Classification Of Guarantee Fruit Murability Based on HSV Image With K-Nearest Neighbor

Author:

Sarimole Frencis Matheos,Fadillah Muhammad Ilham

Abstract

Guava bol is one of the fruits from Indonesia that is favored by many Indonesian people. The guava itself has a soft and dense flesh texture compared to water guava. The guava itself has a pink color if it is raw but if the guava is ripe it will be dark red. From a glance, when viewed from human vision, it is very easy to distinguish between them, but from most people it is still difficult to distinguish which guava is ripe, half-ripe and unripe guava because of differences in opinion from one human eye to another. Based on these problems, researchers have developed a system that is able to detect the maturity level of guava fruit by utilizing the Hue Saturation Value (HSV) feature extraction with K-Nearest Neighbor (KNN). The data used in this study were 465 datasets which were divided into 324 training data and 141 test data. The data had classes, namely ripe, half-cooked, and raw. The data is then classified using the K-Nearest Neighbor method by calculating the closest distance with a value of K = 3. From this study resulted in an accuracy of 97.16%.

Publisher

Yayasan Riset dan Pengembangan Intelektual

Subject

General Medicine

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Latin Hypercube Sampling Approach to Improve K-Nearest Neighbors Performance on Imbalanced Data;2023 International Conference of Computer Science and Information Technology (ICOSNIKOM);2023-11-10

2. Automated Fruit Maturity Classification System for Guava (Psidium Guajava) Using K-NN Method and TCS3200 Sensor;2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI);2023-09-20

3. Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor;2023 International Seminar on Application for Technology of Information and Communication (iSemantic);2023-09-16

4. Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review;Agronomy;2023-02-23

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