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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Anwar Hidayat
Politeknik ATK Yogyakarta, Yogyakarta
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Deteksi Defect Sepatu Berbasis Fine-Tuned CNN Menggunakan Arsitektur DenseNet untuk Mendukung Proses Quality Control Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Nunik Purwaningsih; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin; Alifia Revan Prananda
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.9712

Abstract

Indonesia is a leading global exporter of footwear. Despite successfully exports million pair of shoes, shoes industry in Indonesia faces significant challenges due to conventional quality control (QC) limitations. This study proposes a deep learning-based defect detection model to enhance the efficiency and accuracy of the QC process. This study uses primary datasets collected from Laboratory of PIDI 4.0 Politeknik ATK Yogyakarta and employed in four experimental scenarios. The dataset consists of 2,828 images comprising of two classes (good class and reject class). This study utilizes fine-tuned CNN using DenseNet architecture for distinguishing two classes. Before employing the fine-tuned CNN method, we separate the data into three components, which are training, validation and testing data, with proportion of 80:10:10. The results demonstrate that the integration of data augmentation and the Adam optimizer yielded the highest performance, achieving accuracy, precision, recall and F1-score of 0.9546, 0.9551, 0.9546, 0.9545, respectively. These findings suggest that implementing automated deep learning models has potential to reduce rejection rates by modernizing traditional inspection methods, which can strengthen the competitiveness of Indonesia’s footwear industry in the global market.
Co-Authors Alifia Revan Prananda Eka Legya Frannita Erlita Pramitaningrum Gilang Fatikhul Burhan Jamila Jamila Nunik Purwaningsih Totok Yulaidin Windra Bangun Nuswantoro