Open AccessK-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN
Author(s) -
Ahmad Wali Satria Bahari Johan,
Fitri Utaminingrum,
Agung Setia Budi
Publication year - 2020
Publication title -
jitecs (journal of information technology and computer science)
Language(s) - English
Resource type - Journals
eISSN - 2540-9824
pISSN - 2540-9433
DOI - 10.25126/jitecs.202051144
Subject(s) - stairs , wheelchair , computer science , value (mathematics) , descent (aeronautics) , artificial intelligence , frame (networking) , k nearest neighbors algorithm , class (philosophy) , pattern recognition (psychology) , mathematics , machine learning , engineering , telecommunications , civil engineering , world wide web , aerospace engineering
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