Optimasi Algoritma K-Nearest Neighbor untuk Klasifikasi Tingkat Kematangan Buah Alpukat Berdasarkan Warna

Hanafi, Marsyita; Fadillah, Nurul; Insan, Ahmad

doi:10.25299/itjrd.2019.vol4(1).2477

Cited by 8 publications

(13 citation statements)

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“…In this study u is a feature attribute of the test image and v is a feature attribute of the training image and the test image. After the centroid that has the closest distance was found [16].…”

Section: Methodsmentioning

confidence: 99%

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Ardianto

Sriani

Hasugian

2023

J. Teknik Informatika CIT Medicom,

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Computerization requires system testing and further system development, namely color feature extraction with KNN. Avocado is one that has a high protein content in it. This research uses the KNN algorithm method and feature extraction in order to get more effective results, the purpose of this research is to make it easier for people to choose the ripeness level of butter avocados because people still don't know about the maturity level of butter avocados. In this study, testing was carried out by bringing the avocado fruit closer to the cellphone camera connected to the researcher's internet, after which the application will automatically match the color of the avocado. to the system, the system will produce output based on that color with output in the form of the ripeness level of the avocado, whether it is ripe, ripe, half ripe, rotten and also generates information on how much longer the avocado will ripen. All stages of system development are carried out by analyzing data first, then taking sample data, training and testing datasets, then the results of the system will become benchmarks. The test data in this study used several types of avocado objects, namely: Raw, Half Ripe, Ripe, Ripe, Rotten. It consisted of 55 data samples consisting of 11 raw avocado samples, 11 half-ripe avocado samples, 11 ripe avocado samples, 11 ripe avocado samples and 11 rotten avocado samples. Obtained euclidean distance values for each type of avocado butter. After that, the sum is done to get the overall level of accuracy by adding up the total euclidean distance with the total euclidean distance for each type of avocado. After getting the added value multiply it by 100%. Then the overall accuracy results obtained are 98.38%.

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Section: Methodsmentioning

confidence: 99%

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Ardianto

Sriani

Hasugian

2023

J. Teknik Informatika CIT Medicom,

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“…Buah serba guna ini memiliki banyak manfaat dan khasiat bagi manusia, karena ada banyak zat yang kaya akan manfaat yang terdapat di buah alpukat. Selain rasanya nikmat membuat banyak orang menyukai nya [2]. Petani kebun Usaha tani alpukat merupakan usaha tani yang dapat dikembangkan sehingga kebutuhan pasar dapat dipenuhi dan mendatangkan keuntungan bagi petani.…”

Section: Pendahuluanunclassified

Intelligent System for Identifying Avocado Ripe Using Extraction Features and K-Nearest Neighbor Method

Hasan

2023

Restikom

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So far, farmers in harvesting avocados often experience obstacles, namely farmers are still not precise in determining ripe avocados. The ripeness of avocado when harvested is one of the most important factors in maintaining the quality of the avocado. This is due to fatigue and takes a long time so it is not appropriate to determine which avocados are ripe and which are not. Therefore, a system is needed to find out avocados. This study formulates problems in the form of how to identify avocado identification with the KNN method and how to find out how much accuracy the method obtained. While the objectives achieved in this study can identify avocados based on the KNN method and can measure the accuracy of the method with avocado image data. The results obtained in this study were successful in identifying and using the 80% measurement confusion matrix.

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“…The technique used in manipulating or processing an image in two-dimensional form is also called image processing (Hanafi et al, 2019).…”

Section: ) B) Image Processingmentioning

confidence: 99%

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

Sarimole¹,

Fadillah²

2022

JAETS

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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%.

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Optimasi Algoritma K-Nearest Neighbor untuk Klasifikasi Tingkat Kematangan Buah Alpukat Berdasarkan Warna

Cited by 8 publications

References 0 publications

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Intelligent System for Identifying Avocado Ripe Using Extraction Features and K-Nearest Neighbor Method

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

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