DIGITAL IMAGE IDENTIFICATION OF PLANKTON USING REGIONPROPS AND BAGGING DECISION TREE ALGORITHM
Abstract
Peranan plankton sangat penting bagi kehidupan organisme disekitarnya, sehingga penelitian prihal plankton sangatlah dibutuhkan karena kaitannya dengan kelangsungan kehidupan mahluk hidup lainnya. Kendala yang sering didapatkan dalam hal penelitian plankton khususnya dalam hal pengidentifikasian plankton yaitu tidak efisiennya dalam aspek waktu dan organisme ini memiliki ukuran rata-rata yang sangat kecil. Dalam hal ini diperlukan alternatif yang lebih baik dalam pengidentifikasian jenis plankton ini dengan cara pemrosesan gambar pada citra plankton secara digital atau biasa disebut dengan istilah “Digital Image Processing”. Penelitian ini bertujuan untuk melakukan pengolahan citra digital plankton sebanyak 144 citra yang yang dibagi menjadi 75% sebagai data pelatihan dan 25% sebagai data pengujian, dan citra tersebut didapatkan dari riset pada yayasan Kanopi Indonesia. Dalam prosesnya citra ini dianalisa bentuk menggunakan fungsi Regionprops sehingga didapatkan fitur pembeda dari masing-masing jenis plankton. Setelah citra terekstraksi fitur nya selanjutnya dilakukan pengolahan data dengan mengklasifikasikan setiap jenis plankton tersebut. Untuk menghasilkan sebuah klasifikasi data yang lebih baik, dalam penelitian ini menggunakan algoritma Bagging Decision Tree dalam pengolahan data nya dan menghasilkan akurasi sebesar 92.59%. Algoritma Bagging Decision Tree ini cukup baik dan mudah untuk di implemntasikan kedalam sebuah program identifikasi jenis plankton, terbukti dengan pengujian pada data citra pengujian menghasilkan 33 citra teridentifikasi dengan benar dari total pengujian sebanyak 36 citra.
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