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2 hasil
Subjek: Matlab
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ThesisOpen AccessMetadata saja

Pengenalan Jenis Bunga Anggrek Dendrobium Dengan Ciri Warna Menggunakan Metode K – Nearest Neighbor (KNN)

•Skripsi, Thesis, Disertasi, dan Tugas Akhir

Muhammad Fajar Firdaus, C1655201026, 2020. Introduction of Dendrobium Orchid Species With Color Characteristics Using the K-Nearest Neighbor (KNN) method, Supervisor I Sulistyowati, S.Kom., M.Cs., Supervisor II Catharina Elmayantie, M.Pd Orchid is one of the types of plants that are in great demand because of its attractive flower appearance. Orchids are often used as home decorations, religious ceremonies and as congratulations. The types are widespread throughout the world. Some types of orchids that are widely known include Cattleya, Cymbidium, Dendrobium, Phalaenopsis, and Vanda. Dendrobium is an extraordinary type of orchid both in quantity and in the beauty of flowers. Because of the diverse shapes and colors and the many types of orchids derived from crossing, orchids have very many species and species. The number of orchid species and species makes it very difficult for people to recognize orchid species and species, so a system based on appropriate literary and expert references is established, namely the introduction of dendrobium orchid species with color characteristics using the K-Nearest Neighbor (KNN) method, to identify types of orchids more easily and precisely. Flower colors can be identified through the extraction of color features that can be extracted through the calculation of the average value of Red Green Blue (RGB) and Hue Saturation Value (HSV) images. The results of the two extractions can then be classified by the K-Nearest Neighbor (KNN) method to determine the appropriate type of orchid. KNN classifies objects based on the characteristics of learning data (training data) that are closest to the object's characteristics. Based on the test results, the highest system accuracy reaches 100% with a value of k = 5 and 62.5% for a value of k = 10.

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ThesisOpen AccessMetadata saja

Aplikasi Pendeteksi Kematangan Cabai Rawit Menggunakan Metode Transformasi Ruang Warna HSI

•Skripsi, Thesis, Disertasi, dan Tugas Akhir

Meilyana Wibowo, C1855201044, 2021. Aplikasi Pendeteksi Kamatangan Cabai Rawit Menggunakan Metode Transformasi Ruang Warna HSI, Pembimbing I Lili Rusdiana, M.Kom., Pembimbing II Veny Cahya Hardita, M.Kom Utilization of digital images can be used to create maturity detection applications using color image processing. With the digital image, the process of determining the maturity of cayenne pepper can be done computing. The color model used is Hue Saturation Intensity (HSI). Utilization of HSI color imagery is used to make an application for detecting the maturity of cayenne pepper with the suitability of the color of cayenne pepper. The results of the test of 53 data tested in the application are 15 red cayenne pepper data, the output is ripe, 15 green cayenne pepper data is raw, 14 cayenne pepper data is yellow-orange, the output is half cooked, 1 yellow-orange cayenne pepper is ripe. , 2 red-brown rotten chilies with ripe output, 3 green chilies with raw rotten outpits, 1 uneven color chili with orange-yellow dominant with ripe output and 2 chili peppers with uneven color but dominant orange-yellow color with raw output.

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