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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Saparudin, Saparudin
Telkom University
Computer Science & IT Electrical & Electronics Engineering

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Network and layer experiment using convolutional neural network for content based image retrieval work Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.19759

Abstract

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.
Extraction of object image features with gradation contour Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama; Beni Irawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.19491

Abstract

Image retrieval using features has been used in previous studies including shape, color, texture, but these features are lagging. With the selection of high-level features with contours, this research is done with the hypothesis that images on objects can also be subjected to representations that are commonly used in natural images. Considering the above matters, we need to research the feature extraction of object images using gradation contour. From the results of the gradation contour test results, there is linearity between the results of accuracy with the large number of images tested. Therefore, it can be said that the influence of the number of images will affect the accuracy of classification. The use of contour gradation can be accepted and treated equally in all image types, so there is no more differentiation between image features. The complexity of the image does not affect the method of extracting features that are only used uniquely by an image. From the results of testing the polynomial coefficient savings data as a result of the gradation contour, the highest result is 81.40% with the highest number of categories and the number of images tested in the category is also higher.
Pengenalan Citra Wajah Menggunakan Minimum Distance Classifier Berdasarkan Fitur Principal Component Analysis Bella Adinda Putri; Saparudin Saparudin
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 2 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i2.551

Abstract

This study presents a unique combination of feature extraction techniques and recognition methods that work well on more than one standard face dataset. The main focus in this research is how to obtain the features of each face image to distinguish faces from each other by applying the Principal Component Analysis (PCA) method as feature extraction, and the Minimum Distance Classifier as the recognition algorithm so that recognition results can be obtained. To achieve this goal, a literature study is needed to understand the concepts and theoretical basis in order to strengthen the assumptions of the Principal Component Analysis and Minimum Distance Classifier methods. The results of the recognition using ORL database get 97% accuracy, while the results of the recognition using YALE database get 94.6% accuracy. So it can be concluded that the combination of PCA and Minimum Distance Classifier can provide a quick and simple solution by increasing or without reducing standard accuracy.
Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks Nurhadi Nurhadi; Deris Stiawan; Mohd. Yazid Idris; Saparudin Saparudin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4104

Abstract

Augmented reality (AR) has been used in maintenance, simulation, and remote assistance, among other applications. In AR systems, one of the significant issues is the placement of objects in augmented physical environments. Given the importance of object placement in AR systems, we proposed deep learning-based object placement, covering both object detection and object segmentation, to address relevant issues. Deep learning can help users complete tasks by providing the right information effectively, with the method taking into account dynamically changing environments and users’ situations in real time. The problem is that it is rarely used in AR, thereby prompting the combination of deep learning-based object detection and instance segmentation with wearable AR technology to improve the performance of complex tasks. This challenge was addressed in this work through the use of convolutional neural networks in the detection and segmentation of objects in actual environments. We measured the performance of AR technology on the basis of detection accuracy under environmental conditions of different intensities. Experimental results showed satisfactory segmentation and accurate detection
Co-Authors Deris Stiawan Errissya Rasywir Fachruddin Fachruddin Fachruddin Fachruddin Irawan, Beni Mohd. Yazid Idris Nurhadi Nurhadi Yovi Pratama