ANALISIS PENGARUH TINGKAT AKURASI KLASIFIKASI CITRA MEBEL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.23960/jitet.v13i2.6199Abstract Views: 207 File Views: 126
Abstract
Furniture is a household necessity that serves to complement a room. The design models of furniture are varied, ranging from general or mass-market designs to those made for specific needs. Sofas, tables, chairs, swivel chairs, TV cabinets, wardrobes, beds, and bookshelves are the items analyzed in this study. The fact that there are always more and better furniture models available is one of the reasons the researcher chose these furniture items. The goal of furniture object classification is to help categorize objects automatically, making it easier for users to quickly search for the furniture products they want, and the system can recommend items based on the classification results. This study uses 3 architectures with 3 testing scenarios. The architectures used are MobileNetV1, ResNet-50, and VGGNet-19. Scenario S1 uses image dimensions of 128x128 with 50 epochs, scenario S2 uses image dimensions of 128x128 with 100 epochs, and scenario S3 uses image dimensions of 128x128 + grayscale with 50 epochs. The accuracy results are differentiated according to the scenarios used, namely S1, S2, and S3. The detailed accuracy results are as follows: Scenario S1 using the MobileNetV1 architecture achieved an accuracy of 94.31%. The highest accuracy in scenario S2 was also achieved with the MobileNetV1 architecture at 94.31%. For the highest accuracy in scenario S3, MobileNetV1 achieved an accuracy of 72.61%. The fastest computation time for S1 was 871.97 seconds, for S2 it was 1763.04 seconds, and for S3 it was 436.32 seconds. Among the three architectures used, MobileNetV1 stands out as the best architecture in terms of accuracy and classification speed in this studyDownloads
References
A Yusupa, “Pengembangan Augmented Reality Marketing (ARM) Menggunakan Algoritma Fast Corner Sebagai Media Promosi Produk Furniture Pelaku Usaha”, JIP, vol. 10, no. 1, Dec. 2023.
N. Nurdin, and K. Pratama. “Klasifikasi Kecantikan Wanita Aceh Pada Citra Menggunakan Metode Adaptive ResonanceTheory (ART1)”, Jurnal Techsi, vol. 8, no. 1, 2016.
H. A. Pratiwi, M. Cahyanti, and M. Lamsani, “Implementasi Deep Learning Flower Scanner Menggunakan Metode Convolutional Neural Network”, Sebatik, vol. 25, no. 1, pp. 124–130, Jun. 2021.
E. Susanto, “Analisis Implementasi Kecerdasan Buatan Dalam Pembelajaran”, Sindoro, vol. 1, no. 8, pp. 91–100, Dec. 2023.
P. Dönmez, “Introduction to Machine Learning, 2nd ed., by Ethem Alpaydın. Cambridge, MA: The MIT Press2010. ISBN: 978-0-262-01243-0. $54/£ 39.95 + 584 pages.,” Natural Language Engineering, vol. 19, no. 2, pp. 285–288, 2013. doi:10.1017/S1351324912000290
U. P. Sanjaya, Z. Alawi, A. R. Zayn, and G. P. Dirgantoro, “Optimasi Convolutional Neural Network dengan Standard Deviasi untuk Klasifikasi Pneumonia pada Citra X-rays Paru”, Generation Journal, vol. 7, no. 3, pp.40–47, 2023.
A Rohim, Y. A. Sari, and T. Tibyani, “Convolution Neural Network (CNN) Untuk Pengklasifikasian Citra Makanan Tradisional”, J-PTIIK, vol. 3, no. 7, pp. 7037–7042, 2019.
S. S. Muna, N. Nurdin, and T. Taufiq, Comparative Analysis of State Universities on Website Performance in Aceh Using the PIECES Method,” Journal of Informatics and Telecommunication Engineering, vol. 7., no.1, pp. 71-83, 2023.
W. Saputra, and N. Nurdin, ”Strategi perencanaan sistem informasi dalam rangka peningkatan layanan perpustakaan berbasis digital menggunakan tahapan anita Cassidy,” Jurnal Informatika dan Teknik Elektro Terapan, vol.12, no.3, pp. 461-4647, 2024.
S. Salimuddin, and N. Nurdin, “Implementasi platform e-commerce untuk home industri di Aceh dengan basis web menggunakan framework dan bootstrap,” Jurnal Sains Riset, vol. 13, no.2, pp. 577-582, 2024.
N. Siregar, and N. Nurdin, “Analisis tingkat kepuasan mahasiswa terhadap pelayanan sistem informasi perpustakaan universitas malikussaleh menggunakan metode fuzzy service quality, Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no.3S1, pp. 4660-4673, 2024.
M. Wasil, H. Harianto, and F. Fathurrahman, “Pengaruh epoch pada akurasi menggunakan convolutional neural network untuk klasifikasi fashion dan furniture”, Infotek: Jurnal Informatika dan Teknologi, vol. 5, no.1, pp.53-61, 2022.
J. Alberto, and D. Hermanto, “Bird Species Classification Using CNN Method and ResNet-50 Architecture”, JUTISI (Jurnal Teknik Informatika Dan Sistem Informasi), vol. 10, no.3, pp. 34-46, 2023.
J. Yuwan, A. M. Ihsan, and B. Priambodo, "Klasifikasi Jenis Buah-Buahan Menggunakan Citra Digital Dengan Metode Convolutional Neural Networks”, KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no.3, pp.1737-1746, 2023.
C. R. Gunawan, N. Nurdin, and F. Fajriana, “Deteksi Ikan Segar Secara Realtime dengan YOLOv4 menggunakan Metode Convolutional Neural Network”, Jurnal Komtika (Komputasi dan Informatika), vol. 7, no.1, pp.1-11, 2023)
C. R. Gunawan, N. Nurdin, and F. Fajriana, “Acehnese Traditional Clothing Recognition Prototype System Design Based on Augmented Reality”, International Journal of Engineering, Science and Information Technology, vol. 2, no.3, pp. 100-105, 2022.
