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

doi:10.59841/saber.v1i3.1399

Authors

Triyunita Nur Hayati Universitas Muhammadiyah Gresik
Nuris Sayyidatul Fatimah Universitas Muhammadiyah Gresik
Lailatul Fitria Universitas Muhammadiyah Gresik
Soffiana Agustin Universitas Muhammadiyah Gresik

DOI:

https://doi.org/10.59841/saber.v1i3.1399

Keywords:

palm oil, SVM, texture, image, LBP

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.

References

Christy Atika Sari, Wellia Shinta Sari, & Putri Mega Arum Wijayanti. (2022). Pengaruh Linear Binary Pattern (Lbp) Dalam Pengenalan Citra Aksara Jawa Berbasis Optical Character Recognition (Ocr). Seminar Nasional Teknologi Dan Multidisiplin Ilmu (SEMNASTEKMU), 2(1), 23–30. https://doi.org/10.51903/semnastekmu.v2i1.149

Farhan, N. M., & Setiaji, B. (2023). Indonesian Journal of Computer Science. Indonesian Journal of Computer Science, 12(2), 284–301. http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135

Fernando, E., Surjandy, S., Meyliana, M., & Siagian, P. (2020). Desain Sistem Pengenalan Varietas Bibit Tanaman Kelapa Sawit dengan Pendekatan Design Science Research Methodology (DSRM). Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(2), 249. https://doi.org/10.25126/jtiik.2020721456

Jochsen, E., Angeline, D., Herwindiati, D. E., & Hendryli, J. (2023). Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine. Journal of Computer System and Informatics (JoSYC), 5(1), 40–50. https://doi.org/10.47065/josyc.v5i1.4553

Neneng, N., Puspaningrum, A. S., & Aldino, A. A. (2021). Perbandingan Hasil Klasifikasi Jenis Daging Menggunakan Ekstraksi Ciri Tekstur Gray Level Co-occurrence Matrices (GLCM) Dan Local Binary Pattern (LBP). Smatika Jurnal, 11(01), 48–52. https://doi.org/10.32664/smatika.v11i01.572

Neneng, N., Putri, N. U., & Susanto, E. R. (2021). Klasifikasi Jenis Kayu Menggunakan Support Vector Machine Berdasarkan Ciri Tekstur Local Binary Pattern. Cybernetics, 4(02), 93–100. https://doi.org/10.29406/cbn.v4i02.2324

Prasetiyo, N., Baihaqi, K. A., Arum, S., Lestari, P., & Cahyana, Y. (2024). Classification of Rice Plants Affected By Rats Using the Support Vector Machine ( Svm ) Algorithm Klasifikasi Tanaman Padi Yang Terdampak Hama Tikus Menggunakan Algoritma Support Vector Machine ( Svm ). 5(2), 637–643.

Rahayu F., B. R., Mudjirahardjo, P., & Muslim, M. A. (2021). Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features. International Journal of Computer Applications Technology and Research, 10(6), 149–155. https://doi.org/10.7753/ijcatr1006.1004

Rosalina, E. , & Agustin, S. (2019). Klasifikasi Umur Lahan Perkebunan Kelapa Sawit Pada Citra Foto Udara Berdasarkan Tekstur Menggunakan Metode Naïve Bayes. INDEXIA : Infomatic and Computational Intelligent Journal, 1(1), 6. https://doi.org/10.30587/indexia.v1i1.820

Sipayung, T. (2023). Mengenal Pohon Kelapa Sawit dan Karakteristiknya. Palm Oil Agribusiness Strategic Policy Institute, 06, 406–415.