Authors:
Soleh Darmansyah
1
;
Rika Rosnelly
1
and
Hartono
2
Affiliations:
1
Computer Science Masters Study Program , Faculty of Engineering and Computer Science , Potential Utama University, Medan, Indonesia
;
2
Informatics Engineering Study Program Faculty of Engineering, Medan Area University, Medan, Indonesia
Keyword(s):
Road, Asphalt, Machine Learning, Decission Tree, K-Nearest Neighbor.
Abstract:
Roads are infrastructure made to facilitate land transportation in connecting one area to another. In general, roads in Indonesia use asphalt as a material in the road construction process. The cross-Sumatra route is one of the accesses that plays an important role in increasing economic progress in areas that connect areas on the island of Sumatra. The development of computer vision using various image recognition classification methods results in more accurate data accuracy. The Decission Tree and K-Nearest Neighbor methods in image recognition classification of asphalt damage can be a solution in identifying damage and measuring the area of damage through machine learning from images taken from the field. The design and implementation of making applications is continued using the Decission Tree method using python as a programming language. Asphalt damage conditions are divided into three classification categories of asphalt damage, namely mild, moderate and severe. The results of
the identification can be used as a report or field survey of the damage conditions that occur on the Sumatra route. The accuracy value of the training is carried out using a dataset of 560 images. The Decission Tree method can get light damage 99.3damage, the accuracy value is 99.3for light damage is 79.12accuracy from Machine learning carried out in this study show the highest accuracy value obtained from the Decission Tree method in identifying road damage.
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