EXTRACTION DATA ANALYSIS OF HISTOGRAM MOMENT IMAGE AND COMPARISON OF CLASSIFICATION ALGORITHM MODELS OF NAIVE BAYES, NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, AND DECISION TREE IN CASE STUDY OF DAMAGED ASPHALT AND UNDAMAGED ASPHALT ROAD IMAGES

Abstract

The growth of computer vision technology has helped many people to automate their work such as an intelligent detection system on an image. Detection is the detection of damage or detection of holes on the surface of the asphalt road. To fulfill this, a research was carried out by taking samples of asphalt roads classified as damaged or not damaged to measure the accuracy of the best classification algorithm model to build a machine learning system for asphalt road detection. The sample image of the asphalt road photo will be extracted into a histogram moment then the formed dataset will be applied to four classification model algorithms to measure the accuracy of the system. The tools used in this study are the Pyhton programming language library tools for digital image processing, classification algorithms, and determining the accuracy of the classification algorithm resultsKeywords: Classification, Computer Vision, Machine Learning, Classification Algorithms, Image, Python.