Klasifikasi pada Citra Bunga dengan Ekstraksi Fitur Color Histogram
DOI:
https://doi.org/10.22441/format.2023.v12.i1.007Kata Kunci:
Ekstraksi Fitur, Color Histogram, Pembelajaran MesinAbstrak
Perbedaan ciri pada bunga menyebabkan perbedaan spesies bunga sehingga diperlukan pengetahuan untuk dapat mengklasifikasikannya. Bunga dapat dicirikan dari warnanya. Pada bunga dengan jenis yang sama bahkan bisa memiliki beberapa warna yang berbeda. Salah satu teknik untuk mendapatkan pola bisa dilakukan melalui ekstraksi fitur warna. Penelitian ini mencoba untuk melakukan ekstraksi citra dengan teknik color histogram. Penelitian yang menunjukkan efektifitas ekstraksi fitur warna sebelum melakukan klasifikasi sudah cukup banyak dilakukan. Color Histogram adalah teknik yang paling banyak digunakan untuk mengekstraksi fitur warna dari suatu citra karena mewakili gambar dari sudut pandang yang berbeda. Dataset yang digunakan dalam penelitian ini menggunakan citra bunga dengan bentuk yang hampir sama yaitu sunflower, calendula, black eyed susan, common daisy. Pembuatan model klasifikasi terhadap jenis-jenis bunga tersebut menggunakan algoritma pembelajaran mesin Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor, Linear Discriminant Analysis, Naive Bayes, Support Vector Machine. Selanjutnya Penulis menggunakan Confusion Matrix untuk mengevaluasi model terlatih dan untuk menghasilkan skor akurasi menggunakan 10-fold cross validation. Skor akurasi tertinggi diperoleh dari model Random Forest sebesar 82%.
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