Dogs and Cats Classification using Linear Discriminant Analysis and Support Vector Machine

  • Alethea Suryadibrata
  • Suryadi Darmawan Salim Universitas Multimedia Nusantara

Abstract

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F-score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.

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Published
2019-08-30
How to Cite
Suryadibrata, A., & Salim, S. (2019). Dogs and Cats Classification using Linear Discriminant Analysis and Support Vector Machine. Ultimatics : Jurnal Teknik Informatika, 11(1), 46-51. https://doi.org/https://doi.org/10.31937/ti.v11i1.1076
Section
Articles