Implementasi Pengenalan Ekspresi Wajah dengan Menggunakan Metode Convolutional Neural Network dan OpenCV Berbasis Webcam
Abstract
Facial expressions play an important role as a non-verbal language rich in emotional information, especially in psychological contexts. The main challenge in this research is to understand and analyse complex facial expressions, which are often difficult for psychologists to interpret. The implementation of webcam-based facial expression recognition leverages the computer's ability to visually recognise human emotions, supported by artificial intelligence and machine learning. Convolutional Neural Network (CNN) and OpenCV methods are used to detect and classify facial expressions directly. The CNN model is trained using a dataset with six expression classes (happy, sad, angry, surprised, neutral, afraid), with four convolution layers for multi-class classification. The implementation of facial expression recognition is successful, the system captures facial images from a webcam, detects faces in the frame, and classifies facial expressions directly on the screen window. The performance of training data against the trained model measured using Classification Accuracy shows an accuracy of 72.34% in training accuracy and 60.54% in validation accuracy. While the performance of the facial expression recognition system calculated using Confusion Matrix resulted in an accuracy of 70.55%. The calculation results show that the model is at the Fair Classification parameter level or able to classify facial expressions in humans with a fairly good level of accuracy, this research has great potential for application development in the field of psychology. However, further optimisation is needed by involving experts to ensure its effectiveness.
Downloads
References
S. Masrichah, “Ancaman dan Peluang Artificial Intelligence (AI),” Jurnal Pendidikan dan Sosial Humaniora., vol. 3, pp. 83 101, 2023.
M. Farwati, I. T. Salsabila, K. R. Navira, and T. Sutabri, “Analisa Pengaruh Teknologi Artificial Intelligence (AI) dalam Kehidupan Sehari-hari,” Universitas Darma Palembang., 2023.
D. M. Ulhaq, M. Zaidan, and D. Firdaus, "Pengenalan Ekspresi Wajah Secara Real-time Menggunakan Metode SSD Mobilenet Berbasis Android," Journal of Technology and Informatics (JoTI)., pp. 48-52, 2023.
Wikipedia. Ekspresi Wajah, Access Date July 2024, [Online].Available: https://id.wikipedia.org/wiki/Ekspresi_wajah.
A. Muttaqiin, H. Yuana, and M. Chulkamdi, "Implementasi Algoritma Convolutional Neural Network Untuk Pengenalan Ekspresi Wajah," Jurnal Riset Sistem Informasi dan Teknik Informatika (JURASIK)., pp. 772-792, 2023.
D. Setyadi, “Deteksi Ekspresi Wajah Menggunakan Metode CNN,” 2022.
M. Fauziah, “Perancangan dan Implementasi Sistem Deteksi Gerakan Kepala, Mata, dan Alis Berbasis Machine Learning,” Institut Teknologi Bandung., 2021.
A. N. Pulung, I. Fenriana, and R. Arijanto, "Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) pada Ekspresi Manusia," Jurnal Algor., 2020.
S. A. Lioga, P. A. Hendrianto, and M. Florestiyanto, "Implementation of Convolutional Neural Network (CNN) in Facial Expression Recognition," Jurnal Informatika dan Teknologi Informasi., pp. 211-221, 2021.
S. Li dan W. Deng, "Deep Facial Expression Recognition: A Survey," IEEE Transactions on Affective Computing., vol.13, no. 3, pp. 1195-1215, 2020.
Y. Wang, Y. Zhang, and S. Zhang, "Facial expression recognition: A comprehensive survey," International Journal of Computer Applications., vol. 179, no. 1, pp. 1-6, 2018.
J. E. Widyaya and S. Budi, "Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network," Jurnal Teknik Informatika dan Sistem Informasi., vol. 7, no. 1, 2021.
R. Kumar, S. Agarwal, and A. Kumar, "Facial Expression Recognition: A Survey," International Journal of Computer Applications., vol. 181, no. 20, pp. 18-24, 2019.
Y. LeCun, Y. Bengio, and G. Haffner, Gradient-Based Learning Applied to Document Recognition," Proc. IEEE vol. 86, no. 11, pp. 2278-2324, 1998.
M. A. Leonardi and A. Y. Chandra, "Analisis Perbandingan CNN dan Vision Transformer untuk Klasifikasi Biji Kopi Hasil Sangrai," JURNAL MEDIA INFORMATIKA BUDIDARMA., vol. 8, no. 3, pp. 1398-1307, 2024.
D. Anggara, N. Suarna, and Y. A. Wijaya, "ANALISA PERBANDINGAN PERFORMA OPTIMIZER ADAM, SGD, DAN RMSPROP PADA MODEL H5," NERO (Networking Engineering Research Operation)., vol. 8, no. 1, pp. 53-64, 2023.
D. Pazriyah, “Penggunaan Raspberry Pi Dalam Mendeteksi Warna Melalui Webcam,” Politeknik Negeri Sriwijaya, 2017.
D. Leni, "Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)," Jurnal Surya Teknika., vol. 10, no. 1, pp. 625-632, 2023.
R. S. Irawan, “APLIKASI PERANGKAT BERGERAK PENGKLASIFIKASI KESEGARAN DAGING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR MOBILENETV2,” Sekolah Tinggi Informatika & Komputer Indonesia., 2024.
M. Zufar and B. Setiyono, "Convolutional Neural Networks untuk Pengenalan Wajah Secara Real-Time," Jurnal Sains dan Seni ITS., vol. 5, no. 2, pp. 128-862, 2016.
N. Ketkar, J. Moolayil, "Convolutional Neural Networks," in Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch., pp. 197-242, 2021.
M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, "Learning Pooling for Convolutional Neural Network," Neurocomputing.,vol. 224, pp. 96-104, 2017.
S. S. Basha, S. R. Dubey, V. Pulabaigari, and S. Mukherjee, "Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification," Neurocomputing., vol. 378, pp. 112-119, 2020.
D. Sinaga, "Jaringan Saraf Tiruan Infeksi Mata Dengan Menggunakan Metode Beraksitektur Multi Layer Perceptron,"Informasi dan Teknologi Ilmiah (INTI)., vol. 7, no. 2, pp. 189-192, 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Pengenalan Ekspresi Wajah dengan Menggunakan Metode Convolutional Neural Network dan OpenCV Berbasis Webcam
Pages: 339-348
Copyright (c) 2024 Raifvaldhy Jounias Luppus Melatisudra, Suharjanto Utomo, Sri Sutjiningtyas, Hernawati Hernawati

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).