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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Sriwijaya Journal of Informatics and Applications
Muhammad Fachrurrozi
Sriwijaya University
Computer Science & IT

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Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine Muhammad Fachrurrozi; Muhammad Mujtahid
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 2: June 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i2.1171

Abstract

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.  
Cat Breeds Classification Using Convolutional Neural Network For Multi-Object Image Naura Qatrunnada; Muhammad Fachrurrozi; Alvi Syahrini Utami
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.46

Abstract

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds.  But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively.
Co-Authors Alvi Syahrini Utami Muhammad Mujtahid Naura Qatrunnada