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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Simanjuntak, Andreas Jeremy Obet
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Computer Science & IT

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Klasifikasi Penyakit Daun Sawit Menggunakan Metode Jaringan Saraf Tiruan Dengan Fitur Local Binary Pattern Simanjuntak, Andreas Jeremy Obet; Udjulawa, Daniel
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3158

Abstract

Diseases on palm leaves are diseases caused by bacteria or fungi. One way to find out diseases on palm leaves is to observe the pattern on the surface of the palm leaves. The pattern on the palm leaves will be analyzed by an expert to find out whether there is disease on the palm leaves or not. This study aims to classify oil palm leaves whether there is disease or not on oil palm leaves by using a program. The right method is needed to produce good accuracy, the researcher uses the ANN (Artificial Neural Network) classification method and the LBP (Local Binary Pattern) extraction method. The steps carried out on the image before being classified are Grayscale, then extraction using LBP (Local Binary Pattern) and classification using ANN (Artificial Neural Network) using 17 train functions with the result that 5 neurons get an average accuracy of 81%, precision 95 %, and 94% recall. In 10 neurons get an average of 95% accuracy, 97% precision, and 96% recall. And the 20 neurons get an average of 97% accuracy, 97% precision, and 96% recall. Keywords: Palm leaf disease, LBP, ANN, neuron
Co-Authors Daniel Udjulawa