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All Journal REKAYASA Syntax Literate : Jurnal Ilmiah Indonesia Journal of Intelligent Decision Support System (IDSS) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Witari Aryunani
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Social Sciences

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Journal : REKAYASA

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Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network Purnawarman Musa; Wahid Khairul Anam; Saiful Bahri Musa; Witari Aryunani; Remi Senjaya; Puji Sularsih
Rekayasa Vol 16, No 1: April 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v16i1.16974

Abstract

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%).
Co-Authors Apriana Anggraeini Bangun Endah Kurniasari Garin Caesar Syanugiri Kasman Wicaksono Musa, Purnawarman Octarina Budi Lestari Oktaviani Oktaviani Puji Sularsih Remi Senjaya Rosdiana Rosdiana Saiful Bahri Musa Shinta Teviningrum Sigit Sukmono Stevani Adinda Nurul Huda Wahid Khairul Anam Yeni Setiani