Klasifikasi Hasil Potret pada Cacat Produk Spring Washer menggunakan Metode Transfer Learning Convolutional Neural Network
DOI:
https://doi.org/10.22441/jitkom.v9i1.003Kata Kunci:
Spring Washer, Korosi, Convolutional Neural Network (CNN), MobileNet, XceptionAbstrak
Spring Washer merupakan komponen yang dapat digunakan untuk pengencang berulir. Spring Washer terbuat dari bahan logam lalu dibentuk ring bulat. Dalam Quality Prosedur Perusahaan produk yang diproduksi harus dicek kualitasnya agar tidak ada kecacatan produk terkirim ke pelanggan. Kecacatan pada produk berbahan logam diantaranya adalah korosi pada produk. Pengecekan Spring Washer biasanya dilakukan secara visual manual oleh karyawan, hal ini memerlukan waktu yang tidak sebentar jika yang dicek memiliki kuantitas yang banyak. Dengan adanya Convolutional Neural Network (CNN) dilakukanya perancangan untuk mengklasifikasikan Spring Washer berkualitas baik dan korosi. Dalam penelitian ini dilakukan pembentukan sistem dengan Tiga model transfer learning CNN yaitu VGG16, MobileNet dan Xception. Pembentukan algoritma sistem menggunakan Google Colaboration dengan Bahasa pemrograman Phyton. Dataset diambil dari gambar produk Spring Washer sebanyak 1048 foto dengan keadaan yang berbeda, yaitu Spring Washer Bagus atau Korosi.
Referensi
M. Tegar and E. Sutoyo, "analisis kegagalan spring washer material sk-5 lapisan electroplating zinc", Ame (Aplikasi Mekanika Dan Energi) Jurnal Ilmiah Teknik Mesin, vol. 5, no. 2, p. 53, 2019. https://doi.org/10.32832/ame.v5i2.2471.
G. Priyotomo, ”Buku Praktis Korosi Dan Logam Untuk Mahasiswa”, nulisbuku.com, 2015
R. Rismiyati and A. Luthfiarta, “VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification,” Telematika, vol. 18, no. 1, p. 37, Mar. 2021, doi: https://doi.org/10.31315/telematika.v18i1.4025.
Y. Tian, G. Zhang, J. Ma, & S. Ma, "Automated rust detection via digital image recognition during grinding work process", 2018 IEEE International Conference on Information and Automation (ICIA), p. 318-323, 2018. https://doi.org/10.1109/icinfa.2018.8812345
A. Fujishiro, Y. Nagamura, T. Usami, & M. Inoue, "Minimization of cnn training data by using data augmentation for inline defect classification", 2020 International Symposium on Semiconductor Manufacturing (ISSM), p. 1-4, 2020. https://doi.org/10.1109/issm51728.2020.9377504
Y. Tian, G. Zhang, J. Ma, & S. Ma, "Automated rust detection via digital image recognition during grinding work process", 2018 IEEE International Conference on Information and Automation (ICIA), p. 318-323, 2018. https://doi.org/10.1109/icinfa.2018.8812345
E. Prasetyo, R. Purbaningtyas, R. Adityo, E. Prabowo, & A. Ferdiansyah, "perbandingan convolution neural network untuk klasifikasi kesegaran ikan bandeng pada citra mata", Jurnal Teknologi Informasi Dan Ilmu Komputer, vol. 8, no. 3, p. 601, 2021. https://doi.org/10.25126/jtiik.2021834369
Sandhopi, Lukman Zaman P.C.S.W, and Yosi Kristian, “Identification of Jepara Motifs on Carvings by Utilizing Convolutional Neural Network”, Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 9, no. 4, pp. 403-413, Dec. 2020.
M. Alwanda, R. Ramadhan, & D. Alamsyah, "Implementasi metode convolutional neural network menggunakan arsitektur lenet-5 untuk pengenalan doodle", Jurnal Algoritme, vol. 1, no. 1, p. 45-56, 2020. https://doi.org/10.35957/algoritme.v1i1.434
E. Ihsanto, K. Ramli, D. Sudiana, and T. S. Gunawan, “Fast and Accurate Algorithm for ECG Authentication Using Residual Depthwise Separable Convolutional Neural Networks,” Applied Sciences, vol. 10, no. 9, pp. 3304–3304, May 2020, doi: https://doi.org/10.3390/app10093304.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
