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All Journal Civis: Jurnal Ilmiah Ilmu Sosial dan Pendidikan Indonesian Journal of Artificial Intelligence and Data Mining Edumaspul: Jurnal Pendidikan Jambura Law Review Academy of Education Journal PKn Progresif : Jurnal Pemikiran dan Penelitian Kewarganegaraan Jurnal Pendidikan PKn Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Pendidikan Kewarganegaraan dan Politik Proceeding of International Conference on Humanity Education and Society Scientific Journal of Informatics Al-Mabsut: Jurnal Studi Islam dan Sosial
Suyahman Suyahman
Master Program Of Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia, 55166
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection Suyahman, Suyahman; Sunardi, Sunardi; Murinto, Murinto; Khusna, Arfiani Nur
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.28804

Abstract

The detection and acknowledgment of logos holds significant importance in the corporate sphere, facilitating the detection of unauthorized logo usage and ensuring trademark uniqueness within specific industry sectors. Presently, convolutional neural networks powered by deep learning are widely utilized for image recognition. However, their effectiveness is dependent on a substantial volume of training images which may not always be readily available. This study suggests employing Test Time Augmentation to address dataset constraints by expanding the original dataset, thereby enhancing classification accuracy and preventing overfitting. Test-Time Augmentation is a method used to improve the accuracy of convolutional neural networks by creating numerous augmented variations of the test images and then merging their predictions. The research findings indicate that the application of TTA has the highest performance on the VGG16 model with 98% precision, 99% recall, and 98% F1-score, and 98.87% accuracy
Co-Authors Albadri Albadri Arfiani Nur Khusna Arfiani Nur Khusna Ika Murtiningsih Ika Murtiningsih Karimullah, Suud Sarim Mohammad Edy Nurtamam Muh. Akbar Fhad Syahril Murinto Murinto Murinto Normansyah Oktaviana Dita Prasticha Qaedi Zihni, Faris Hazmi Ranat Mulia Pardede Siti Fatimah Sumardi, Mar'ah Sholikhah Sunardi Sunardi Sunardi Sutarto Sutarto Tomi Apra Santosa Vinsensius Singgih Wahyu Putri Hasanah Waskito, Yohanes Saing