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<http://digilib.unila.ac.id/id/eprint/68665> <http://purl.org/dc/terms/title> "PENERAPAN METODE AUTOREGRESSIVE FRACTIONALLY\r\nINTEGRATED MOVING AVERAGE (ARFIMA) PADA DATA\r\nNILAI EKSPOR MIGAS"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/68665> <http://purl.org/ontology/bibo/abstract> "The ARFIMA model is a development of the ARIMA model which has the\r\nadvantage of being able to explain short and long term time series. The ARFIMA\r\nmodel was first introduced by Grager (1980) which was then continued by\r\nHosking (1981) regarding the study of the properties of long memory. This study\r\naims to determine the best model by comparing the Akaike Information Criterion\r\n(AIC) values and measuring the estimated value in the form of a percentage of the\r\naverage error of the model obtained. The data used in this study is secondary data\r\nobtained from the Central Bureau of Statistics regarding the Value of Oil and Gas\r\nExports for the period January 2012 to December 2021. Based on the research that\r\nhas been conducted it can be concluded that the model chosen for forecasting Oil\r\nand Gas Exports, namely ARFIMA(2;0.045; 0) with an AIC value of 1316.237\r\nand a model accuracy value of 1.39%.\r\nKeywords: ARFIMA, Long Memory, Oil and Gas Export Value, AIC, MAPE.\r\n\r\n\r\nModel ARFIMA merupakan pengembangan dari model ARIMA yang memiliki\r\nkelebihan dapat menjelaskan runtun waktu jangka pendek maupun panjang.\r\nModel ARFIMA pertama kali diperkenalkan oleh Grager (1980) yang kemudian\r\ndilanjutkan oleh Hosking (1981) mengenai pengkajian terhadap sifat-sifat long\r\nmemory. Penelitian ini bertujuan untuk menentukan model terbaik dengan\r\nmembandingkan nilai Akaike Information Criterion (AIC) dan mengukur nilai\r\ndugaan dalam bentuk persentase dari rata-rata galat model yang didapatkan. Data\r\nyang digunakan dalam penelitian ini ialah data sekunder yang diperoleh dari\r\nBadan Pusat Statistik tentang Nilai Ekspor Migas periode Januari 2012 sampai\r\ndengan Desember 2021. Berdasarkan penelitian yang telah dilakukan dapat\r\ndisimpulkan bahwa model yang terpilih untuk prakiraan Ekspor Migas, yaitu\r\nARFIMA(2;0,045;0) dengan nilai AIC sebesar 1316,237 dan nilai keakuratan\r\nmodel sebesar 1,39%.\r\nKata Kunci: ARFIMA, Long Memory, Nilai Ekspor Migas, AIC, MAPE.\r\n"^^<http://www.w3.org/2001/XMLSchema#string> .
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<http://digilib.unila.ac.id/id/document/2099774> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/68665/12/indexcodes.txt> .
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<http://digilib.unila.ac.id/id/document/2099775> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2099764> .
<http://digilib.unila.ac.id/id/document/2099775> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2099764> .
<http://digilib.unila.ac.id/id/document/2099775> <http://eprints.org/relation/isIndexCodesVersionOf> <http://digilib.unila.ac.id/id/document/2099764> .
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<http://digilib.unila.ac.id/id/document/2099776> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2099765> .
<http://digilib.unila.ac.id/id/document/2099776> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2099765> .
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<http://digilib.unila.ac.id/id/document/2099776> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/68665/14/indexcodes.txt> .
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<http://digilib.unila.ac.id/id/document/2099777> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2099765> .
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<http://digilib.unila.ac.id/id/document/2099780> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2099765> .
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