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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal Komunikasi Jurnal Pensil : Pendidikan Teknik Sipil Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Infotech: Journal of Technology Information Humantech : Jurnal Ilmiah Multidisiplin Indonesia International Journal of Application on Economics and Business
Dyah Erny Herwindiati
Faculty of Information Technolgy Tarumanagara University
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Implementasi Algoritma Convolutional Neural Networks Untuk Klasifikasi Jenis Cat Tembok Menggunakan Arsitektur MobileNet Carlos, Daniel; Herwindiati, Dyah Erny; Lubis, Chairisni
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5322

Abstract

The development of image recognition technology has made significant advancements, particularly with the emergence of Convolutional Neural Networks (CNN) algorithms. One of the CNN architectures that is efficient and effective for mobile devices is MobileNet. This study aims to implement the CNN algorithm using the MobileNet architecture for classifying types of wall paint. The main problem addressed is the accurate identification of wall paint types based on images, requiring a model that performs well even on devices with limited resources. MobileNet was chosen as the solution due to its ability to reduce computational complexity without sacrificing performance. The methodology used in this research involves two approaches: classification with feature extraction using GLCM and histogram, and classification without feature extraction directly using MobileNet. The training and testing process was conducted using the early stopping technique to prevent overfitting, with the model trained for 50 epochs. The final results show that classification without feature extraction using MobileNet yields excellent results. The model achieved a training accuracy of 89.68% and a testing accuracy of 88.86%, with low loss values (0.0111 for training and 0.0117 for testing). These results indicate that MobileNet is effective in recognizing and classifying types of wall paint and can operate efficiently on devices with limited resources. Therefore, this research demonstrates that using the MobileNet architecture for classifying wall paint types is an effective and efficient solution, opening opportunities for similar applications on various mobile devices in the future.
Co-Authors Andri Muliawan Angel Angel Budianto, Willson Carlos, Daniel Chairisni Lubis Denny, Mandy Sandra Djoenaedi, Owen Ferdinand, Kelvin Gozali, Carisha Puspa Halim, Henry Handoyo, Rico Hendryli, Janson Karendef Karendef Lely Hiryanto Martha, Regina Muhammad Idrus Taba Nagaputra, Hongi Novario Jaya Perdana Otto Randa Payangan Pamungkas, Ary Satria Sani M. Isa Sari, Endah Murtiana Soelaiman, Lydiawati Stephen Christian, Stephen Sukamdani, Ryan Rici Suluh, David Susilo, Venezia Valen Teny Handhayani Tony Tony TRI SUTRISNO Wenda Kartika, Wenda Yasmin, Yamani