How to cite (IEEE): A. Rangkuti, & V. Hasbi "A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 864-863, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1153

How to cite (APA): Rangkuti, A., & Hasbi, V. (2023). A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support. JOIV : International Journal on Informatics Visualization, 7(3), 864-863. https://doi.org/10.30630/joiv.7.3.1153

How to cite (Chicago): Rangkuti, Abdul, AND Hasbi, Varyl Athala. "A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1153

How to cite (Vancouver): Rangkuti A, & Hasbi V . A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):864-863. https://doi.org/10.30630/joiv.7.3.1153

How to cite (Harvard): Rangkuti, A., & Hasbi, V. ,2023. A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 864-863. https://doi.org/10.30630/joiv.7.3.1153

How to cite (MLA8): Rangkuti, Abdul, & Varyl Athala Hasbi. "A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 864-863. Web. 5 Dec. 2023 , https://doi.org/10.30630/joiv.7.3.1153

BibTex Citation Data :

@article{JOIV1153,
    	author = {Abdul Rangkuti and Varyl Athala Hasbi},
    	title = {A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {Animal skin; CLAHE; SUCK; CNN; Resnet50 V2; Inception V3; Densenet121},
    	abstract = {This study describes the process of classifying animal skin images which are rather difficult to obtain optimal image characteristics. For this reason, in the pre-processing stage, we propose two methods to support feature extraction: sharpening using a convolutional kernel (SUCK-Sharpening) and adaptive histogram equalization with limited contrast (CLAHE-Equalized). SUCK works by operating on these pixel values using direct math to build a new image; this final value is the new value of the current pixel. CLAHE overcomes the limitations of the global approach by performing local contrast enhancement. Because of the advantages of the two methods, it becomes a solution to get features processed at the feature extraction and classification stage. The process of animal skin imagery has characteristics in terms of shape and texture, including the characteristics of animal skin color. In this study, some experiments have been carried out on several CNN models, with an average classification accuracy of more than 70% using the sharpened and equalized methods on six animal skins. More detail, the average classification accuracy using 3 CNN models supported by two methods, namely Sharpening and Equalize on the CNN Resnet 50V2 model is 67.73% and 73.78%, InceptionV3 model at 82.13%, and 74.76% and Densenet121 models were 87.64% and 87.46 %. This research can be continued to improve the accuracy of other animal skin images, including determining fake or genuine skin images.},
        	issn = {2549-9904},    	pages = {864--863},    	doi = {10.30630/joiv.7.3.1153},
        	url = {http://joiv.org/index.php/joiv/article/view/1153}
    }
    

Refworks Citation Data :

@article{{JOIV}{1153}, author = {Rangkuti, A., Hasbi, V.}, title = {A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1153}, url = {} }