Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi

Abstract

Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.

Klasifikasi Ayam Petelur Menggunakan Artificial Neural Network dan Decision Tree

“…Hasil dalam pengujian tersebut menunjukkan 45% bobot ayam dari 320 data yang di uji. Hal ini menunjukkan sistem klasifikasi otomatis masih dikatakan rendah dalam kinerjanya karena masih di bawah 70% [11][6] [12] .…”

“…Hasil dalam penelitian tersebut memiliki intensitas warna yang tinggi, yakni berwarna merah kecoklatan mencapai yakni sekitar 2,95-4,25 keakuratannya atau mencapai 56% dari 150 data ayam. Namun, dalam penelitian tersebut masih dikatakan rendah sistem kinerjanya karena masih di bawah 70% [11][7] [15] .…”

Klasifikasi Kualitas Biji Kopi Menggunakan MultilayerPerceptron Berbasis Fitur Warna LCH

“…Hasil klasifikasi menunjukkan bahwa tingkat akurasi yang diperoleh adalah 70%. Meningkatkan kinerja akurasi dibutuhkan penambahan pada fitur warna dan ruang warna agar hasilklasifikasi lebih maksimal [12].…”