Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

Johan, Ahmad Wali Satria Bahari; Putri, Sekar; Hajar, Granita; Wicaksono, Ardian Yusuf

doi:10.24843/lkjiti.2021.v12.i03.p02

Cited by 1 publication

(1 citation statement)

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“…This is how it operates. One way to calculate the closest distance, namely using the Euclidean distance [15]. In Equation ( 8), the distance between the training data and the evaluated data is measured using the Euclidean distance [16], [17].…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Determination of Coffee Fruit Maturity Level Using Image Histogram and K-Nearest Neighbor

Damayanti,

Michael

2024

BAREKENG: J. Math. & App.

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Coffee has a very important role in the Indonesian economy, as one of the country's foreign exchange contributors in the plantation sector. Therefore, coffee processing is very important in determining the quality of coffee. The procedure for choosing and evaluating the coffee fruit's physical quality is one of the most crucial steps. The step of determining the maturity level of coffee fruit is carried out using the image histogram and K-Nearest Neighbor (KNN) method. This research uses the KNN algorithm with classification stages that will show the level of accuracy value according to the value of k = 5 used when processing the classification of coffee fruit image data. In order to complete this step, the features of the coffee fruit are identified using its color. The qualities of quality coffee fruit, which is flawlessly red in color. Twenty images total—ten of which are of ripe coffee fruit and ten of which are of raw coffee fruit—were used in this study. The test results were carried out using rapidminer tools using 40% training data and 60% testing data from the total data set. Based on the test results, it gives an accuracy value of 100%, meaning that the data set can be used in the next stage as valid data to be used.

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Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%