Yunianti, Nia (2021) IMPLEMENTASI ALGORITMA PRINCIPAL COMPONENT ANALYSIS DAN KNN UNTUK KLASIFIKASI JENIS TANAMAN AGLONEMA. Skripsi (S1) thesis, Universitas Muhammadiyah Ponorogo.
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Abstract
IMPLEMENTASI ALGORITMA PRINCIPAL COMPONENT ANALYSIS DAN KNN UNTUK KLASIFIKASI JENIS TANAMAN AGLONEMA Nia Yunianti, Ida Widaningrum, Khoiru Nurfitri Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Ponorogo e-mail : niayunianti170@gmail.com ABSTRAK Tumbuhan Aglonema masih sangat sulit untuk dikenali. Banyak pecinta aglonema atau petani masih sulit untuk mengidentifikasi beberapa jenis aglaonema karena banyaknya jenis aglaonema baru dari hasil persilangan para ahli. Karena banyak sekali tumbuhan Aglaonema dengan jenis yang berbeda yaitu mempunyai corak dan motif yang hampir sama. Dibutuhkan sebuah teknologi untuk mengenali tanaman algonema berdasarkan ciri-ciri yang dimiliki. Principal Component Analysis sebuah metode yang dibutuhkan dalam ekstraksi ciri dan metode K Nearest Neighbor untuk pengklasifikasian jenis tanaman aglonema. Implementasi PCA dan KNN mampu membedakan dari 5 jenis tanaman aglonema yaitu Snow White, Widuri, Dona Carmen, Red Kochin, dan Lipstik. Pada penelitian dimulai dari pengambilan data sampel dari 5 jenis tanaman aglonema. Dilanjutkan proses segmentasi, ekstraksi ciri, pelatihan dan pengujian. Pada proses ekstraksi ciri terdiri dari RGB, HSV, dan area. Terdiri dari 100 data latih dari 5 jenis tanaman aglonema serta 25 data uji. Hasil pengujian akurasi untuk klasifikasi jenis tanaman aglonema diperoleh akurasi sebesar 96%. Kata Kunci : Aglonema, PCA, KNN ABSTRACT Aglonema plants are still very difficult to identify. Many aglonema lovers or farmers are still difficult to identify several types of aglaonema because of the many new types of aglaonema from crosses by experts. Because there are so many Aglaonema plants with different types, which have almost the same patterns and motifs. It takes a technology to recognize algonema plants based on the characteristics they have. Principal Component Analysis is a method needed in feature extraction and the K Nearest Neighbor method for classifying aglonema plant species. The implementation of PCA and KNN is able to distinguish between 5 types of aglonema plants, namely Snow White, Thistle, Dona Carmen, Red Kochin, and Lipstick. The research started from taking sample data from 5 types of aglonema plants. The process of segmentation, feature extraction, training and testing is continued. The feature extraction process consists of RGB, HSV, and area. Consists of 100 training data from 5 types of aglonema plants and 25 test data. The results of the accuracy test for the classification of aglonema plant species obtained an accuracy of 96%. Key word : Aglonema, PCA, KNN
| Item Type: | Thesis (Skripsi (S1)) |
|---|---|
| Uncontrolled Keywords: | Aglonema, PCA, KNN, Klasifikasi |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | ft . userft |
| Date Deposited: | 07 Sep 2021 03:19 |
| Last Modified: | 02 Nov 2021 01:51 |
| URI: | http://eprints.umpo.ac.id/id/eprint/7752 |
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