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Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)

E-ISSN: 2580-1643 | P-ISSN:

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

Issue: Vol. 8 No. 4 (2024): OCTOBER-DECEMBER 2024, Pages: 954-964, Section: Computer & Communication Science

Alief Yuwastika Firmandicky (1) , Yeremia Alfa Susetyo (2)

Published: Oct 1, 2024

Show Affiliations

(1) Alief Yuwastika Firmandicky:

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

(2) Yeremia Alfa Susetyo:

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

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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How to Cite
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
Author Biographies

Alief Yuwastika Firmandicky, Universitas Kristen Satya Wacana

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

Yeremia Alfa Susetyo, Universitas Kristen Satya Wacana

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

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