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Clustering Ornamental Plants Turnover Data Using K-Mean Algorithm To Predicate The Continuous Of Ornamental Plants Sales Businees Clustering Ornamental Plants Turnover Data Using K-Mean Algorithm To Predicate The Continuous Of Ornamental Plants Sales Businees

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Ornamental plants are a commodity with high production in Indonesia, with a 17.61
million stalk increase recorded in 2018. (9.55%). Ornamental plants have potential
business prospects in Indonesia as well. The increase and decrease in ornamental
plant turnover can be attributed to a variety of factors such as beauty awareness, the
development of the tourism industry, ornamental plant trends, and the construction
of housing and hotel complexes. A few of the factors mentioned can have an
indirect impact on the sustainability of the ornamental plant business. To resolve
these concerns, the grouping method was used with K-Means Clustering to
determine the equation of ornamental plant turnover data based on plant
commodities and monthly turnover values. The K-Means Clustering Algorithm is
used in this study to group turnover data based on crop commodities and turnover
value. The results of grouping using the K-Means Clustering Algorithm in the
WEKA application resulted in two clusters with values of 11% (8 data) and 89%
(66 data) from a total of 74 data, where the two cluster values appeared after three
time iterations

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Ketersediaan

SI 21/049

SI 21/049

Referensi

Tersedia

Informasi Detail

Judul Seri

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No. Panggil

SI 21/049

Penerbit

Jakarta : Universitas Trilogi., 2021

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

NONE

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Tipe Pembawa

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Edisi

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Pernyataan Tanggungjawab

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