FACE EXPRESSION RECOGNIZER DENGAN CONVOLUTIONAL NEURAL NETWORK UNTUK MEMBANTU PENDERITA AUTISME  MENGENALI EKSPRESI WAJAH SESEORANG

yohana christela oktaviani; Yulia Wahyuningsih

doi:10.23960/jitet.v11i3.3108

Authors

yohana christela oktaviani Darma Cendika Catholic University
Yulia Wahyuningsih Darma Cendika Catholic University

DOI:

https://doi.org/10.23960/jitet.v11i3.3108

Abstract Views: 372 File Views: 440 File Views: 0

Abstract

Autism is a type of mental health disorder that is 80% caused by heredity, and environmental influences cause the rest. People with autism tend to be unable to concentrate and look at the other person when interacting. Since the age of toddlers, people with autism have a shallow response to the surrounding environment. In addition, people with autism also find it very difficult to recognize someone's expression, even though facial expressions or facial expressions are one way that can be used to recognize someone's emotions. In addition, facial expressions indirectly reveal the contents of a person's thoughts. To overcome this, the author wants to build a face expression recognition model to help people with autism recognize someone's facial expressions. The primary purpose of this research is to help people with autism socialize and recognize the facial expressions of the people around them. This face expression recognition model was built by applying Convolutional Neural Network (CNN) intelligence and using the Tensorflow library and the Keras API. The dataset used in this study is a collection of faces from all over the world. In this research and model development process, the output display of the detection of facial expressions is in the form of diagrams and descriptions of the expressions that a person is experiencing.

Downloads

Download data is not yet available.

Author Biographies

yohana christela oktaviani, Darma Cendika Catholic University

Darma Cendika Catholic University

Yulia Wahyuningsih, Darma Cendika Catholic University

Darma Cendika Catholic University

References

M. Furqan, R. Kurniawan, and K. I. Hp, “Evaluasi Performa Support Vector Machine Classifier Terhadap Penyakit Mental,” J. Sist. Inf. Bisnis, vol. 10, no. 2, pp. 203–210, 2020, doi: 10.21456/vol10iss2pp203-210.

R. Irawan, A. Raharjo, A. Mulyono, and S. N. Afifi, “Aplikasi Praktis dan Mudah Mengenali Gejala Anak Autisme Sejak Dini,” ABDI MOESTOPO J. Pengabdi. Pada Masy., vol. 5, no. 1, pp. 109–117, 2022, doi: 10.32509/abdimoestopo.v5i1.1769.

D. R. Salsabila, R. Aisuwarya, N. P. Novani, L. Arief, and N. Afriyeni, “Sistem Pendeteksi Gejala Awal Tantrum pada Anak Autisme Melalui Ekspresi Wajah dengan Convolutional Neural Network,” Jitce, vol. 02, pp. 93–106, 2021, [Online]. Available: http://jitce.fti.unand.ac.id/.

Andri Nugraha Ramdhon and Fadly Febriya, “Penerapan Face Recognition Pada Sistem Presensi,” J. Appl. Comput. Sci. Technol., vol. 2, no. 1, pp. 12–17, 2021, doi: 10.52158/jacost.v2i1.121.

S. Arifianto and H. Wibowo, “Pembuatan Karakter 3D Dengan Facial,” pp. 61–68, 2016.

M. Elviyenti and S. Yulina, “Analisa Penerapan Facial Emotion Recognition pada Sistem E-Learning Analysis of Facial Emotion Recognition In E-Learning Systems,” Cahaya Pendidik., vol. 7, no. 2, pp. 132–140, 2021.

F. Ibrahim, “Implementasi Machine Learning Pada Alat Deteksi Emosi Untuk Sistem Kontrol Suhu Dan Pencahayaan Ruangan Implementation Of Machine Learning In Emotion Detection Device For Room Temperature And Lightning Control Systems,” vol. 9, no. 2, pp. 450–456, 2022.

R. R. K. Dewi, F. Sthevanie, and a. Arifianto, “Face expression recognition using Local Gabor Binary Pattern Three Orthogonal Planes (LGBP-TOP) and Support Vector Machine (SVM) method,” J. Phys. Conf. Ser., vol. 1192, no. 1, 2019, doi: 10.1088/1742-6596/1192/1/012048.

S. Bahri, R. Samsinar, and P. S. Denta, “Pengenalan Ekspresi Wajah untuk Identifikasi Psikologis Pengguna dengan Neural Network dan Transformasi Ten Crops,” vol. 5, no. 1, pp. 15–20.

P. P. Kusdiananggalih, E. Rachmawati, and Risanandar, “Pengenalan Ekspresi Wajah Menggunakan Deep Convolutional Neural Network,” J. Tugas Akhir Fak. Inform., vol. 8, no. 2, pp. 3429–3445, 2021, doi: 10.35200/explore.v11i2.478.

L. Vianika Sari, A. Musthafa, and T. Harmini, “Pengenalan Ekspresi Wajah Secara Realtime Menggunakan Transfer Learning Pada Facenet,” pp. 1–7, 2022.

A. Lioga Seandrio, A. Hendrianto Pratomo, and M. Y. Florestiyanto, “Implementation of Convolutional Neural Network (CNN) in Facial Expression Recognition Implementasi Convolutional Neural Network (CNN) Pada Pengenalan Ekspresi Wajah,” J. Inform. dan Teknol. Inf., vol. 18, no. 2, pp. 211–221, 2021, doi: 10.31515/telematika.v18i2.4823.

S. N. Faadhilah, S. Bukhori, and J. A. Putra, “Pengenalan Ekspresi Emosi pada Citra Wajah Menggunakan Extreme Machine Learning Studi Kasus Dataset Publik JAFFE,” vol. 2, no. October, pp. 19–27, 2022.

Tinaliah and T. Elizabeth, “Penerapan Convolutional Neural Network Untuk Klasifikasi Citra Ekspresi Wajah Manusia Pada MMA Facial Expression Dataset,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 4, pp. 2051–2059, 2017, [Online]. Available: http://ci.nii.ac.jp/naid/110009766950/.

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.

F. E. Ramadhan, “Penerapan Image Classification Dengan Pre-Trained Model Mobilenet Dalam Client-Side Machine Learning,” pp. 1–133, 2020.