KLASIFIKASI PENGENALAN BUAH MENGGUNAKAN ALGORITMA NAIVE BAIYES

  • Arif Saputra Universitas Nahdlatul Ulama Sidoarjo
Keywords: Apples, Image processing, Naive Bayes, Pattern Recognition, Classification

Abstract

Manually sorting varieties of apples result in high costs, subjectivity, boredom, and inconsistencies associated with humans. A means is needed to distinguish between types of apples and, therefore, some reliable techniques are necessary to identify varieties quickly and without damage. The purpose of conducting research is to investigate the application and performance for Naive Bayes algorithm for apple varieties. This software methodology involves image acquisition, preprocessing, segmentation and analysis classification varieties for apple. The prototype of Apple's classification system was built using the MATLAB R2017 development platform environment. The results in this study indicate that the estimated average accuracy, sensitivity, precision, and specificity are 81%, 73%, 100%, and 70%, respectively. MLP-Neural shows that performance of the Naive Bayes technique is consistent with Principal, Fuzzy Logic, and Neural analysis with 89%, 91%, 87%, and 82% respectively in terms of accuracy. This study shows that Naif Bayes has excellent potential for identifying nondestructive and accurate apple varieties.

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References

P.Redman. Good essay writing: a social sciences guide3rd ed., London: Open University in assoc. with Sage, 2006.

J. Bell, Machine Learning: Hands-On for Developers and Technical Professionals. Indianapolis : Wiley, 2015, p. 2.

A. Géron, Hands-On Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol : O’Reilly, 2017.

P. Willett, “The Porter stemming algorithm: then and now,” Program: Electronic Library and Information Systems, vol. 40, no. 3, pp. 219-223,2006.

X. Lu, Computational Methods for Corpus Annotation and Analysis. New York: Springer, 2014.

L. Weitzel, R. A. Freire, P. Quaresma, T. Gonc¸alves, and R. Prati. How does irony affect sentiment analysis tools? In Progress in Artificial Intelligence, pages 803–808, Cham, 2015. Springer International Publishing.

I Made Sukafona, Emmy Febriani Thalib Content Based Image Retrieval Dengan Metode Color Moment Dan K-Means, pages 73–78, 2018. Jurnal Resistor.

A. Mccallum and K. Nigam. A comparison of event models for naive bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization’, 1998.

L. Weitzel, R. A. Freire, P. Quaresma, T. Gonc¸alves, and R. Prati. How does irony affect sentiment analysis tools? In Progress in Artificial Intelligence, pages 803–808, Cham, 2015. Springer International Publishing.

P. Yang and Y. Chen. A survey on sentiment analysis by using machine learning methods. In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pages 117–121, Dec 2017.

Published
2019-10-28
How to Cite
Saputra, A. (2019). KLASIFIKASI PENGENALAN BUAH MENGGUNAKAN ALGORITMA NAIVE BAIYES. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(2), 83-88. https://doi.org/10.31598/jurnalresistor.v2i2.434
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