Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Kecacatan Pada Proses Welding di Perusahaan Manufacturing
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Nandang Saefulloh
*
Universitas Buana Perjuangan, Karawang,
Indonesia
- Jamaludin Indra Universitas Buana Perjuangan, Karawang, Indonesia
- Rahmat Rahmat Universitas Buana Perjuangan, Karawang, Indonesia
- Ayu Ratna Juwita Ayu Ratna Juwita, Indonesia (*) Corresponding Author
Abstract
Manufacturing industry has become one of the largest sectors in Indonesia, driven by increasing demand from the public. A primary concern to meet both local and international market needs is product quality. In ensuring high-quality standards, production processes require strict quality control. One common issue in quality control is defects occurring during the welding process, which significantly affects inspection cycle times. To address this, the Convolutional Neural Network (CNN) approach with VGG-16 architecture can help classify defects in the welding process. This method not only expedites the defect classification process but also enhances the accuracy of identifying product defects. The stages of implementing this method include dataset preparation, data preprocessing, CNN model design, model training, and performance evaluation. Evaluation results demonstrate that the use of automatic defect detection technology, especially with balanced data scenarios, can significantly improve quality control performance. Accuracy, precision, recall, and F1-score achieve excellent levels, reaching 92%. Thus, this research provides a significant contribution to enhancing production efficiency and improving product quality in the motorcycle manufacturing industry in Indonesia. It is hoped that the use of this technology will assist manufacturing companies in identifying and addressing production defects more effectively, thereby enhancing the overall competitiveness of Indonesia's manufacturing industry.
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References
J. Aprilion and W. Simamora, “SISTEM PAKAR MENDETEKSI KERUSAKAN SEPEDA MOTOR N-MAX MENGGUNAKAN METODE FORWARD CHAINING BERBASIS ANDROID Expert System To Detect Damage To N-Max Motorbikes Using Forward chaining Method Based on Android,” 2021.
D. Raya, A. Yunan, and R. I. Rosihan, “Analisis Upaya Menurunkan Cacat Produk Crank Case LH pada Proses Die Casting dengan Metode PDCA dan FMEA di PT. Suzuki Indo Mobil/Motor,” 2020.
A. S. Putra and P. Yuliarty, “PEMBUATAN PROGRAM MACHINING TIME GUARD MESIN CNC UNTUK MENJAGA CYCLE TIME MESIN PADA PROSES PRODUKSI SEPEDA MOTOR MATIC,” vol. XVI, no. 1, pp. 101–115, 2022.
K. Lee, S. Yi, S. Hyun, and C. Kim, “Review on the Recent Welding Research with Application of CNN-Based Deep Learning Part I: Models and Applications,” Journal of Welding and Joining, vol. 39, no. 1, pp. 10–19, Feb. 2021, doi: 10.5781/jwj.2021.39.1.1.
M. S. H. Nizam, S. Marizan, S. A. Zaki, and A. R. Mohd Zamzuri, “Vision based identification and classification of weld defects in welding environments: A review,” Indian Journal of Science and Technology, vol. 9, no. 20. Indian Society for Education and Environment, May 01, 2016. doi: 10.17485/ijst/2016/v9i20/82779.
R. Juniardo, L. Tobing, and H. H. Azwir, “PENINGKATAN PRODUKTIVITAS PENGECEKAN QUALITY CONTROL PART BRACKET D14N DENGAN PERANCANGAN JIG DI PT. TIANURA TEKNO KARYA,” 2020.
Erifive. Pranatal, “‘Analisa Cacat Pengelasan Smaw Pada Posisi 2G Pada Baja Material a36 Dengan Variasi Arus Dan Sudut Pengelasan.,’” Jurnal Sumberdaya Bumi Berkelanjutan (SEMITAN) 1.1 : 257-263., 2022.
A. Ludfi and A. Shidiq, “Klasifikasi Kecacatan Ban Untuk Mengendalikan Kualitas Produk Menggunakan Model CNN Dengan Arsitektur VGG-16 Classification Of Tire Defect To Control Product Quality Using Cnn Model With VGG-16 Architecture,” 2022. [Online]. Available: www.kaggle.com.
F. Nurona Cahya et al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN),” 2021. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
A. M. Rizki and N. Marina, “KLASIFIKASI KERUSAKAN BANGUNAN SEKOLAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN PRE-TRAINED MODEL VGG-16,” Jurnal Ilmiah Teknologi dan Rekayasa, vol. 24, no. 3, pp. 197–206, 2021, doi: 10.35760/tr.2019.v24i3.2396.
S. Kumaresan, K. S. J. Aultrin, S. S. Kumar, and M. D. Anand, “Transfer Learning with CNN for Classification of Weld Defect,” IEEE Access, vol. 9, pp. 95097–95108, 2021, doi: 10.1109/ACCESS.2021.3093487.
S. J. Oh, M. J. Jung, C. Lim, and S. C. Shin, “Automatic detection of welding defects using faster R-CNN,” Applied Sciences (Switzerland), vol. 10, no. 23, pp. 1–10, Dec. 2020, doi: 10.3390/app10238629.
Ety Sutanty, Maukar, Dina Kusuma Astuti, and Handayani, “Penerapan Model Arsitektur VGG16 Untuk Klasifikasi Jenis Sampah,” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 3, no. 2, pp. 407–419, Sep. 2023, doi: 10.51454/decode.v3i2.331.
H. Pangestu Rahman and J. Indra, “Penerapan Convolutional Neural Network pada Timbangan Pintar Menggunakan ESP32-CAM,” vol. 7, no. 1, pp. 283–291, 2023, doi: 10.30865/mib.v7i1.5469.
A. Kirana and H. Hikmayanti, “Pengenalan Pola Aksara Sunda dengan Metode Convolutional Neural Network,” vol. 1, no. 2, 2020.
N. Fadlia and R. Kosasih, “KLASIFIKASI JENIS KENDARAAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN),” Jurnal Ilmiah Teknologi dan Rekayasa, vol. 24, no. 3, pp. 207–215, 2021, doi: 10.35760/tr.2019.v24i3.2397.
D. Indarti, “Pengendalian Kualitas Produksi Lembaran Baja Melalui Klasifikasi Jenis Cacat Permukaan Menggunakan CNN,” Jurnal Rekayasa Sistem Industri, vol. 12, no. 2, pp. 165–172, Oct. 2023, doi: 10.26593/jrsi.v12i2.5862.165-172.
Birawa Kaca Buana Gora, Nugroho Tegar Maulana, Muhamad Novan Aulia Zam Zami, Anan Nugroho, and Alfa Faridh Suni, “Deteksi Ketahanan Ban Sepeda Motor berdasarkan Indeks Beban Ban menggunakan CNN,” Jurnal Komputer dan Teknologi Informasi, 2023, [Online]. Available: http://e-journal.unipma.ac.id/index.php/doubleclick
A. H. Nasrullah, “IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI PRODUK LARIS,” vol. 7, no. 2, 2021, [Online]. Available: http://ejournal.fikom-unasman.ac.id
H. S. Siska Febriani1, “ANALISIS DATA HASIL DIAGNOSA UNTUK KLASIFIKASI GANGGUAN KEPRIBADIAN MENGGUNAKAN ALGORITMA C4.5,” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 2, no. 4, pp. 89–95, 2021.
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