PERBANDINGAN KEAKURATAN ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBORS DALAM KLASIFIKASI TINGKAT KUALITAS CABAI BESAR
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Cabai besar memiliki nilai ekonomi yang tinggi, namun kualitasnya sangat bergantung pada pemilihan dan klasifikasi yang akurat. Penelitian ini telah berhasil mengembangkan model klasifikasi otomatis untuk mendeteksi kualitas cabai menggunakan dua metode, yaitu Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). Hasil penelitian menunjukkan bahwa model SVM mencapai akurasi tertinggi sebesar 92% untuk masing-masing kategori cabai: "Cabai Baik", "Cabai Buruk", dan "Tidak Terdeteksi". Sementara itu, KNN juga memberikan hasil yang baik, dengan akurasi masing-masing mencapai 88% untuk kelas "Cabai Baik" dan "Tidak Terdeteksi", serta 86% untuk "Cabai Buruk". Hasil ini menegaskan efektivitas kedua model dalam mengklasifikasikan cabai berdasarkan fitur visualnya, yang dapat memberikan manfaat signifikan bagi para petani dan distributor dalam menentukan kualitas produk mereka
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