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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Faudisyah, Alfendio Alif
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Deteksi Cacat pada Isolasi Trafo Secara Visual menggunakan Algoritma Convolutional Neural Network (CNN) Faudisyah, Alfendio Alif; Hartomo, Kristoko Dwi; Purnomo, Hindriyanto Dwi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1067

Abstract

Transformer insulation is a dielectric material that has the function of selling two or more voltage electrical conductors. Damage to the transformer insulation will cause interference with the performance of the transformer so that it can cause the transformer to experience operational failure or even damage. This research builds a system that can classify defective and normal transformer insulation images. The Convolutional Neural Network method is implemented in model building. The research method begins with conducting research planning, dataset collection, data preprocessing, classification of development models, training models, as well as testing and evaluation. Based on the test results with standardized data size 180 x 180 x 3 pixels, it produces an accuracy of 0.9913 for training, 0.9884 for testing, and 1.00 for evaluation. Test results with standardized data size 240 x 240 x 3 pixels produce an accuracy of 0.9798 for training, 0.9651 for testing, and 0.94 for evaluation. Based on the research that has been done, shows that differences in data standardization can affect the results of the model performance
Co-Authors Hindriyanto Dwi Purnomo Kristoko Dwi Hartomo