File Tesis Periode 1 2025

File Tesis Periode 1 2025

• By Trihardo, Rendra;
• Tesis
• 33

Penelitian ini menganalisis sentimen pengguna aplikasi Taspen Otentikasi menggunakan pemrosesan bahasa alami dan berbagai metode klasifikasi. Data ulasan diambil dari Google Play Store dan dilabeli secara manual. Proses penelitian dilakukan melalui empat tahap yaitu tahap baseline dengan SVM dan BiLSTM menggunakan Word2Vec, tahap SVM dan BiLSTM menggunakan GrEmLIn, tahap fine-tuning model mDeBERTa-v3 dan tahap stacking ensemble menggunakan XGBoost. Evaluasi mencakup akurasi, precision, recall, F1-score dan ROC AUC serta efisiensi komputasi. Model stacking ensemble mencetak akurasi 92,23% dan ROC AUC 0,9594, sementara mDeBERTa-v3 unggul sebagai model tunggal. Embedding GrEmLIn mampu meningkatkan performa BiLSTM berbasis CPU maupun GPU, sedangkan Word2Vec tetap efisien untuk model SVM. Hasil menunjukkan bahwa pemilihan arsitektur dan embedding yang tepat menghasilkan sistem klasifikasi sentimen yang akurat dan efisien untuk layanan digital berbahasa Indonesia.

• Katakunci analisis sentimen, mDeBERTa-v3, GrEmLIn, BiLSTM, SVM, stacking ensemble, XGBoost
• URL http://
• Penerbit ,

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