Implementasi Convolutional Neural Network Untuk Deteksi Emosi Melalui Wajah

Rizki Rafiif Amaanullah; Gracia Rizka Pasfica; Satria Adi Nugraha; Mohammad Rifqi Zein; Faisal Dharma Adhinata

doi:10.35746/jtim.v3i4.189

Rizki Rafiif Amaanullah Institut Teknologi Telkom Purwokerto
Gracia Rizka Pasfica Institut Teknologi Telkom Purwokerto
Satria Adi Nugraha Institut Teknologi Telkom Purwokerto
Mohammad Rifqi Zein Institut Teknologi Telkom Purwokerto
Faisal Dharma Adhinata Institut Teknologi Telkom Purwokerto

DOI: https://doi.org/10.35746/jtim.v3i4.189

Keywords: Convolutional Neural Network, Emotion, Expression

Abstract

The human emotional condition can be reflected in speech, gestures, and especially facial expressions. The problem that is often faced is that humans tend to be subjective in assessing people's emotions. Humans can easily guess someone's emotions through the expressions shown, as well as computers. Computers can think like humans if they are given an algorithm for human thinking or artificial intelligence. This research will be an interaction between humans and computers in analyzing human expressions. This research was conducted to prove whether the implementation of CNN (Convolutional Neural Network) can be used in detecting human emotions or not. The material needed to conduct facial recognition research is a dataset in images of various kinds of human expressions. Based on the dataset that has been obtained, the images that have been collected are divided into two parts, namely training data and test data, where each training data and test data has seven different emotion subfolders. Each category of images is 35 thousand data which will later be trimmed to around a few thousand data to balance the dataset. According to their class, these various expressions will be classified into several emotions: angry emotions, happy emotions, fearful emotions, disgusting emotions, surprising emotions, neutral emotions, and sad emotions. The results showed that from the calculation of 40 epochs, 81.92% was obtained for training and 81.69% for testing.

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References

P. P. Kusdiananggalih and E. Rachmawati, “Pengenalan Ekspresi Wajah Dari Cross Dataset Menggunakan Convolutional Neural Network ( CNN ),” vol. 8, no. 2, pp. 3429–3445, 2021.

N. Fadlia and R. Kosasih, “Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn),” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 207–215, 2019, doi: 10.35760/tr.2019.v24i3.2397.

V. M. P. Salawazo, D. P. J. Gea, R. F. Gea, and F. Azmi, “Implementasi Metode Convolutional Neural Network ( CNN ) Pada Peneganalan Objek Video CCTV,” J. Mantik Penusa, vol. 3, no. 1, pp. 74–79, 2019.

S. F. Handono, F. T. Anggraeny, and B. Rahmat, “Implementasi Convolutional Neural Network (Cnn) Untuk Deteksi Retinopati Diabetik,” J. Inform. dan Sist. Inf., vol. 1, no. 2, pp. 669–678, 2020.

N. Dewi and F. Ismawan, “Implementasi Deep Learning Menggunakan Cnn Untuk Sistem Pengenalan Wajah,” Fakt. Exacta, vol. 14, no. 1, p. 34, 2021, doi: 10.30998/faktorexacta.v14i1.8989.

W. S. Jeon and S. Y. Rhee, “Fingerprint pattern classification using convolution neural network,” Int. J. Fuzzy Log. Intell. Syst., vol. 17, no. 3, pp. 170–176, 2017, doi: 10.5391/IJFIS.2017.17.3.170.

E. N. Arrofiqoh and Harintaka, “IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN PADA CITRA RESOLUSI TINGGI ( The Implementation of Convolutional Neural Network Method for Agricultural Plant Classification in High Resolution Imagery ),” Geomatika, vol. 24, no. 2, pp. 61–68, 2018.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

A. Santoso and G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,” Emit. J. Tek. Elektro, vol. 18, no. 01, pp. 15–21, 2018, doi: 10.23917/emitor.v18i01.6235.

P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–21, 2020.

Y. Achmad, R. C. Wihandika, and C. Dewi, “Klasifikasi emosi berdasarkan ciri wajah wenggunakan convolutional neural network,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 11, pp. 10595–10604, 2019.