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<http://digilib.unila.ac.id/id/eprint/83502> <http://purl.org/dc/terms/title> "PENERAPAN METODE HYBRID AUTOREGRESSIVE INTEGRATED\r\nMOVING AVERAGE (ARIMA)-ARTIFICIAL NEURAL NETWORK (ANN)\r\nDALAM MERAMALKAN TINGKAT INFLASI DI INDONESIA"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/83502> <http://purl.org/ontology/bibo/abstract> "Time series adalah data yang disusun berdasarkan urutan waktu, data time series\r\ndigunakan untuk meramalkan kejadian di masa depan. Metode deret waktu yang\r\nsering digunakan yaitu Autoregressive Integrated Moving Average (ARIMA) yang\r\nmerupakan model univariate yang menangkap pola linear pada suatu data. Metode\r\nArtificial Neural Network (ANN) merupakan metode yang menggunakan prinsip\r\njaringan syaraf manusia keuntungan dari metode ini yaitu memiliki kemampuan\r\nyang fleksibel dalam memodelkan pola nonlinear pada deret waktu. Sehingga\r\npenggunaan Metode hybrid ARIMA-ANN dapat memaksimalkan hasil peramalan\r\nyang berbentuk pola linear dan nonlinear. Penelitian ini bertujuan untuk\r\nmenerapkan metode hybrid ARIMA-ANN untuk meramalkan tingkat inflasi di\r\nIndonesia. Hasil analisis menunjukan penggunaan metode ARIMA menghasilkan\r\nnilai MAPE sebesar 0.0850 dan MSE sebesar 6.3713e-05. Kemudian dengan\r\nmetode hybrid ARIMA-ANN diperoleh penggunaan splitting data dengan skema\r\n70% data training 30% data testing memiliki performa yang terbaik dengan nilai\r\nMSE sebesar 1.2997e-05, MAPE sebesar 0.0968, dan akurasi sebesar 99.9032%.\r\nBerdasarkan hasil yang diperoleh nilai MSE metode hybrid dan hasil visualisasi\r\nperamalan metode hybrid lebih baik sehingga metode hybrid lebih baik digunakan\r\ndalam peramalan jangka panjang.\r\n\r\nKata Kunci: Hybrid ARIMA-ANN, ARIMA, ANN, Peramalan, Inflasi\r\nTime series is data organized by time sequence, time series data is used to forecast\r\nfuture events. Time series method that often used is Autoregressive Moving\r\nAverage (ARIMA) which is a univariate model that capture liniar pattern in a data.\r\nArtificial Neural Network (ANN) method is a method that used a principle of\r\nhuman neural network which have a benefit to had a flexible capabilities to model\r\na nonlinear pattern in time series. Regardy this capabilities, the us of hybrid\r\nARIMA-ANN method can maximize the result of forecast that had the liniar and\r\nnonlinear pattern. This study had a purpose to apply hybrid ARIMA-ANN method\r\nfor forecasting inflation rate in Indonesia. The analysis shows that the use of the\r\nARIMA method produces a MAPE value of 0.0850 and MSE of 6.3713e-05. Then\r\nwith the hybrid ARIMA-ANN method, it is found that the use of data splitting with\r\n70% training data 30% testing data scheme has the best performance with MSE\r\nvalue of 1.2997e-05, MAPE of 0.0968, and accuracy of 99.9032%. Based on the\r\nresult, MSE value from hybrid method and visualization forecasting result of hybrid\r\nmethod are better to use for forecasting in a long time period.\r\n\r\nKeywords: Hybrid ARIMA-ANN, ARIMA, ANN, Forecasting, Inflation"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/83502> <http://purl.org/dc/terms/date> "2024-04-01" .
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<http://digilib.unila.ac.id/id/document/2316140> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
<http://digilib.unila.ac.id/id/document/2316140> <http://www.w3.org/2000/01/rdf-schema#label> "PENERAPAN METODE HYBRID AUTOREGRESSIVE INTEGRATED\r\nMOVING AVERAGE (ARIMA)-ARTIFICIAL NEURAL NETWORK (ANN)\r\nDALAM MERAMALKAN TINGKAT INFLASI DI INDONESIA (Other)"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/document/2316140> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315976> .
<http://digilib.unila.ac.id/id/document/2316140> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315976> .
<http://digilib.unila.ac.id/id/document/2316140> <http://eprints.org/relation/isIndexCodesVersionOf> <http://digilib.unila.ac.id/id/document/2315976> .
<http://digilib.unila.ac.id/id/document/2316140> <http://eprints.org/ontology/hasFile> <http://digilib.unila.ac.id/83502/12/indexcodes.txt> .
<http://digilib.unila.ac.id/id/document/2316140> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/12/indexcodes.txt> .
<http://digilib.unila.ac.id/83502/12/indexcodes.txt> <http://www.w3.org/2000/01/rdf-schema#label> "indexcodes.txt"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/83502> <http://eprints.org/ontology/hasDocument> <http://digilib.unila.ac.id/id/document/2316141> .
