Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/18896
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dc.contributor.authorNur Syarafina Mohamed-
dc.contributor.authorMustafa Mamat-
dc.contributor.authorMohd Rivaie-
dc.contributor.authorNur Hamizah Abdul Ghani-
dc.contributor.authorNorhaslinda Zull-
dc.contributor.authorSyazni Syoid-
dc.contributor.author(UniKL MITEC)-
dc.date.accessioned2018-05-08T08:10:47Z-
dc.date.available2018-05-08T08:10:47Z-
dc.date.issued2018-05-08-
dc.identifier.otherInternational Journal of Engineering & Technology-
dc.identifier.otherwww.sciencepubco.com/index.php/IJET-
dc.identifier.urihttp://ir.unikl.edu.my/jspui/handle/123456789/18896-
dc.description.abstractUnemployment rate is one of the major issues among Malaysian citizens. The unemployment rate indicates the percentage of the total workforce who are actively seeking employment and currently unemployed. In this paper, a data of unemployment rate of a state in Malaysia from year 2000 until 2015 is collected. The statistics data is extracted by Labour Force Survey Malaysia (LFSM) which was conducted monthly by using household approach targeted to working ages between 15 to 64 years old. An estimation data for year 2016 can be forecasted by using discrete least square method of numerical analysis and conjugate gradient method in unconstrained optimization. These methods have been chosen based on its simplicity and accuracy. The calculations are based on linear and quadratic models for each the method together with their errors. Results showed that the conjugate gradient method is comparable with the least square method.en_US
dc.language.isoenen_US
dc.relation.ispartofseries7 (2.15) (2018) 94-97;-
dc.subjectConjugate gradient methoden_US
dc.subjectLeast square methoden_US
dc.subjectUnconstrained optimizationen_US
dc.subjectWorkforceen_US
dc.titleEstimating the unemployment rate using least square and conjugate gradient methodsen_US
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