Klasifikasi Jenis Emosi Melalui Ucapan Menggunakan Metode Convolutional Neural Network
Klasifikasi Jenis Emosi Melalui Ucapan
DOI:
https://doi.org/10.26594/teknologi.v13i2.3740Abstract
Ucapan adalah metode yang paling sering digunakan manusia untuk berkomunikasi satu sama lain. Ucapan berisi informasi yang bervariasi dimana selain dapat mengetahui pesan seseorang, juga dapat mengetahui kondisi emosi orang tersebut. Ekspresi emosi dalam sebuah percakapan berperan penting dalam memberikan penekanan pada informasi yang disampaikan menjadi lebih kuat. Pengenalan emosi melalui ucapan ini dapat diaplikasikan ke dalam berbagai bidang seperti ilmu kognitif, call centre dan bidang lainnya. Oleh karena itu, penelitian ini bermaksud untuk mengklasifikasikan emosi seseorang melalui ucapan mereka dengan menggunakan algoritma Convolutional Neural Network (CNN). Tujuan penelitian ini adalah untuk mencari model CNN dengan performansi terbaik dalam mengklasifikasikan emosi menjadi 8 kelas yaitu, netral, sedih, tenang, takut, senang, terkejut, jijik dan marah. Model CNN dibedakan berdasarkan data masukkan yang menggunakan metode ekstraksi fitur yang berbeda-beda. Dari hasil pengujian diperoleh model paling baik dengan rasio pembagian data sebesar 80% untuk data latih, 10% untuk data validasi serta 10% untuk data uji yang memakai metode ekstraksi Mel-Frequency Cepstral Coefficients (MFCC) dengan nilai rata-rata akurasi sebesar 70% diikuti nilai rata-rata recall dan presisi masing-masing 68% dan 67%. Untuk emosi yang paling sering ditebak dengan benar adalah emosi marah, terkejut, sedih dan tenang dengan rata-rata prediksi benar sebesar 77%.
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Copyright (c) 2023 Fransiskus Jonathan Tanudjaja, Eva Yulia Puspaningrum , Yisti Vita Via
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