<http://digilib.unila.ac.id/id/document/2316141> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
<http://digilib.unila.ac.id/id/document/2316141> <http://www.w3.org/2000/01/rdf-schema#label> "PENERAPAN METODE HYBRID AUTOREGRESSIVE INTEGRATED\r\nMOVING AVERAGE (ARIMA)-ARTIFICIAL NEURAL NETWORK (ANN)\r\nDALAM MERAMALKAN TINGKAT INFLASI DI INDONESIA (Other)"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/document/2316141> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315977> .
<http://digilib.unila.ac.id/id/document/2316141> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315977> .
<http://digilib.unila.ac.id/id/document/2316141> <http://eprints.org/relation/isIndexCodesVersionOf> <http://digilib.unila.ac.id/id/document/2315977> .
<http://digilib.unila.ac.id/id/eprint/83502> <http://eprints.org/ontology/hasDocument> <http://digilib.unila.ac.id/id/document/2316142> .
<http://digilib.unila.ac.id/id/document/2316142> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
<http://digilib.unila.ac.id/id/document/2316142> <http://www.w3.org/2000/01/rdf-schema#label> "PENERAPAN METODE HYBRID AUTOREGRESSIVE INTEGRATED\r\nMOVING AVERAGE (ARIMA)-ARTIFICIAL NEURAL NETWORK (ANN)\r\nDALAM MERAMALKAN TINGKAT INFLASI DI INDONESIA (Other)"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/document/2316142> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316142> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316142> <http://eprints.org/relation/isIndexCodesVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
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<http://digilib.unila.ac.id/id/document/2316142> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/14/indexcodes.txt> .
<http://digilib.unila.ac.id/83502/14/indexcodes.txt> <http://www.w3.org/2000/01/rdf-schema#label> "indexcodes.txt"^^<http://www.w3.org/2001/XMLSchema#string> .
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<http://digilib.unila.ac.id/id/document/2316149> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316149> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316149> <http://eprints.org/relation/islightboxThumbnailVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
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<http://digilib.unila.ac.id/id/document/2316149> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/15/lightbox.jpg> .
<http://digilib.unila.ac.id/83502/15/lightbox.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "lightbox.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
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<http://digilib.unila.ac.id/id/document/2316150> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
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<http://digilib.unila.ac.id/id/document/2316150> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316150> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316150> <http://eprints.org/relation/ispreviewThumbnailVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
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<http://digilib.unila.ac.id/id/document/2316150> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/16/preview.jpg> .
<http://digilib.unila.ac.id/83502/16/preview.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "preview.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
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<http://digilib.unila.ac.id/id/document/2316151> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
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<http://digilib.unila.ac.id/id/document/2316151> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316151> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
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<http://digilib.unila.ac.id/id/document/2316151> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/17/medium.jpg> .
<http://digilib.unila.ac.id/83502/17/medium.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "medium.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
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<http://digilib.unila.ac.id/id/document/2316152> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
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<http://digilib.unila.ac.id/id/document/2316152> <http://eprints.org/relation/isVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316152> <http://eprints.org/relation/isVolatileVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
<http://digilib.unila.ac.id/id/document/2316152> <http://eprints.org/relation/issmallThumbnailVersionOf> <http://digilib.unila.ac.id/id/document/2315978> .
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<http://digilib.unila.ac.id/id/document/2316152> <http://purl.org/dc/terms/hasPart> <http://digilib.unila.ac.id/83502/18/small.jpg> .
<http://digilib.unila.ac.id/83502/18/small.jpg> <http://www.w3.org/2000/01/rdf-schema#label> "small.jpg"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/83502> <http://www.w3.org/2000/01/rdf-schema#seeAlso> <http://digilib.unila.ac.id/83502/> .
<http://digilib.unila.ac.id/83502/> <http://purl.org/dc/elements/1.1/title> "HTML Summary of #83502 \n\nPENERAPAN METODE HYBRID AUTOREGRESSIVE INTEGRATED&#13;\nMOVING AVERAGE (ARIMA)-ARTIFICIAL NEURAL NETWORK (ANN)&#13;\nDALAM MERAMALKAN TINGKAT INFLASI DI INDONESIA\n\n" .
<http://digilib.unila.ac.id/83502/> <http://purl.org/dc/elements/1.1/format> "text/html" .
<http://digilib.unila.ac.id/83502/> <http://xmlns.com/foaf/0.1/primaryTopic> <http://digilib.unila.ac.id/id/eprint/83502> .
<http://digilib.unila.ac.id/id/subject/500> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2004/02/skos/core#Concept> .
<http://digilib.unila.ac.id/id/subject/500> <http://www.w3.org/2004/02/skos/core#prefLabel> "500 ilmu pengetahuan alam dan matematika" .
<http://digilib.unila.ac.id/id/eprint/83502> <http://purl.org/dc/terms/subject> <http://digilib.unila.ac.id/id/subject/500> .