Large Detection of Mamalian Animals by Area and Range Using the K-MEANS Method
Mammals are a class of vertebrate animals with features such as hair and mammary glands. Mammals are spread almost all over the world and occupy different types of habitats, from the arctic to the equator, from the sea to the land. In this study, a program will be built using the K-means clustering...
| Published in: | J-ICOM - Jurnal Informatika dan Teknologi Komputer |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | Indonesian |
| Published: |
E-Jurnal Universitas Samudra
2021
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| Subjects: | |
| Online Access: | https://ejurnalunsam.id/index.php/jicom/article/view/3385 https://doi.org/10.33059/j-icom.v2i1.3385 |
| Summary: | Mammals are a class of vertebrate animals with features such as hair and mammary glands. Mammals are spread almost all over the world and occupy different types of habitats, from the arctic to the equator, from the sea to the land. In this study, a program will be built using the K-means clustering method. The program will classify 60 images from 3 types of mammal images, namely 20 images of bats, 20 images of fish and 20 images of frogs. The cluster results are presented in diagrammatic form. After conducting research on the number of centroids, it can be concluded that the more the number of centroids in each clustering process, the more specific the resulting cluster groups will be. Thus making conclusions on similarities in cluster groups is easier. Mamalia merupakan salah satu kelas dari hewan vertebrata dengan ciri seperti adanya rambut dan kelenjar susu. Hewan mamalia tersebar hampir diseluruh dunia dan menempati tipe habitat yang berbeda-beda, mulai dari daerah kutub sampai khatulistiwa, mulai dari laut hingga daratan. Dalam penelitian ini akan dibangun program dengan menerapkan metode K-means clustering. Program akan mengelompokkan 60 citra dari 3 jenis citra hewan mamalia yaitu 20 citra kelelawar, 20 citra ikan dan 20 citra katak. Hasil cluster disajikan dalam bentuk diagram. Setelah dilakukan penelitian terhadap jumlah centroidnya, dapat ditarik kesimpulan bahwa semakin banyak jumlah centroid dalam setiap proses clustering, maka makin spesifik kelompok cluster yang dihasilkan. Dengan demikian pengambilan kesimpulan kesamaan dalam kelompok cluster makin mudah. |
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