Hybrid Deep Feature Extraction dan Boosting Ensemble untuk Klasifikasi Multikelas Penyakit Daun Kopi Arabika
DOI:
https://doi.org/10.54065/artificial.2015Abstract
Penyakit pada daun kopi Arabika dapat menurunkan kualitas tanaman dan produktivitas apabila tidak diidentifikasi secara tepat. Proses klasifikasi berdasarkan citra daun menghadapi tantangan berupa kemiripan karakteristik visual antar kelas penyakit, sehingga diperlukan pendekatan yang mampu menghasilkan representasi fitur yang informatif. Penelitian ini bertujuan mengembangkan metode klasifikasi multikelas penyakit daun kopi Arabika dengan mengombinasikan deep feature extraction dan boosting ensemble. Dataset yang digunakan merupakan dataset JMuBEN dengan lima kelas, yaitu Cercospora, Healthy, Leaf Rust, Miner, dan Phoma. Sebanyak 58.549 citra digunakan dalam eksperimen, terdiri atas 46.836 citra data latih dan 11.713 citra data uji. Ekstraksi fitur dilakukan menggunakan DenseNet121 yang menghasilkan vektor fitur berdimensi 1.024, kemudian fitur tersebut diklasifikasikan menggunakan Logistic Regression dan Support Vector Machine (SVM) sebagai base classifier. Keluaran kedua classifier selanjutnya digunakan sebagai masukan XGBoost sebagai meta-classifier. Hasil pengujian menunjukkan bahwa Logistic Regression dan SVM masing-masing memperoleh akurasi 100% pada data latih dan data uji. Kombinasi keduanya melalui XGBoost menghasilkan 11.712 prediksi benar dari 11.713 data uji dengan akurasi 99,99%. Kesalahan klasifikasi hanya ditemukan pada satu citra Cercospora yang diprediksi sebagai Miner. Hasil tersebut menunjukkan bahwa pendekatan hybrid DenseNet121 dan boosting ensemble dapat digunakan untuk klasifikasi multikelas penyakit daun kopi Arabika dengan kinerja tinggi pada dataset yang digunakan.
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Copyright (c) 2026 Dandi Shayendra, Muhammad Akbar, Deni Nurdiansyah, Novi Lestari
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