ILKOM Jurnal Ilmiah
Vol 14, No 2 (2022)

Extreme learning machine with feature extraction using GLCM for phosphorus deficiency identification of cocoa plants

Basri Basri (Universitas Al Asyariah Mandar)
Muhammad Assidiq (Universitas Al Asyariah Mandar)
Harli A. Karim (Universitas Al Asyariah Mandar)
Andi Nuraisyah (Universitas Al Asyariah Mandar)

Article Info

Publish Date
31 Aug 2022


This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence 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 was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with 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.

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Journal Info





Computer Science & IT


ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, ...