Prediksi Emosi Pada Teks Berbahasa Indonesia Menggunakan Model Long-Short Term Memory (LSTM)
DOI:
https://doi.org/10.29407/s5xkwf84Keywords:
Analisis Emosi, BiLSTM, Klasifikasi Teks, Pengolahan Bahasa AlamiAbstract
Analisis emosi pada teks merupakan bagian penting dalam pengolahan bahasa alami karena mampu memberikan pemahaman terhadap opini dan perasaan pengguna dalam berbagai konteks digital. Penelitian ini bertujuan untuk mengklasifikasikan emosi pada teks berbahasa Indonesia dengan memanfaatkan model Bidirectional Long Short-Term Memory (BiLSTM) tanpa menggunakan model pretrained. Pemilihan topik ini didasarkan pada kebutuhan akan model klasifikasi emosi yang efisien, ringan, dan dapat diimplementasikan pada lingkungan dengan keterbatasan sumber daya komputasi. Metode penelitian meliputi tahap praproses data, vektorisasi teks menggunakan TextVectorization, pembagian data latih dan validasi secara terstratifikasi, serta pelatihan model BiLSTM bertingkat dengan penerapan class weighting dan regularisasi untuk mengatasi ketidakseimbangan kelas dan overfitting. Evaluasi dilakukan menggunakan metrik akurasi, grafik pelatihan, dan confusion matrix. Hasil eksperimen menunjukkan bahwa model BiLSTM yang diusulkan mencapai akurasi validasi sebesar 86.01% dengan proses pelatihan yang stabil berdasarkan grafik akurasi dan loss. Analisis confusion matrix memperlihatkan bahwa model mampu mengklasifikasikan emosi dengan ciri leksikal yang jelas secara akurat, sementara kesalahan klasifikasi lebih sering terjadi pada kelas emosi yang memiliki kedekatan semantik.
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