IDENTIFIKASI SPESIES KUPU-KUPU MENGGUNAKAN FITUR GLCM DAN DETEKSI TEPI DENGAN ALGORITMA KNN (K-NEAREST NEIGHBOR) DAN DECISION TREE (C.45)

• MUHAMAD HASAN
• 14002203

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ABSTRAK

ABSTRAK

 

 

 

Nama                     :     Muhamad Hasan

NIM                      :     14002203

Program Studi       :     Magister Ilmu Komputer

Jenjang                  :     Strata Dua (S2)

Konsentrasi           :     Image Processing

Judul                     :     “Identifikasi Spesies Menggunakan Fitur GLCM dan Deteksi Tepi Menggunakan Algoritma KNN (K-Nearest Neighbor) dan Decision Tree (C.45)”

 

            Kupu-kupu adalah serangga yang berasal dari kingdom Animalia, yang merupakan kelas Insecta, ordo Lepidoptera dan sub ordo dari Rhopalocera. Kupu-kupu dapat diklasifikasikan menurut pola yang ditemukan pada sayap kupu-kupu. Spesies kupu-kupu memiliki pola berbeda berdasarkan pigmen, struktur sisik dan jatuhnya sinar matahari. Kelemahan mata manusia dalam membedakan pola pada kupu-kupu adalah fondasi dalam membangun identifikasi kupu-kupu berdasarkan pada pengenalan pola. Penelitian ini menggunakan 3 spesies kupu-kupu: Adonis, Black Hairstreak dan Gray Hairstreak. Dataset kupu-kupu yang digunakan adalah 150 yang diperoleh secara online. Tahap pra-pemrosesan menggunakan metode segmentasi dan deteksi tepi. Tahap ekstraksi fitur menggunakan metode Graylevel Co-occurrence Matriks (GLCM) yang mengekstraksi 8 fitur bentuk dan tekstur diantaranya area, perimeter, metric, eccentricity, contras, correlation, energy, dan homogeneity. Fase klasifikasi menggunakan metode K-Nearest Neighbor (KNN) dengan nilai k = 3, 5, 7, 9, 11, 13, 15, 17 dan 19, serta metode Decision Tree (C.45). Hasil Identifikasi kupu-kupu dengan akurasi tertinggi diperoleh oleh Algoritma KNN dari pengujian dengan nilai k = 3 sebesar 93,33%, sedangkan hasil akurasi dengan metode Decision Tree (C.45) dengan akurasi 84,44%. Sementara hasil identifikasi yang diperoleh menggunakan aplikasi yang dibuat menggunakan GUI Matlab2017 dengan algoritma KNN memperoleh akurasi sebesar 93,33% dengan nilai k = 3.

Kata kunci: Kupu-Kupu, K-Means, GLCM, Canny, KNN, Decision Tree, Identifikasi

KATA KUNCI

K-Nearest Neighbor

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

 

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