Classifying The Swallow Nest Quality Using Support Vector Machine Based on Computer Vision

Septiarini, Anindita and Maulana, Ferda and Hamdani, Hamdani and Saputra, Rizqi and Wahyuningrum, Tenia and Indra, Indra (2022) Classifying The Swallow Nest Quality Using Support Vector Machine Based on Computer Vision. In: 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 16-18 Juni 2022, Malang, Indonesia.

	Text IEEE 2-plagiarisme Classifying The Swallow Nest Quality.pdf Download (656kB)
	Text IEEE 2-Classifying The Swallow Nest Quality Using.pdf Download (599kB)

Abstract

Swallow Nest is a valuable export commodity, particularly in Indonesia. It is produced when a swallow's saliva hardens and is frequently encountered in high-rise buildings. Swallow nests can be utilized to treat various ailments in the medical sector. The price of a swallow nest varies according to its quality, which is commonly classified into three grades: quality 1 (Q1), quality 2 (Q2), and quality 3 (Q3). Q1 is of the highest quality, while Q3 is of the lowest. Each grade has a different physical appearance. Currently, many people lack knowledge regarding the grade of a swallow nest. Therefore, a method is needed to automatically classify the quality of swallow nests based on computer vision. The proposed method consists of several main processes, including image acquisition, ROI detection, pre-processing, segmentation, feature extraction, and classification. The feature extraction was applied based on shapes, followed by the Support Vector Machine (SVM) implementation in the classification process. This process was performed with cross-validation using the k-fold values of 5. The performance evaluation was done using three parameters: precision, recall, and accuracy, by achieving the value of 90.6%, 89.3%, and 89.3%, respectively.

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