International Journal of Supply Chain Management
Vol 9, No 4 (2020): International Journal of Supply Chain Management (IJSCM)

Principal Component Analysis (PCA) of Phytoplankton Community Relations Based on Physical-Chemical Structures for Supply Chain Management in the Waters of the Bangka Bay Region of West Bay

Julita Nahar (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, INDONESIA.)
Elis Hertini (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, INDONESIA.)



Article Info

Publish Date
28 Aug 2020

Abstract

Phytoplankton as one of the most important organisms in the territorial waters, which is the beginning of the food chain in a territorial waters. Phytoplankton have certain tolerance limits to physical-chemical factors so that they will form different community structures. The relationship between chemical physics parameters with phytoplankton communities in a territorial waters, can be used as an indicator of water quality. West Bangka Regency has considerable marine and fisheries resource potential, especially for marine aquaculture. So research on the structure of the phytoplankton community in relation to the physical-chemical parameters of seawater needs to be carried out to see indicators of fertility and availability of natural food in the waters that will be used as a marine aquaculture location. To find out the relationship between physico-chemical parameters and phytoplankton abundance, Principal Component Analysis (PCA) was used. Some physico-chemical parameters observed were temperature, brightness, pH, salinity, coated oxygen (DO), phosphate, nitrate, and ammonia. The results show the eigenvalues value of the main component 1 (PC1) represents about 41.78% of the diversity of data with its main identifying variable, namely temperature with a loading factor value of -0.885, brightness with a loading factor value of -0.8872 and salinity with a loading factor value of 0.824. For the main component 2 (PC2) represents about 28.16% of the diversity of data with its founder variable, pH with a loading factor value of -0.841. As for the main component 3 (PC3), it represents 18.67% of the diversity of data with its originating variable, Nitrate, with a loading factor value of -0,700. So that it can be formed into 3 clusters namely the first cluster is temperature and salinity, the second cluster is pH and nitrate and the third cluster is DO, phosphate, and ammonia.

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Journal Info

Abbrev

IJSCM

Publisher

Subject

Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

Description

International Journal of Supply Chain Management (IJSCM) is a peer-reviewed indexed journal, ISSN: 2050-7399 (Online), 2051-3771 (Print), that publishes original, high quality, supply chain management empirical research that will have a significant impact on SCM theory and practice. Manuscripts ...