Implementasi Metode Haar Cascade Classifier Dalam Deteksi Objek Tanaman Bawang Merah
DOI:
https://doi.org/10.29407/inotek.v8i1.5010Abstract
Penelitian ini mengimplementasikan metode Haar Cascade Classifier untuk mendeteksi tanaman bawang merah, dengan fokus pada pemanfaatan teknologi Computer Vision. Latar belakang penelitian ini didasarkan pada pentingnya bawang merah dalam ekonomi Indonesia dan tantangan yang dihadapi petani akibat serangan hama dan penyakit. Metode yang digunakan meliputi pengumpulan dataset positif dan negatif, pra-pemrosesan data, pelatihan metode, dan evaluasi hasil. Dataset positif diambil dari gambar tanaman bawang merah, sedangkan dataset negatif terdiri dari gambar tanpa objek bawang merah. Evaluasi dilakukan dengan mengukur akurasi, presisi, recall, dan f1 score. Hasil penelitian menunjukkan bahwa skenario pengujian terbaik memiliki nilai akurasi 100%, menunjukkan pentingnya optimasi parameter seperti Scale Factor dan Minimum Neighbor. Kesimpulannya, Haar Cascade Classifier efektif dalam mendeteksi bawang merah, dan pengoptimalan lebih lanjut dapat meningkatkan kinerja sistem ini untuk mendukung petani.
Keywords:
Deteksi objek, Haar Cascade Classifier, Bawang merah, Computer Vision, Pertanian##plugins.themes.default.displayStats.downloads##
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