Deep Learning Convolutional Neural Networks on Multi Label Image Classification of Torajanese Buffalo


Aslan Poetra Ramadhan(1*); Anik Nur Handayani(2); Ilham Ari Elbaith Zaeni(3);

(1) Universitas Negeri Malang
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(*) Corresponding Author

  

Abstract


Convolutional Neural Networks (CNNs) represent the primary methodology in the advancement of intelligent systems and technologies. The capacity to transition from prediction to categorization establishes CNNs as the primary benchmark in the advancement of deep artificial intelligence. This study use CNN implementation to categorize photos of Torajanese buffalo. The Torajanese buffalo is a distinctive animal species belonging to the Bos bubalis family, integral to the lives and culture of the Torajanese people residing in northern South Sulawesi. This species is integral to the culture, deeply intertwined with several traditional practices of the community. This renders the species distinctive for more investigation. The distinctiveness of the buffalo's style, coloration, and form differentiates them from one another. This study use Convolutional Neural Networks (CNNs) as the primary method to categorize Torajanese buffalo species using head photos and markers derived from local knowledge. This research employs InceptionV3, DenseNet, and Xception as primary architectures, each with variations corresponding to 10, 50, and 100 epochs, therefore enhancing the study. The findings of this investigation indicate that the InceptionV3 architecture has commendable performance across both versions, achieving an average AUC-ROC score of 0.96, although with increased execution time. Nonetheless, the DenseNet architecture demonstrates superior performance in its optimal configuration, achieving flawless accuracy; nonetheless, it incurs the most processing time for the frontal view of the Torajanese buffalo head test case.


Keywords


CNN; Multi Label; Image Processing; Classification; Torajanese Buffalo;

  
  

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doi  https://doi.org/10.33096/ilkom.v17i2.2905.162-169 		  

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