Mendeteksi Emosi Berdasarkan Postingan Sosial Media X Menggunakan Algoritma Long Short-Term Memory
DOI:
https://doi.org/10.62411/tc.v24i3.13509Abstract
Emosi merupakan aspek penting dalam komunikasi manusia yang sering muncul melalui unggahan di media sosial. Emosi tersebut diekspresikan dalam teks berbahasa Indonesia di platform media sosial X. Penelitian ini bertujuan untuk mendeteksi lima kategori emosi, yaitu marah, takut, senang, cinta, dan sedih. Model yang digunakan adalah algoritma Long Short-Term Memory (LSTM) dengan representasi kata dari FastText. Model dilatih menggunakan metode EarlyStopping dan dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa model mencapai akurasi sebesar 79% pada data testing dengan performa yang relatif seimbang untuk setiap kategori emosi. Penelitian ini menunjukkan bahwa FastText dan LSTM efektif untuk mendeteksi emosi dalam teks media sosial berbahasa Indonesia. Penelitian ini diharapkan bermanfaat dalam pengembangan penelitian berbasis emosi, seperti analisis sentimen, pemantauan opini publik, dan sistem pendukung kesehatan mental. Kata Kunci – Deteksi Emosi, Sosial Media, Long Short-Term Memory, FastText
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