Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/101588
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dc.contributor.authorRAMADHANI, Lutvia Citra-
dc.contributor.authorANGGRAENI, Dian-
dc.contributor.authorKAMSYAKAWUNI, Ahmad-
dc.date.accessioned2020-11-03T02:49:11Z-
dc.date.available2020-11-03T02:49:11Z-
dc.date.issued2019-01-02-
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/101588-
dc.description.abstractSaxena-Easo Fuzzy Time Series (FTS ) is a softcomputing method for forecasting using fuzzy concept. It doesn’t need any assumption like conventional forecasting method. Generally it’s focused on three important steps like percentage change as the universe of discourse, interval partition, and defuzzification. In this research, this method is applied to Indonesia’s inflation rate data. The aim of this research is to forecast Indonesia’s inflation rate in 2017 by using input from Autoregressive Integrated Moving Average (ARIMA ) process, Saxena-Easo FTS, and actual data from 1970-2016. ARIMA is focused on four steps like identifying, parameter estimation, diagnostic checking, and forecasting. The result for Indonesia’s inflation rate forecasting in 2017 is about 5.9182 using Saxena-Easo FTS. Root Mean Square Error (RMSE ) is also computed to compare the accuracy rate from each method between Saxena-Easo FTS and ARIMA. RMSE from Saxena-Easo FTS is about 0.9743 while ARIMA is about 6.3046.en_US
dc.language.isoInden_US
dc.publisherJurnal ILMU DASAR, Vol.20 No. 1, Januari 2019 : 53-60en_US
dc.subjectsaxena-easo fuzzy time seriesen_US
dc.subjectARIMAen_US
dc.subjectinflation rateen_US
dc.subjectRMSEen_US
dc.titleFuzzy Time Series Saxena-Easo Pada Peramalan Laju Inflasi Indonesia (Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting)en_US
dc.typeArticleen_US
dc.identifier.kodeprodikodeprodi1810101#Matematika-
dc.identifier.nidnNIDN0016028201-
dc.identifier.nidnNIDN0029117202-
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