien dan adaptif terhadap data berdimensi tinggi sangat sesuai untuk klasifikasi sentimen teks dalam Bahasa Indonesia. Penelitian ini memberikan kontribusi dalam pengembangan sistem analisis opini pengguna untuk peningkatan kualitas layanan aplikasi digital.

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Published

2025-11-01

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Vol. 13 No. 4 (2025)

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