Analisis Citra Digital untuk Klasifikasi Kematangan Kelapa Menggunakan K-Nearest Neighbor
DOI:
https://doi.org/10.55338/jumin.v6i4.6783Keywords:
Klasifikasi kelapa, Pengolahan citra digital, K-Nearest Neighbors, Kematangan buah, Ruang warna RGBAbstract
Penelitian ini membahas pengembangan sistem klasifikasi tingkat kematangan kelapa berbasis pengolahan citra digital dengan algoritma K-Nearest Neighbors (KNN). Tujuannya adalah menyediakan metode penilaian kualitas kelapa yang lebih objektif, konsisten, dan efisien dibandingkan metode manual. Dataset citra kelapa diperoleh melalui proses akuisisi terkontrol, diikuti tahapan pra-pemrosesan, konversi ruang warna RGB ke HSV, dan ekstraksi fitur warna. Model KNN dioptimalkan melalui validasi silang untuk menentukan nilai k terbaik, yaitu k = 3. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, serta analisis Receiver Operating Characteristic (ROC) dan Area Under Curve (AUC). Hasil menunjukkan akurasi rata-rata 92%, dengan nilai AUC di atas 0,90 untuk semua kelas (Muda, Setengah Matang, Matang), mengindikasikan kinerja model yang sangat baik. Kesalahan klasifikasi paling sering terjadi pada kelas Setengah Matang dan Matang karena kemiripan warna. Temuan ini menegaskan potensi KNN untuk klasifikasi kualitas kelapa berbasis warna, meskipun peningkatan dapat dilakukan melalui fitur tekstur, augmentasi data, dan pengujian pada kondisi pencahayaan yang bervariasi. Sistem ini diharapkan dapat mendukung otomasi sortasi kelapa di tingkat petani maupun industri.
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Paisal Hamid Marpaung,
Universitas Muhammadiyah Tapanuli Selatan,
Indonesia 








