Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Journal of Information System and Artificial Intelligence
Khasnur Hidjah
Bumigora University
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN): Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN) Dading Oktaviadi Resmiranta; Bambang Krismono; Khasnur Hidjah
Journal of Information System and Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.271

Abstract

The face is an important object in the biometric identification system. However, low image quality due to uneven lighting, noise, and variations in facial expressions can interfere with the accuracy of the recognition system. The study investigated the use of Convolutional Artificial Neural Network (CNN) combined with hybrid screening techniques to improve image quality, thereby improving the accuracy of facial recognition systems. Filters used include weight mean filtering, median filtering, Contrast-Limited Adaptive Histogram Equalization and gaussian filtering, wavelet filtering. The pre-processed image was then trained using image denoising measurements of the Structural Similarity Index, Mean Squared Error, and Peak Signal to Noise Ratio. The main objective of this study is to evaluate the best filtration combination to produce high accuracy in face classification. The datasets used were 55 classes and 100 images per class. The inceptionV3 architecture model is used for classifications with a number of epochs of 10. Evaluation was carried out on a facial data set with an 80%:20% scheme. The results of the experiment showed that the hybrid method produced the best performance with 94.5% validation accuracy, 94.2% precision, and 94.6% recall, an increase of +1.4% compared to baseline. The (original) baseline itself recorded 93.1% validation accuracy, 92.8% precision, and 93.2% recall. In addition, the loss graph shows that the pre-process model has faster and more stable convergence than the non-pre-processing model. These results confirm that the application of preprocessing, especially the hybrid approach, is able to improve the accuracy and stability of the model in image classification tasks.
Development of highway vehicle detection using background subtraction and Haar cascade methods Ni Gusti Ayu Dasriani; Anthony Anggrawan; Khasnur Hidjah; Christofer Satria; I Nyoman Yoga Sumadewa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1004-1015

Abstract

Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Co-Authors Anthony Anggrawan Bambang Krismono Christofer Satria Dading Oktaviadi Resmiranta I Nyoman Yoga Sumadewa Ni Gusti Ayu Dasriani