Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Irwan Sembiring
Faculty of Information Technology Universitas Kristen Satya Wacana, Jl. Diponegoro No.52-60, Salatiga, Jawa Tengah 50711
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning Florentina Tatrin Kurniati; Irwan Sembiring; Adi Setiawan; Iwan Setyawan; Roy Rudolf Huizen
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27842

Abstract

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.
Co-Authors Adi Setiawan Florentina Tatrin Kurniati Iwan Setyawan Roy Rudolf Huizen