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All Journal Jurnal Teknik Informatika (JUTIF) Green Intelligent Systems and Applications JESCEE- Journal of Emerging Supply Chain, Clean Energy, and Process Engineering
Pratama, Dhika Wahyu
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Classification of Metal Surface Defects Using Convolutional Neural Networks (CNN) Pratama, Dhika Wahyu; Ismail, Muchammad; Nurraudah, Restu; Rifai, Achmad Pratama; Nguyen , Huu Tho
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.581

Abstract

Metal surface quality inspection is an important step in ensuring that products meet predetermined industry standards. The manual methods used were often slow and prone to errors, so more efficient solutions were needed. The application of Machine Learning (ML)-based technologies, especially Convolutional Neural Networks (CNN), offered an innovative approach to overcome these challenges. CNN had the ability to automatically extract visual features from images with high accuracy, making it an effective tool in defect classification. This research used several CNN architectures, including MobileNetV2 and InceptionV3, as well as a model developed in-house, the K3 Model. Data augmentation, such as rotation and lighting adjustments, was applied to increase variation in the dataset and aid the model in generalization. The research results showed that the K3+Augmentation model achieved the highest accuracy of 100% in testing, with a very low loss of 0.0009. While MobileNetV2 offered better training speed, K3+Augmentation showed superior performance in detecting and classifying metal defects. These findings confirmed the potential of CNN in improving the efficiency of quality inspection in modern industry.
Temperature Control using PI Controller Wahyudi, Muhammad Zidane; Pratama, Dhika Wahyu; Fitrian, Ansya; Abdillah, Muhammad; Setiadi, Herlambang
Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Vol 1 No 1 (2022): Journal of Emerging Supply Chain, Clean Energy, and Process Engineering
Publisher : Universitas Pertamina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57102/jescee.v1i1.8

Abstract

Indonesia is a large archipelago with a tropical climate consisting of dry and wet seasons. Indonesia has had high rainfall and temperature over the year because this country lies on the equator lines. Moreover, severe global warming occurs because of the depletion of the ozone which affects the inclement weather, air, and temperature over the years. Therefore, special equipment is required to obtain appropriate thermal conditions by controlling the temperature. This paper proposed the PI controller to maintain the temperature in their nominal values and its temperature stability is analyzed using pole placement. In this study, the system model is 1st order, called first order plus dead time (FOPDT). Pole placement is utilized to improve the output signal to obtain the gain of the PI controller. The gain of the PI controller obtained is Kp as 0.36095 and Ki as I as of 0.00072231. The percentages of overshoot and steady-state error are 29.98% and 1.5% for the Ziegler Nichols method while 1.28% and 0.26% for the PI Tunner, respectively. PI controller is robust for this system where the pole's position is on the left side of the real axis and has small values of overshoot and steady-state error.
Multi-architectural Transfer Learning CNN for Klowong Batik Fabric Defect Classification Pratama, Dhika Wahyu; Sudiarso, Andi; Atmaja, Denny Sukma Eka; Herliansyah, Muhammad Kusumawan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4806

Abstract

Klowong is a base cloth that has been given a hot wax pattern as the initial stage in the batik making process but has not yet become a finished batik. Nowdays, written batik machine are available but still limited and production defects still occur, reducing the value of batik. Manual QC makes subjective assessments, so an accurate and efficient automated inspection system is needed for SMEs.This study proposes a defect classification approach on batik klowong fabric based on transfer learning using deep convolutional neural networks (CNN) architecture that has been verified to be reliable in image classification schemes. The basic models used include VGG16, ResNet50V2, InceptionV3, and MobileNetV2, with modifications to the fully connected layers to reduce parameter complexity. The dataset consists of 1000 klowong fabric images with a resolution of 224×224 pixels, with a ratio of 80:10:10 for training, validation, and testing. Data augmentation was applied to improve the generalization of the model. Evaluation is performed based on accuracy, precision, recall, F1-score, and inference time. The experimental results show that VGG16 has the best performance in the testing stage with 92% accuracy. The combination of VGG16 with conventional classifiers (SVM and Random Forest) significantly speeds up the inference time (up to 0.0001 seconds per image) but with a decrease in accuracy to 81-83%. Therefore, the VGG16 model with the modified final layer is recommended as the optimal solution with the best trade-off between classification performance and computational efficiency, especially for application scenarios on low-resource devices such as batik SMEs.
Co-Authors Achmad Pratama Rifai Andi Sudiarso, Andi Denny Sukma Eka Atmaja Fitrian, Ansya Herlambang Setiadi Muchammad Ismail Muhammad Abdillah Muhammad Kusumawan Herliansyah Nguyen , Huu Tho Nurraudah, Restu Wahyudi, Muhammad Zidane