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Ahmad Wali Satria Bahari Johan
Computer Vision Research Group, Faculty of Computer Science, Brawijaya University, Malang, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN Ahmad Wali Satria Bahari Johan; Fitri Utaminingrum; Agung Setia Budi
Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1299.817 KB) | DOI: 10.25126/jitecs.202051144

Abstract

This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair
Co-Authors Agung Setia Budi Utaminingrum, Fitri