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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Happy Alyzhya Haay
Master of Data Science Study Program, Department of Mathematics and Data Science, Faculty of Science and Mathematics, Satya Wacana Christian University, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK Happy Alyzhya Haay; Suryasatriya Trihandaru; Bambang Susanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.962 KB) | DOI: 10.30598/barekengvol17iss1pp0211-0224

Abstract

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.
Co-Authors Bambang Susanto Suryasatriya Trihandaru