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All Journal Jurnal Pseudocode IT JOURNAL RESEARCH AND DEVELOPMENT JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Industrika : Jurnal Ilmiah Teknik Industri Jurnal Sains dan Teknologi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Dinamika Kerajinan dan Batik: Majalah Ilmiah Industrial Innovation
Eka Legya Frannita
Department Of Leather Product Processing Technology, Politeknik ATK Yogyakarta
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Pengendalian Kualitas Menggunakan Statistical Quality Control: Studi Kasus pada Proses Produksi Sepatu di PT. ABC Burhan, Gilang Fatikhul; Frannita, Eka Legya
Industrika : Jurnal Ilmiah Teknik Industri Vol. 10 No. 2 (2026): Industrika: Jurnal Ilmiah Teknik Industri
Publisher : Fakultas Teknik Universitas Tulang Bawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37090/qmv0q872

Abstract

The quality of footwear products such as shoes is a major factor that influences the success of a company in producing footwear products. Shoe companies usually have various quality control methods to maintain the production process. One of the most widely used methods in the industry is the statistical quality control method. PT ABC is a company that produces sports shoes located in Bandung. ABC Company is experiencing problems in terms of production, namely the number of defective products produced is still high. This study aims to apply statistical quality control to control the number of defects that appear in the production process. The application process is focused on the problems that arise in the screen printing division where there are many defects that appear during the production process. The steps taken in this study include data preparation and initial analysis using check sheets and Pareto diagrams, data adequacy tests, making p control charts, and analyzing results. Based on the control chart that has been made on the initial defect proportion and the proportion of defective data after improvements have been made to the screen printing process, it can be concluded that there is a decrease in the average value of the defect proportion of 0.02. This figure is quite significant. In addition, there was a drastic decrease in the LCL (lower control limit) value. This significant decrease is a positive indication that the improvements made to the screen printing process have succeeded in reducing product defects significantly. Keywords: Control Chart, Quality Control, Shoes Defect, Statistical Quality Control
Modeling of women shoes sizing system based on 3D foot scanner result using machine learning approach Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin
Industrial Innovation Vol. 2 No. 2 (2025): Industrial Innovation
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58533/aqn1f925

Abstract

Accurate shoe sizing plays a crucial role in ensuring comfort, performance, and consumer satisfaction, particularly for women whose foot shapes exhibit considerable anatomical variability. To address this challenge, this research proposes a data-driven modeling framework for developing a women’s shoe sizing system based on three-dimensional foot scanner data. The study was carried out through a systematic process consisting of data preprocessing, clustering using the K-Means algorithm, and evaluation of the clustering performance. The clustering analysis identified four optimal clusters within the dataset, representing distinct patterns in foot dimension measurements. The evaluation result, with a Silhouette Score of 0.25, indicates a moderate yet acceptable level of cohesion and separation among the clusters. These findings demonstrate that the proposed model can effectively capture the underlying structure of women’s foot morphology, providing a scientific foundation for establishing more accurate, customized, and ergonomically appropriate shoe sizing standards.
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 Abdillah, Mokhammad Nurkholis Alifia Revan Prananda Anwar Hidayat Anwar Hidayat Anwar Hidayat Bagus Fatkhurrozi Burhan, Gilang Fatikhul Dwi Wulandari Erlita Pramitaningrum Fauzi Ashari Gilang Fatikhul Burhan Gilang Fatikhul Burhan Hidayat, Anwar Jamila Jamila Jamila Jamila Latifah Listyalina Majidah, Zharifa Nur Marwanto Marwanto, Marwanto Mochammad Charis Hidayahtullah Mochammad Charis Hidayatullah Naimah Putri Nunik Purwaningsih Oktavian, Dicky Pramitaningrum, Erlita Pramitaningrum, Erlita Pramitaningrum Prihadianto, Braam Delfian Purwaningsih , Nunik Rahmawati, Atiqa Setiawan , Wawan Budi Setyawan, Wawan Budi Suamanda Ika Novichasari Sugiyanto Totok Yulaidin Totok Yulaidin Utanto, Taufik Rudhi Wahyu Ratnaningsih Wawan Budi Setyawan Wibowo, Fadhila Syahida Windra Bangun Nuswantoro Windra Bangun Nuswantoro Yuafni