Estimasi Parameter Model Inflasi Untuk Menganalisa Pengaruh Covid-19 Menggunakan GSTAR-Filter Kalman

Agustina, Miftakhul Janah Seftia (2021) Estimasi Parameter Model Inflasi Untuk Menganalisa Pengaruh Covid-19 Menggunakan GSTAR-Filter Kalman. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pandemi Covid-19 selain mengganggu kesehatan manusia juga dapat mengganggu kesehatan ekonomi di seluruh dunia termasuk Indonesia. Dengan keadaan ekonomi yang tidak stabil akhir-akhir ini, permasalahan inflasi menjadi salah satu fokus penting bagi pemerintah. Inflasi merupakan salah satu indikator penting dalam stabilitas perekonomian bagi suatu negara. Oleh karena itu, perlu adanya pemodelan matematika yang sesuai yang dapat memprediksi inflasi di masa mendatang. Pengaruh Covid-19 terhadap inflasi dapat diamati dengan memperhatikan pergerakan inflasi terhadap Covid-19 berdasarkan plot data inflasi. Selanjutnya data inflasi dimodelkan menggunakan model Generalized Space Time Autoregressive (GSTAR) dengan menggunakan pembobotan invers jarak antar lokasi dan pembobotan normalisasi korelasi silang untuk mendapatkan model inflasi yang sesuai. Selanjutnya dilakukan estimasi pada parameter model menggunakan metode Filter Kalman (FK). Hasil akhir menunjukkan bahwa Filter Kalman mampu memperbaiki hasil estimasi pada model GSTAR sehingga didapatkan hasil prediksi yang mendekati data aktual. Hal ini ditunjukkan dengan hasil simulasi dan nilai MAPE yang lebih kecil dari pada nilai MAPE model GSTAR-OLS dan GSTAR-GLS sebesar 0.14302%.
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The Covid-19 pandemic, apart from disrupting human health, can also disrupt economic health throughout the world, including Indonesia. With the recent unstable economic conditions, the issue of inflation has become an important focus for the government. Inflation is one of the important indicators in economic stability for a country. Therefore, there is a need for an appropriate mathematical model that can predict future inflation. The effect of Covid-19 on inflation can be observed by observing the movement of inflation towards Covid-19 based on the inflation data plot. Furthermore, the inflation data is modeled using the Generalized Space Time Autoregressive (GSTAR) model using inverse weighting of distances between locations and weighting of normalized cross-correlation to obtain the appropriate inflation model. Furthermore, the estimation of the model parameters is carried out using the Kalman Filter (KF) method. The final result shows that the Kalman Filter is able to improve the estimation results on the GSTAR model so that prediction results are obtained that are close to the actual data. This is indicated by the simulation results and the MAPE value is smaller than the MAPE value of the GSTAR-OLS and GSTAR-GLS models which is 0.14302%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Inflasi, Generalized Space Time Autoregressive, Filter Kalman, Invers Jarak, Normalisasi Korelasi Silang, Inflation, Generalized Space Time Autoregressive, Kalman Filter, Distance inverse, Cross Correlation Normalization
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression Q Science > QA Mathematics > QA402.3 Kalman filtering.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Miftakhul Janah Seftia Agustina
Date Deposited:	27 Aug 2021 08:03
Last Modified:	27 Aug 2021 08:03
URI:	http://repository.its.ac.id/id/eprint/90036

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