Klasifikasi Kardus Barang di PT XYZ Menggunakan Convolutional Neural Network dengan Pendekatan Fine Grained Image Classification
DOI:
https://doi.org/10.35870/jtik.v8i4.2337Keywords:
Convolutional Neural Network, Fine Grained Image Classification, ResNet ArchitectureAbstract
PT XYZ requires a system to automatically validate items in storage with the system. Cardboard boxes containing items exhibit high visual similarity within a specific sub-category. Several studies have demonstrated the use of Convolutional Neural Network (CNN) as a method for image classification with a Fine Grained Image Classification (FGIC) approach for classifying data with high similarity, resulting in good accuracy, and this will be applied in this research. The ResNet architecture is used with and without ImageNet weight initialization, combined with the RMCSAM architecture, resulting in eight training configurations. Based on testing results using 172 images across 14 classes, the ResNet + RMCSAM configuration with ImageNet weight initialization and the 20 times augmentation dataset achieves the highest accuracy compared to other configurations, with an accuracy of 99.42% and a loss of 0.0004. This configuration is utilized for cardboard classification in the PT XYZ warehouse.
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References
Rahardjo, B. (2017). Perancangan sistem manajemen gudang material penunjang di PT XYZ. J@ ti Undip: Jurnal Teknik Industri, 12(2), 127-136.
Senduk, H. Y., & Sitokdana, M. N. (2022). Perancangan Sistem Informasi Pencatatan Gudang Berbasis Website (Studi Kasus Slingbag Salatiga). JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 373-383. DOI: https://doi.org/10.35957/jatisi.v9i1.1503.
Liu, D., Wang, Y., Mase, K., & Kato, J. (2022). Recursive multi-scale channel-spatial attention for fine-grained image classification. IEICE TRANSACTIONS on Information and Systems, 105(3), 713-726. DOI: 10.1587/transinf.2021EDP7166.
Radikto, R., Mulyana, D. I., Rofik, M. A., & Zakaria, M. O. Z. (2022). Klasifikasi Kendaraan pada Jalan Raya menggunakan Algoritma Convolutional Neural Network (CNN). Jurnal Pendidikan Tambusai, 6(1), 1668-1679. DOI: https://doi.org/10.31004/jptam.v6i1.3179.
Sholeh, I., & Wiyono, D. A. (2023). Model Klasifikasi Terumbu Karang Menggunakan Metode Convolutional Neural Network. DoubleClick: Journal of Computer and Information Technology, 6(2), 77-81. DOI: http://doi.org/10.25273/doubleclick.v6i2.13901.
Nata, I. D., Sulistianingsih, N., & Yusuf, S. A. A. (2023). Implementasi Deep Learning Dalam Klasifikasi Citra Gambar Dengan Menggunakan Metode CNN.
Harahap, M., Laia, E. M., Sitanggang, L. S., Sinaga, M., Sihombing, D. F., & Husein, A. M. (2022). Deteksi penyakit covid-19 pada citra x-ray dengan pendekatan convolutional neural network (cnn). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 70-77. DOI: https://doi.org/10.29207/resti.v6i1.3373.
Wei, X. S., Song, Y. Z., Mac Aodha, O., Wu, J., Peng, Y., Tang, J., ... & Belongie, S. (2021). Fine-grained image analysis with deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(12), 8927-8948. DOI: 10.1109/TPAMI.2021.3126648.
Andrade, C. (2021). The inconvenient truth about convenience and purposive samples. Indian journal of psychological medicine, 43(1), 86-88. DOI: https://doi.org/10.1177/0253717620977000.
Sanjaya, J., & Ayub, M. (2020). Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup. Jurnal Teknik Informatika dan Sistem Informasi, 6(2). DOI: https://doi.org/10.28932/jutisi.v6i2.2688.
Krishna, S. T., & Kalluri, H. K. (2019). Deep learning and transfer learning approaches for image classification. International Journal of Recent Technology and Engineering (IJRTE), 7(5S4), 427-432.
Wightman, R., Touvron, H., & Jégou, H. (2021). Resnet strikes back: An improved training procedure in timm. arXiv preprint arXiv:2110.00476.
Arieska, P. K., & Herdiani, N. (2018). Pemilihan teknik sampling berdasarkan perhitungan efisiensi relatif. Jurnal Statistika Universitas Muhammadiyah Semarang, 6(2). DOI: https://doi.org/10.26714/jsunimus.6.2.2018.%25p.
Zuo, X., Yang, X., Dou, Z., & Wen, J. R. (2019). RUCIR at TREC 2019: Conversational Assistance Track. In TREC.
Zhou, T., Zhao, Y., & Wu, J. (2021, January). Resnext and res2net structures for speaker verification. In 2021 IEEE Spoken Language Technology Workshop (SLT) (pp. 301-307). IEEE. DOI: 10.1109/SLT48900.2021.9383531.
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