Extreme Learning Machine with Feature Extraction Using GLCM for Phosphorus Deficiency Identification of Cocoa Plants


Basri Basri(1*); Muhammad Assidiq(2); Harli A. Karim(3); Andi Nuraisyah(4);

(1) Universitas Al Asyariah Mandar
(2) Universitas Al Asyariah Mandar
(3) Universitas Al Asyariah Mandar
(4) Universitas Al Asyariah Mandar
(*) Corresponding Author

  

Abstract


This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurance Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM is analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. In the case study conducted and the methodology applied from data collection, algorithm development to validation, and measurement using ROC, it was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.


Keywords


extreme learning machine; GLCM feature extraction; phosphorus deficiency; cocoa

  
     

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