Prillantika, Jessica Rahma (2017) Perbandingan Model Gstar Dan Gstar-Filter Kalman Pada Peramalan Tingkat Inflasi Di Tiga Kota Di Jawa Timur. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam kehidupan sehari-hari, sering ditemukan data yang mempunyai keterkaitan antar waktu dan lokasi. Data seperti ini disebut data spasial. Tingkat inflasi merupakan salah satu jenis data spasial karena tidak hanya memiliki keterkaitan dengan kejadian pada waktu sebelumnya, tetapi juga mempunyai keterkaitan dengan lokasi lain. Pada penelitian ini dilakukan perbandingan antara GSTAR dan GSTAR-Filter Kalman untuk mendapatkan peramalan dengan tingkat error yang kecil. Filter Kalman merupakan salah satu estimator yang melakukan estimasi terhadap perubahan state karena gangguan dari white noise. Hasil akhir menunjukkan bahwa Filter Kalman mampu memperbaiki hasil ramalan GSTAR. Hal ini ditunjukkan melalui hasil simulasi berupa grafik dan diperjelas dengan nilai RMSE yang lebih kecil.
Kata Kunci : data spasial, inflasi, GSTAR, parameter, Estimasi Filter Kalman
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Up to now, often found data that has correlation between time and location. This data also known as spatial data. The rate of inflation is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research will be done comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.
Kata Kunci : spatial data, inflation, GSTAR, parameter, Kalman Filter Estimator
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSMa 519.535 Pri p-1 |
| Uncontrolled Keywords: | data spasial, inflasi, GSTAR, parameter, Estimasi Filter Kalman |
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Jessica Rahma Prillantika |
| Date Deposited: | 15 Dec 2017 07:56 |
| Last Modified: | 06 Mar 2019 02:25 |
| URI: | http://repository.its.ac.id/id/eprint/47430 |
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