Pengenalan Emosi pada Citra wajah menggunakan Metode YOLO

Apliana Gallu(1*), AR. Himamunanto(2), Haeni Budiati(3),

(1) Universitas Kristen Immanuel, Yogyakarta, Indonesia
(2) Universitas Kristen Immanuel, Yogyakarta, Indonesia
(3) Universitas Kristen Immanuel, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Human emotions can be expressed through facial expressions, and automatic recognition has a wide range of applications, from human and computer interaction to behavior analysis. Researchers developed a YOLO-based model that was trained to recognize various basic emotions such as happy, sad, angry, and surprised. The dataset used includes various facial images with corresponding emotion labels. This research produced a web to detect human faces using the YOLO algorithm in realtime. A total of 400 photos were used in the analysis; these images were separated into 4 classes: happy, sad, angry, and surprised. Of the 400 images, 70% are training images, 20% are validation images, and 10% are test images. There were 200 epochs of training data, which resulted in a new model. The validation rate of the mAP is 90%, the final score of the model shows that the object identification accuracy of the YOLOv8 model on facial expressions is at the highest point. The experimental results show that the YOLO method is able to detect and classify emotions with a high degree of accuracy. These results demonstrate its advantages in speed and efficiency compared to other more conventional methods. This implementation opens up opportunities for further development in real-time applications that allow the YOLO method to be used in a variety of applications.

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References


A. D. W. M. Sidik Et Al., “Pengenalan Ekspresi Wajah Menggunakan Teknik Filter Wavelet Gabor,” Fidel. J. Tek. Elektro, Vol. 3, No. 1, Pp. 1–4, 2021, Doi: 10.52005/Fidelity.V3i1.84.

F. Setiawan And D. A. R., “Sistem Pengenalan Wajah Dengan Metode Local Binary Pattern Histogram Pada Firebase,” Sentik, Vol. 4, No. 1, Pp. 19–25, 2020.

D. Nur Cahyo, H. Zulfia Zahro’, And N. Vendyansyah, “Pengenalan Ekspresi Mikro Wajah Dengan Ekstraksi Fitur Pada Komponen Wajah Menggunakan Metode Local Binary Pattern Histogram,” Jati (Jurnal Mhs. Tek. Inform., Vol. 7, No. 1, Pp. 822–829, 2023, Doi: 10.36040/Jati.V7i1.6167.

E. Tarigan, R. S. Naibaho, And A. Satria, “Pengenal Wajah Menggunakan Metode Viola Jonesdengan Menggunakan Aplikasi Matlab 2015,” Djtechno J. Teknol. Inf., Vol. 4, No. 1, Pp. 82–89, 2023, Doi: 10.46576/Djtechno.V4i1.3256.

R. Anita Jasmine, P. Arockia Jansi Rani, And J. Ashley Dhas, “Hyper Parameters Optimization For Effective Brain Tumor Segmentation With Yolo Deep Learning,” J. Pharm. Negat. Results, Vol. 13, Pp. 2247–2257, 2022, Doi: 10.47750/Pnr.2022.13.S06.292.

I. Maulana, N. Rahaningsih, And T. Suprapti, “Analisis Penggunaan Model Yolov8 (You Only Look Once) Terhadap Deteksi Citra Senjata Berbahaya,” Jati (Jurnal Mhs. Tek. Inform., Vol. 7, No. 6, Pp. 3621–3627, 2024, Doi: 10.36040/Jati.V7i6.8271.

D. P. Andini, Y. G. Sugiarta, T. Y. Putro, And R. D. Setiawan, “Sistem Presensi Kelas Berbasis Pengenalan Wajah Menggunakan Metode Cnn,” Jtera (Jurnal Teknol. Rekayasa), Vol. 7, No. 2, P. 315, 2022, Doi: 10.31544/Jtera.V7.I2.2022.315-322.

D. I. Mulyana, A. Sufriman, And M. B. Yel, “Implementasi Deteksi Emosional Pada Wajah Menggunakan Deep Learning - Yolov5,” Jutech J. Educ. Technol., Vol. 4, No. 1, Pp. 12–22, 2023, Doi: 10.31932/Jutech.V4i1.2174.

I. Azhar And Fitriyani, “Implementasi Algoritma Convolutional Neural Network Dalam Deteksi Emosi Manusia Berdasarkan Ekspresi Wajah,” Eprosiding Tek. Inform., Vol. 1, No. 1, Pp. 112–118, 2021.

M. N. Al Azam And C. Darujati, “Pengenalan Citra Wajah Frontal Menggunakan Hirarikal Klaster Berbasis Deep Learning Inception V3,” Jrec (Journal Electr. Electron., Vol. 9, No. 2, Pp. 9–13, 2022, Doi: 10.33558/Jrec.V9i2.3187.

J. Homepage, S. N. Faadhilah, S. Bukhori, And J. A. Putra, “Malcom: Indonesian Journal Of Machine Learning And Computer Science Emotional Expressions Recognition In Facial Images Using Extreme Machine Learning Case Study Of Jaffe Public Dataset Pengenalan Ekspresi Emosi Pada Citra Wajah Menggunakan Extreme Machine,” Vol. 2, No. October, Pp. 19–27, 2022.

R. Armandhani, R. C. Wihandika, And M. A. Rahman, “Klasifikasi Gender Berbasis Wajah Menggunakan Metode Local Binary Pattern Dan Random Knn,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., Vol. 3, No. 8, Pp. 7575–7582, 2019.

W. N. Jasim And R. J. Mohammed, “A Survey On Segmentation Techniques For Image Processing,” Iraqi J. Electr. Electron. Eng., Vol. 17, No. 2, Pp. 73–93, 2021, Doi: 10.37917/Ijeee.17.2.10.

M. Hatami, T. Tukino, F. Nurapriani, W. Widiyawati, And W. Andriani, “Deteksi Helmet Dan Vest Keselamatan Secara Realtime Menggunakan Metode Yolo Berbasis Web Flask,” Edusaintek J. Pendidikan, Sains Dan Teknol., Vol. 10, No. 1, Pp. 221–233, 2023, Doi: 10.47668/Edusaintek.V10i1.651.

Agustinus Andi, Kurniawan Rudi, And Oktavia Lingga Wijaya Harma, “Klasifikasi Emosi Melalui Ekspresi Wajah Menggunakan Algoritma Deep Learning,” Proc. Econ. Soc. Sci. Comput. Agric. Fish. 2nd 2023, P. 177, 2022.

Evan Tanuwijaya Timotius, David Christian Kartamihardja, And Timotius Leonardo Lianoto, “Deteksi Ekspresi Wajah Manusia Menggunakan Convolution Neural Network Pada Citra Pembelajaran Daring,” J. Ilm. Betrik, Vol. 13, No. 3, Pp. 224–230, 2021.




DOI: https://doi.org/10.30645/kesatria.v5i3.444

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.444.g439

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