Analyze of Classification Accaptence Subsidy Food Using Kernel Discriminant

Prahutama, Alan and Mukid, Moch. Abdul (2015) Analyze of Classification Accaptence Subsidy Food Using Kernel Discriminant. In: The 5th International Seminar on New Paradigm and Innovation on Natural Sciences and Its Application (5th ISNPINSA) i , 7-8 Oktober 2015, Semarang.

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Abstract

Subsidy food is government program for social protection to poor households. The aims of this program are to effort households from starve and to decrease poverty. Less precisely target of this program has negative impact. So that to successful program, it’s important to know accuracy classification of admission subsidy food. The variables classification are number of household members, number of household member in work, average expenditure capita, weighted household, and floor area. Discriminant analysis is a multivariate statistical technique which can be used to classify the new observation into a specific group. Kernel discriminant analysis is a non-parametric method which is flexible because it does not have to concern about assumption from certain distribution and equal variance matrices as in parametric discriminant analysis. The classification using the kernel discriminant analysis with the normal kernel function with optimum bandwidth 0.6 gives accurate classification 75.35%.

Item Type:Conference or Workshop Item (Paper)
Subjects:Q Science > Q Science (General)
Divisions:Faculty of Science and Mathematics > Department of Chemistry
ID Code:62542
Deposited By:Faculty of Engineering
Deposited On:28 May 2018 09:18
Last Modified:28 May 2018 09:18

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