Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color
DOI:
https://doi.org/10.37385/jaets.v3i2.763Keywords:
Machine Learning, Data Classification, Vegetable QualityAbstract
In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values ??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of the K value of K-Nearest Neighbor 3,4,5,6,7,8,9. Has a very good percentage of accuracy compared to only K-NN. The test results show the K-Nearest Neighbor method in data classification has a good percentage accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9 has a percentage of 100%.
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Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big data, 7(4), 221-248.
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.
Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018, October). Sentiment analysis about E-commerce from tweets using decision tree, K-nearest neighbor, and naïve bayes. In 2018 international conference on orange technologies (ICOT) (pp. 1-6). IEEE.
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., ... & Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20), 2470.
Binti Jaafar, H., binti Mukahar, N., & Ramli, D. A. B. (2016, December). A methodology of nearest neighbor: Design and comparison of biometric image database. In 2016 IEEE Student Conference on Research and Development (SCOReD) (pp. 1-6). IEEE.
Diah, K. T., Faqih, A., & Kusumoputro, B. (2019, November). Exploring the feature selection of the EEG signal time and frequency domain features for k-NN and weighted k-NN. In 2019 IEEE R10 Humanitarian Technology Conference (R10-HTC)(47129) (pp. 196-199). IEEE.
Farhat, R., Mourali, Y., Jemni, M., & Ezzedine, H. (2020, February). An overview of Machine Learning Technologies and their use in E-learning. In 2020 International Multi-Conference on:“Organization of Knowledge and Advanced Technologies”(OCTA) (pp. 1-4). IEEE.
Findawati, Y., Astutik, I. I., Fitroni, A. S., Indrawati, I., & Yuniasih, N. (2019, December). Comparative analysis of Naïve Bayes, K Nearest Neighbor and C. 45 method in weather forecast. In Journal of Physics: Conference Series (Vol. 1402, No. 6, p. 066046). IOP Publishing.
Gempita, G. P., Wilasari, D., Kristalina, P., & Sukaridhoto, S. (2019, September). Implementation of K-NN fingerprint method on receiving server for indoor mobile object tracking. In 2019 International Electronics Symposium (IES) (pp. 411-416). IEEE.
Jain, A., & Lella, R. L. (2020, December). Pearson Correlation Coefficient Based Attribute Weighted k-NN for Air Pollution Prediction. In 2020 IEEE 17th India Council International Conference (INDICON) (pp. 1-8). IEEE.
Mughnyanti, M., Efendi, S., & Zarlis, M. (2020). Analysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation. In IOP Conference Series: Materials Science and Engineering (Vol. 725, No. 1, p. 012128). IOP Publishing.
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020). A review of convolutional neural network applied to fruit image processing. Applied Sciences, 10(10), 3443.
Safri, Y. F., Arifudin, R., & Muslim, M. A. (2018). K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor. Sci. J. Informatics, 5(1), 18.
Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., ... & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 202, 116137.
Triguero, I., García?Gil, D., Maillo, J., Luengo, J., García, S., & Herrera, F. (2019). Transforming big data into smart data: An insight on the use of the k?nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(2), e1289.
Putra, P. H., & Novelan, M. S. (2020). Analysis Of The Use Of X-Means Method In Grouping Interest And Talent Data Students. Jurnal Ipteks Terapan, 14(2), 152-159.
Putra, P. H. (2021). Application Of The K-Means Algorithm In Identifying Types Of Skin Disease1. Infokum, 9(2, June), 281-286.
Von Rueden, L., Mayer, S., Garcke, J., Bauckhage, C., & Schuecker, J. (2019). Informed machine learning–towards a taxonomy of explicit integration of knowledge into machine learning. Learning, 18, 19-20.
Yahaya, O. K. M., MatJafri, M. Z., Aziz, A. A., & Omar, A. F. (2014, August). Non-destructive quality evaluation of fruit by color based on RGB LEDs system. In 2014 2nd International Conference on Electronic Design (ICED) (pp. 230-233). IEEE.
Yang, C. C., Soh, C. S., & Yap, V. V. (2018). A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency, 11(1), 239-259.
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