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.
Highlights
Lovebird is a pet that many people in Indonesia have known
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%
Berdasarkan perbandingan yang sudah diketahui, Hasil yang didapatkan pada split ratio yang sama dengan perbandingan 60:40 pada Decision Tree J48 memiliki hasil precision 1.000, recall 1.000, f-measure 1.000 dan nilai accuracy mencapai 100%
Summary
Intensitas dalam citra, korelasi rumus 3 untuk mengukur 2.3.3 Fitur Warna keterkaitan linear pada derajat citra grayscale, energy. Lambang σ merupakan komponen dalam matriks co- Artificial Neural Network (ANN) digunakan pada occurence. Pada lambang i dan j menandakan bagian penelitian sebelumnya untuk klasifikasi citra burung dari intensitas yang berdekatan, dengan masing-masing lovebird. Pada fitur bentuk ini merupakan proses pengambilan ciri output layer. Nilai ciri yang diekstraksi pada Dimana pada rumus 9 xi(h-1) merupakan input pada fitur ini yaitu metric dan eccentricity. Variabel pada rumus 8 adalah M untuk metric, A adalah Untuk bj(h) melambangkan output pada neuron ke-j pada area, dan C adalah lingkar piksel yang memiliki ukuran layer ke-h. (7) Decision Tree J48 digunakan untuk klasifikasi citra burung lovebird yang telah diusulkan pada penelitian (8) ini. Perbandingan evaluasi ekstraksi fitur bentuk menggunakan Artificial Neural Network dan Decision Tree J48
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