Komparasi Metode Naive Bayes Dan K-NN Untuk Klasifikasi Jenis Kismis (Raisin)

Syahbana R, Nur Muhammad (2023) Komparasi Metode Naive Bayes Dan K-NN Untuk Klasifikasi Jenis Kismis (Raisin). Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Raisins are dried grapes (Vitis Vinifera L.) with their seeds removed. In previous research, a machine vision system was developed to distinguish between two different varieties of raisins (kecimen and benis) grown in Turkey. Initially, a total of 900 raisins were obtained from both varieties in equal amounts. The classification achieved 85.22% accuracy with LR, 86.33% with MLP, and the highest classification accuracy of 86.44% was obtained in the study using SVM. Based on the above discussion, the objective of this research is to develop software that can classify types of raisins using the K-NN and Naïve Bayes methods. This will allow researchers to determine which method achieves the highest accuracy. The methods used in this research include literature study, data collection, system design, system implementation, system testing, and result analysis. Based on the results of accuracy testing in the previous stage, the K-NN method achieved the highest accuracy with a value of 7 (K = 7), reaching 89%. On the other hand, the Naïve Bayes method only achieved an accuracy of 30%. Therefore, it can be concluded that the K-NN method has a higher accuracy based on the results of the two testing methods.

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