Enhancing Covid-19 Diagnosis: Glrlm Texture Analysis And Kelm For Lung X-Ray Classification

Authors

  • Dian C Rini Novitasari Mathematics Department, UIN Sunan Ampel
  • Alvin Nuralif Ramadanti Mathematics Department, UIN Sunan Ampel
  • Dina Zatusiva Haq Mathematics Department, UIN Sunan Ampel

Abstract

Abstrak This study aims to diagnose COVID-19 using GLRLM feature extraction, known for its high accuracy, and optimize Kernel Extreme Learning Machine (KELM) with Genetic Algorithm (GA) for improved computational efficiency, along with Principal Component Analysis (PCA) for data reduction. The gamma values in KELM are optimized using GA, yielding the best solution function. Results reveal that at angles of 0°, 45°, and 135°, the optimal gamma value with KELM is 1, while at 90°, GA determines it to be 1.35. This adjustment demonstrates the critical role of gamma values in achieving optimal performance. Performance analysis of various classification methods demonstrates that GLRLM-PCA-Optimized KELM outperforms others, achieving an accuracy exceeding 97%, particularly notable at 90° angles. This study shows that the importance of hyperparameter optimization in enhancing classification accuracy, revealing a significant improvement of over 1% compared to non-optimized models. Kata kunci: COVID-19, GLRLM, KELM, Feature Reduction, PCA   Abstract Penelitian ini bertujuan untuk mendiagnosis COVID-19 menggunakan ekstraksi fitur GLRLM yang dikenal dengan akurasi tinggi, dan mengoptimalkan Kernel Extreme Learning Machine (KELM) dengan Algoritma Genetika (GA) untuk meningkatkan efisiensi komputasi, bersama dengan Principal Component Analysis (PCA) untuk reduksi data. Nilai gamma dalam KELM dioptimalkan menggunakan GA, menghasilkan fungsi solusi terbaik. Hasil penelitian menunjukkan bahwa pada sudut 0°, 45°, dan 135°, nilai gamma optimal dengan KELM adalah 1, sedangkan pada 90°, GA menentukan nilainya menjadi 1,35. Penyesuaian ini menunjukkan peran penting nilai gamma dalam mencapai kinerja optimal. Analisis kinerja berbagai metode klasifikasi menunjukkan bahwa GLRLM-PCA-KELM yang Dioptimalkan mengungguli yang lain, mencapai akurasi lebih dari 97%, terutama mencolok pada sudut 90°. Studi ini menyoroti pentingnya optimasi hyperparameter dalam meningkatkan akurasi klasifikasi, mengungkapkan peningkatan signifikan lebih dari 1% dibandingkan dengan model KELM konvesional. Keywords: COVID-19, GLRLM, KELM, Feature Reduction, PCA

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Submitted

2024-05-24

Accepted

2024-06-13

Published

2024-10-05

Issue

Vol. 9 No. 1 (2024): Mei 2024

Section

Articles

License

Copyright (c) 2024 Dian C Rini Novitasari, Alvin Nuralif Ramadanti , Dina Zatusiva Haq

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