Published: 2024-10-01

Klasifikasi Kardus Barang di PT XYZ Menggunakan Convolutional Neural Network dengan Pendekatan Fine Grained Image Classification

DOI: 10.35870/jtik.v8i4.2337

Alief Yuwastika Firmandicky, Yeremia Alfa Susetyo
  • Alief Yuwastika Firmandicky: Satya Wacana Christian University , Indonesia . image/svg+xml
  • Yeremia Alfa Susetyo: Satya Wacana Christian University , Indonesia . image/svg+xml
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Abstract


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.

Keywords

Convolutional Neural Network ; Fine Grained Image Classification ; ResNet Architecture

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Firmandicky, A. Y., & Susetyo, Y. A. (2024). Klasifikasi Kardus Barang di PT XYZ Menggunakan Convolutional Neural Network dengan Pendekatan Fine Grained Image Classification. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(4), 954-964. https://doi.org/10.35870/jtik.v8i4.2337
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This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 8 No. 4 (2024)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2024

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Author Biographies
Alief Yuwastika Firmandicky
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia

Yeremia Alfa Susetyo
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia

References
  • Rahardjo, B. (2017). Perancangan sistem manajemen gudang material penunjang di PT XYZ. J@ ti Undip: Jurnal Teknik Industri, 12(2), 127-136.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nata, I. D., Sulistianingsih, N., & Yusuf, S. A. A. (2023). Implementasi Deep Learning Dalam Klasifikasi Citra Gambar Dengan Menggunakan Metode CNN.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Wightman, R., Touvron, H., & Jégou, H. (2021). Resnet strikes back: An improved training procedure in timm. arXiv preprint arXiv:2110.00476.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zuo, X., Yang, X., Dou, Z., & Wen, J. R. (2019). RUCIR at TREC 2019: Conversational Assistance Track. In TREC.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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