leman, D Deteksi Penyakit Pada Daun Pisang dengan Penggunakan Algoritma Local Binary Pattern Dan K-Nearest Neighbor

Dedi Leman; Obedh Eliezer Sidauruk

doi:10.70340/jirsi.v3i2.134

Authors

Dedi Leman Universitas Potensi Utama
Obedh Eliezer Sidauruk Universitas Potensi Utama

DOI:

https://doi.org/10.70340/jirsi.v3i2.134

Keywords:

Leaf, Banana, Local Binary Pattern, K-Nearest Neighbor (K-NN)

Abstract

Bananas are a type of fruit that has high production and is liked by many people. Mango productivity fluctuates from year to year. This is due to fluctuations in harvest area, plants that have not produced optimally, climate disturbances and attacks by various pests and diseases which are factors inhibiting banana growth and production in Indonesia. This identification will take a relatively long time and produce various diseases on banana leaves because humans have visual limitations in identifying, the level of fatigue and differences in opinion about diseases on banana leaves. The process of recognizing leaf patterns can be done by recognizing the characteristics of leaf structures such as leaf shape and texture. The method used in this research is Local Binary Pattern, an algorithm that can be used to classify based on images. In this study, 4 types of banana leaf diseases were used. Based on the results of the accuracy test, an accuracy value of 90.5% was obtained for the disease detection process on banana leaves for 10 pieces of data.

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References

N. Hayati, “Klasifikasi Jenis Bunga Mawar Menggunakan Algoritma K-Nearest Neighbour,” J. Inform. dan Ris., vol. 1, no. 1, pp. 31–37, 2023, doi: 10.36308/iris.v1i1.474.

L. P. R. Noviana and I. N. B. S. Nugraha, “Perbandingan Klasifikasi Citra Daun Herbal Menggunakan Metode Logistic Regression dan Decision Tree Classifier Berdasarkan Fitur (Warna, GLCM, Bentuk),” JITU J. Inform. Technol. Commun., vol. 7, no. 2, pp. 126–133, 2023, doi: 10.36596/jitu.v7i2.1241.

Ananta Dwi Prayoga Alwy, M Syahid Nur Wahid, Bukhari Naufal Nur Ag, and M Miftach Fakhri, “Klasifikasi Penyakit Pada Padi Dengan Ekstraksi Fitur LBP dan GLCM,” J. Deep Learn. Comput. Vis. Digit. Image Process., pp. 1–10, 2023, doi: 10.61255/decoding.v1i1.51.

Andri Nugraha Ramdhon and Fadly Febriya, “Penerapan Face Recognition Pada Sistem Presensi,” J. Appl. Comput. Sci. Technol., vol. 2, no. 1, pp. 12–17, 2021, doi: 10.52158/jacost.v2i1.121.

R. G. Guntara, “Ekstraksi Fitur Warna Citra Daun Untuk Klasifikasi Skala Klorofil dan Rekomendasi Pemupukan,” J. Minfo Polgan, vol. 11, no. 1, pp. 15–22, 2022, doi: 10.33395/jmp.v11i1.11644.

I. Hibatur Rahman, M. Rizki Pratama, R. Jaya Subita, A. Bima Fauzan, A. Novka Alana, and N. Pratiwi, “Pengujian Identifikasi Jumlah Kerumunan Face Recognition Menggunakan Haar Cascade Clasifier,” J. Ilm. Multidisiplin, vol. 2, pp. 58–65, 2023.

S. Talib, S. Sudin, and M. Dzikrullah Suratin, “Penerapan Metode Support Vector Machine (Svm) Pada Klasifikasi Jenis Cengkeh Berdasarkan Fitur Tekstur Daun,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 11, no. 1, pp. 26–34, 2024, doi: 10.30656/prosisko.v11i1.7911.

K. A. Zahroh, D. Candra, R. Novitasari, and L. Hakim, “Perbandingan Ekstraksi Fitur Untuk Klasifikasi COVID-19 , MERS , dan SARS Menggunakan Algoritma Extreme Learning Machine,” vol. 13, no. 1, pp. 30–41, 2024, doi: 10.14421/fourier.2024.131.30-41.