Poisonous Shrimp Detection System for Litopenaeus Vannamei using k-Nearest Neighbor Method

  • Abdullah Husin Universitas Islam Indragiri
  • Othman Mahmod Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia
  • Lisa Afrinanda Department of Information System, Universitas Islam Indragiri, Indonesia

Abstract

One of the important seafoods in the food consumption of humans is shrimp. Although shrimp contains proteins that are needed by the human body, sometimes it contains toxins. This is due to environmental factors or catching processes that may use toxins. Therefore, the community should take precautions when consuming shrimp. White shrimp (Litopenaeus vannamei) is one type of shrimp that is preferred because of its delicious taste. The purpose of this research is to develop a computerized system for poisonous white shrimp detection. The category of white shrimps consists of two kinds, i.e., fresh white shrimps that are caught in a natural way (class A), and poisonous white shrimps that are caught by using toxin (class B). The features used are RGB colors (red, green, and blue) and texture (energy, contrast, correlation, and homogeneity). A similarity-based classification is performed by the k-Nearest Neighbor (k-NN) algorithm. The experiment was conducted on a dataset consisting of 90 white shrimp images. The holdout validation method was used to evaluate the system. The original dataset was divided into two parts, whereby 60 images were used as training samples and 30 images were used as testing images. Based on the evaluation results, it can be concluded that the classification accuracy is 73.33%. The benefit of the developed system is to help the community in selecting good and safe white shrimps.

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References

[1] D. Novita, T. R. Ferasyi, and Z. A. Muchlisin, “Intensitas dan Prevalensi Ektoparasit Pada Udang Pisang ( Penaeus sp .) Yang Berasal dari Tambak Budidaya di Pantai Barat Aceh,” Jurnal Ilmiah Mahasiswa Kelautan dan Perikanan Unsyiah, vol. 1, no. 3, pp. 268–279, 2016.
[2] M. Prashanth and C. Indranil, “Journal of Medical and Health Sciences Food Poisoning : Illness Ranges from Relatively Mild Through To Life Threatening,” Journal of Medical and Health Sciences Food, vol. 5, no. 4, pp. 1–19, 2016.
[3] Abdullah, Usman, and M. Efendi, “Sistem Klasifikasi Kualitas Kopra berdasarkan Warna dan tekstur Menggunakan Metode Nearest Classifier (NMC),” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 4, no. 4, pp. 297–303, 2017.
[4] S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN Classification With Different Numbers of Nearest Neighbors,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2017.
[5] P. Galdi and R. Tagliaferri, “Data Mining: Accuracy and Error Measures for Classification and Prediction,” in Reference Module in Life Sciences, no. January, Elsevier, 2018, pp. 1–14.
[6] J. M. Kirimi and C. A. Moturi, “Application of Data Mining Classification in Employee Performance Prediction,” International Journal of Computer Applications, vol. 146, no. 7, pp. 28–35, 2016.
[7] N. C. S. Reddy, K. S. Prasad, and A. Mounika, “Classification Algorithms on Datamining : A Study,” International Journal of Computer Intelligence Research, vol. 13, no. 8, pp. 2135–2142, 2017.
[8] P. Sagar, Prinima, and Indu, “Analysis of Prediction Techniques based on Classification and Regression,” International Journal of Computer Applications, vol. 163, no. 7, pp. 47–51, 2017.
[9] M. Kibanov, M. Becker, J. Mueller, M. Atzmueller, A. Hotho, and G. Stumme, “Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data,” in Proceedings of Symposium on Applied Computing (SAC), 2017, pp. 1–9.
[10] E. López-Iñesta, F. Grimaldo, and M. Arevalillo-Herráez, “Classification similarity learning using feature-based and distance-based representations: A comparative study,” Applied Artificial Intelligence, vol. 29, no. 5, pp. 445–458, 2015.
Published
2018-05-01
How to Cite
HUSIN, Abdullah; MAHMOD, Othman; AFRINANDA, Lisa. Poisonous Shrimp Detection System for Litopenaeus Vannamei using k-Nearest Neighbor Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 20-27, may 2018. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/38098>. Date accessed: 13 nov. 2024. doi: https://doi.org/10.24843/LKJITI.2018.v09.i01.p03.
Section
Articles