Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network
Deep learning is a neural network that creates innovations that give computer-implanted problem-solving expertise. One of the principles of computer vision is a detection system with a vision framework that can identify things encountered in the same manner as a human vision system. Using an artific...
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| Format: | Article |
| Language: | English |
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Lembaga Penelitian dan Pengabdian kepada Masyarakat
2023-04-01
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| Series: | Rekayasa |
| Subjects: | |
| Online Access: | https://journal.trunojoyo.ac.id/rekayasa/article/view/16974 |
| _version_ | 1797832803120840704 |
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| author | Purnawarman Musa Wahid Khairul Anam Saiful Bahri Musa Witari Aryunani Remi Senjaya Puji Sularsih |
| author_facet | Purnawarman Musa Wahid Khairul Anam Saiful Bahri Musa Witari Aryunani Remi Senjaya Puji Sularsih |
| author_sort | Purnawarman Musa |
| collection | DOAJ |
| description | Deep learning is a neural network that creates innovations that give computer-implanted problem-solving expertise. One of the principles of computer vision is a detection system with a vision framework that can identify things encountered in the same manner as a human vision system. Using an artificial intelligence-based Convolutional Neural Network (CNN) model with deep learning techniques, we present a face emotion identification system. The categorization of facial expressions will be utilized as the basis for a face recognition system trained using CNN. The applications are intended to use the OpenCV, Keras, and TensorFlow libraries as the backend. We were discussing the study on the best use of xception architectural models in facial expression recognition systems. Based on the results of these tests, the study obtained an increased accuracy value in training and data testing on an xception architecture model trained for facial expressions using the FER-2013 dataset, resulting in an accuracy value of 66% as well as the value of each average for precision (76%), recall (65%), and F1 score (63%). |
| first_indexed | 2024-04-09T14:13:38Z |
| format | Article |
| id | doaj.art-f62067cb67a74431b747e6d60d831c9d |
| institution | Directory Open Access Journal |
| issn | 0216-9495 2502-5325 |
| language | English |
| last_indexed | 2024-04-09T14:13:38Z |
| publishDate | 2023-04-01 |
| publisher | Lembaga Penelitian dan Pengabdian kepada Masyarakat |
| record_format | Article |
| series | Rekayasa |
| spelling | doaj.art-f62067cb67a74431b747e6d60d831c9d2023-05-06T04:27:28ZengLembaga Penelitian dan Pengabdian kepada MasyarakatRekayasa0216-94952502-53252023-04-01161657310.21107/rekayasa.v16i1.169747363Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural NetworkPurnawarman Musa0Wahid Khairul Anam1Saiful Bahri Musa2Witari Aryunani3Remi Senjaya4Puji Sularsih5Gunadarma UniversityUniversitas GunadarmaPoliteknik GorontaloUniversitas GunadarmaUniversitas GunadarmaUniversitas GunadarmaDeep learning is a neural network that creates innovations that give computer-implanted problem-solving expertise. One of the principles of computer vision is a detection system with a vision framework that can identify things encountered in the same manner as a human vision system. Using an artificial intelligence-based Convolutional Neural Network (CNN) model with deep learning techniques, we present a face emotion identification system. The categorization of facial expressions will be utilized as the basis for a face recognition system trained using CNN. The applications are intended to use the OpenCV, Keras, and TensorFlow libraries as the backend. We were discussing the study on the best use of xception architectural models in facial expression recognition systems. Based on the results of these tests, the study obtained an increased accuracy value in training and data testing on an xception architecture model trained for facial expressions using the FER-2013 dataset, resulting in an accuracy value of 66% as well as the value of each average for precision (76%), recall (65%), and F1 score (63%).https://journal.trunojoyo.ac.id/rekayasa/article/view/16974face detection, artificial intelligence, deep learning, facial expressions |
| spellingShingle | Purnawarman Musa Wahid Khairul Anam Saiful Bahri Musa Witari Aryunani Remi Senjaya Puji Sularsih Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network Rekayasa face detection, artificial intelligence, deep learning, facial expressions |
| title | Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network |
| title_full | Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network |
| title_fullStr | Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network |
| title_full_unstemmed | Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network |
| title_short | Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network |
| title_sort | pembelajaran mendalam pengklasifikasi ekspresi wajah manusia dengan model arsitektur xception pada metode convolutional neural network |
| topic | face detection, artificial intelligence, deep learning, facial expressions |
| url | https://journal.trunojoyo.ac.id/rekayasa/article/view/16974 |
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