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All Journal Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Nuris Sayyidatul Fatimah
Universitas Muhammadiyah Gresik
Computer Science & IT

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Klasifikasi Lahan Perkebunan Kelapa Sawit Pada Citra Foto Udara Menggunakan Metode Local Binary Pattern dan Klasifikasi SVM Triyunita Nur Hayati; Nuris Sayyidatul Fatimah; Lailatul Fitria; Soffiana Agustin
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 3 (2024): Juli : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v1i3.1399

Abstract

Land classification for oil palm plantations is an important topic in agricultural and plantation development. In this research, the local binary pattern (LBP) method and support vector machine (SVM) classification were used to identify oil palm plantations from aerial photography images. The main challenge in this process is accurately distinguishing oil palm fields and forests that have similar patterns and colors in satellite images. The LBP method is used to extract important texture features from images, while SVM is used to build a classification model based on these features. The test results show that using this method provides an accuracy value of 83.33% in the classification of oil palm land images. The development of oil palm plantations in Indonesia is becoming increasingly important as investment prospects strengthen. This research helps develop image classification technology to support the agricultural industry.
Co-Authors Lailatul Fitria Soffiana Agustin, Soffiana Triyunita Nur Hayati