Suryadibrata, Alethea and Salim, Suryadi Darmawan (2019) Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine. Ultimatics : Jurnal Teknik Informatika, 11 (1). ISSN 2085-4552
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 12 Oct 2021 04:52 |
| Last Modified: | 12 Oct 2021 04:52 |
| URI: | https://kc.umn.ac.id/id/eprint/18683 |
Actions (login required)
 |
View Item |
