Analisis Sentimen pada Ulasan Aplikasi FinTech di Indonesia: Studi Komparatif Model Machine Learning dan Deep Learning
DOI:
https://doi.org/10.31004/joecy.v6i1.6939Keywords:
Analisis Sentimen, FinTech, Deep Learning, LSTM, Machine Learning, TF-IDFAbstract
Pertumbuhan sektor Teknologi Finansial (FinTech) telah menjadikan umpan balik pelanggan dari platform digital sebagai sumber penting untuk pengambilan keputusan strategis. Namun, volume dan ketidakstrukturan data, khususnya dalam bahasa informal seperti Bahasa Indonesia, menimbulkan tantangan analitis yang signifikan. Penelitian ini bertujuan untuk mengidentifikasi pipeline optimal untuk klasifikasi sentimen pada ulasan pengguna Livin' by Mandiri, super-app perbankan digital Indonesia. Kami melakukan analisis komparatif menggunakan dataset dunia nyata berisi 117.471 ulasan yang tidak seimbang (55% negatif, 31% positif, 14% netral) yang dibersihkan dari Google Play Store. Dua teknik vektorisasi teks, Bag-of-Words (BoW) dan TF-IDF, diuji pada empat classifier machine learning: Random Forest, Logistic Regression, Decision Tree, dan SVM, serta dibandingkan dengan model Deep Learning berbasis Long Short-Term Memory (LSTM). Hasilnya menunjukkan bahwa model LSTM unggul dengan akurasi 98,02% dan weighted F1-score 0,99, sementara model machine learning terbaik, Logistic Regression dengan TF-IDF, menghasilkan weighted F1-score 0,92. Temuan ini menegaskan bahwa meskipun machine learning tradisional efektif, LSTM lebih unggul dalam menangkap konteks dalam data sekuensial yang kompleks dan tidak seimbang. Penelitian ini menawarkan kerangka kerja yang berguna bagi institusi keuangan untuk menerapkan sistem analisis sentimen otomatis yang akurat dan efektif.
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