Classification Based on Machine Learning Methods for Identification of
Image Matching Achievements

Classification Based on Machine Learning Methods for Identification of
Image Matching Achievements

: image matching, logo, machine learning, kNN, RF, MLP

Classification is one method in image processing. Image processing to search for similar images or with similarity ownership
is called image matching or image matching. In the measurement of image matching, the original and fake logo objects are
used. Identification of similarity manually with the help of human vision is not necessarily precise and it is difficult to obtain
accurate results. Based on this, the identification of image matching of the original and fake logos automatically requires an
application, in order to obtain precise and more accurate results. Identification of image suitability is determined through the
image segmentation process, and feature extraction is based on the statistics of Red-Green-Blue (RGB), Hue-Saturation-Value
(HSV), feature extraction of area, perimeter, eccentricity, and tangent distance measurements. The purpose of this study
includes the identification of the achievement of image-matching logo images with comparisons of accuracy between various
machine learning methods. The use of machine learning methods in this study includes the k-Nearest Neighbor (kNN), Random
Forest (RF), and Multilayer Perceptron (MLP) methods. The use of the dataset includes eighteen training data and eight logo
image testing data, divided into genuine and fake classes. The results of the measurement of the accuracy value obtained a
value of seventy-five percent with the kNN method or the RF method, while the MLP method obtained an accuracy value of
eighty-seven point five percent. Based on these results, it can be concluded that the MLP method with the highest accuracy
value was chosen as a classification model from machine learning to identify the achievement of image matching on the original
and fake logos. For further development, the system can be developed using other methods or a combination of different
methods, in order to obtain better accurate results

Rusydi Umar1
, Imam Riadi2
, Dewi Astria Faroek3

Ahmad Dahlan University

20-04-2022

Fajar bagus W

PDF

Indonesia

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