Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

Sinaga, Daurat; Agustina, Feri; Setiyanto, Noor Ageng; Suprayogi, Suprayogi; Jatmoko, Cahaya

doi:10.33633/jais.v6i2.4627

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…The calculation process in the k-NN algorithm is an euclidean distance calculation, which is a search method between two variable points, the closer and similar the smaller the distance between the two points [36]. Euclidean distance is said to be good if the new data has a minimum distance and has a high similarity.…”

Section: K-nearest Neighbor Algorithm Calculation Processmentioning

confidence: 99%

“…The distance calculation is carried out using the square distance (SD) formula shown in (1). The results of the analysis show that the prediction process is carried out after calculations using the k-NN algorithm with a total of 58 training data as many as 36 The recapitulation of the calculation results using the k-NN algorithm with SD with a total of 36 training data and 85 complete test data as shown in Tables 3 and 4.…”

Section: K-nearest Neighbor Algorithm Calculation Processmentioning

confidence: 99%

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K-nearest neighbor algorithm analysis for path determination in network simulation using software defined network

Ritzkal

Suhadi²,

Amalia

et al. 2023

Bulletin EEI

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Software defined network (SDN) is a new approach concept for planning, developing, and operating computer networks. Routing is the process of selecting paths on a network system to send or forward packets to the destination network. This study aims to obtain the results of the calculation analysis using the k-nearest neighbor (k-NN) method as the implementation of the recommendations. The results of the analysis use a mesh topology design approach with predicted values of throughput, jitter, delay, and packet loss. This value is used as a recommendation to the network manager to determine the best path. The best path selection from the analysis results is i) path-1 (very good) which includes switch 1, switch 2, and switch 4; ii) path-2 (good) to switch 1, switch 3 and switch 4 switch 4; iii) channel-3 (moderate) through switch 1, switch 2, switch 3 and switch 4; and iv) channel-4 (bad) through switch 1, switch 3, switch 2, and switch 4. While the calculation using the confusion matrix is accuracy=72.31%, precision=96.08% and recall=87.84%.

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Section: K-nearest Neighbor Algorithm Calculation Processmentioning

confidence: 99%

Section: K-nearest Neighbor Algorithm Calculation Processmentioning

confidence: 99%

K-nearest neighbor algorithm analysis for path determination in network simulation using software defined network

Ritzkal

Suhadi²,

Amalia

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Further research was conducted by (Luay Nabila El Suffa, 2021) "Identification of Formalin Chicken Meat Image Using the Gray Level Co-Occurrence Matrix (Glcm) and K-Nearest Neighbor (KNN) method" Identifying the image of fresh broiler and village chicken meat using the K method -Nearest Neighbor (K-NN) as classifier and Gray Level Co-Occurrence (GLCM) method as feature extraction (Sinaga & Agustina, 2021). The test uses a dataset of 60 and the test results get an accuracy of 85%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…That is one of them with image processing that can help a sorting process so that it can be obtained with uniform results and in accordance with the price. As research material, researchers used the types of durian fruit using a technology that can help the process of segmenting the level of durian fruit types based on color similarity using GLCM and K-Nearest Neighbor Extraction (Sinaga, et al, 2021;Kamdar, et al, 2022;Larasati, 2021;.…”

Section: Introductionmentioning

confidence: 99%

Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)

Sarimole¹,

Syaeful²

2022

JAETS

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Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because consumers generally still find it difficult to distinguish physically identified types of Durian fruit due to limited knowledge of the types of Durian fruit and require a relatively long time and accuracy in sorting. Therefore, there is a need for a method to sort the types of Durian fruit effectively and efficiently. Namely image segmentation based on the classification of the types of Durian fruit to help consumers. The method used is Gray Level Co-Occurrence Matrices for feature extraction, while to determine the proximity between the test image and the training image using the K-Nearest Neighbor method based on texture based on the color of the Durian fruit obtained. Extraction features using the GLCM method based on angles of 0°, 45°, 90° and 135°. Then the KNN method is used for the classification of characteristic results using K = 3. In this study, 1281 data training was used and 321 data testing was used, resulting in an accuracy of 93%.

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Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

Cited by 2 publications

References 15 publications

K-nearest neighbor algorithm analysis for path determination in network simulation using software defined network

K-nearest neighbor algorithm analysis for path determination in network simulation using software defined network

Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)

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