PERAMALAN VOLUME KENDARAAN YANG MASUK KE KOTA BANDUNG DENGAN VECTOR AUTOREGRESSIVE-GENERALIZED SPACE TIME AUTOREGRESSIVE (VAR-GSTAR)

Winata, Hilma Mutiara (2017) PERAMALAN VOLUME KENDARAAN YANG MASUK KE KOTA BANDUNG DENGAN VECTOR AUTOREGRESSIVE-GENERALIZED SPACE TIME AUTOREGRESSIVE (VAR-GSTAR). S1 thesis, Universitas Pendidikan Indonesia.

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Abstract

Salah satu model yang dapat digunakan untuk peramalan pada suatu data deret waktu multivariat yang menggabungkan keterkaitan waktu dan lokasi adalah model Vector Autoregressive-Generalized Space Time Autoregressive (VAR-GSTAR). VAR-GSTAR adalah model VAR dengan skema respon prediktor yang direpresentasikan dalam skema pada model GSTAR. Model ini memiliki dua orde yaitu orde waktu yang diperoleh dari model VAR dan orde ruang yang ditentukan dari model GSTAR. Pada peneltian ini model VAR-GSTAR diterapkan dalam kasus peramalan volume kendaraan masuk ke Kota Bandung melalui gerbang tol yang beada di Kota Bandung. Bobot lokasi yang digunkan pada peneltian ini adalah bobot lokasi normalisasi korelasi silang dan bobot lokasi invers jarak. Diperoleh model VAR-GSTAR(11) dengan bobot lokasi normalisasi korelasi silang yang digunakan untuk peramalan karena memberikan kesalahan ramalan terkecil. Hasil dari peramalan model VAR-GSTAR(11) dengan bobot lokasi normalisasi korelasi silang selama 12 bulan mendatang cenderung mengikuti pola data yang sebelumnya, yaitu berfluktuasi dengan trend naik.;---
One of model that can be used for forecasting in a multivariate time series that combines the connectedness of time and location is Vector Autoregressive-Generalized Space Time Autoregressive (VAR-GSTAR). VAR-GSTAR is a VAR model with predictors of response scheme represented in the schematic in GSTAR. This model has two orders, the order of the time obtained from the VAR model and order the space obtained from GSTAR model. In the present study, the model VAR-GSTAR applied in the case of forecasting the volume of the vehicle entrance to the city of Bandung through toll gates in the city of Bandung. The weight of the locations used in this research is the weight normalized cross correlation and inverse distance weighting. Retrieved VAR-GSTAR (11) model with a weight normalized cross-correlation locations used for forecasting because it gives the smallest forecast error. Results of forecasting models VAR-GSTAR (11) with a weight normalized cross-correlation locations over the next 12 months are likely to follow the pattern of previous data, which fluctuates with the rising trend.

Item Type: Tugas Akhir,Skripsi,Tesis,Disertasi (S1)
Additional Information: No.panggil : S MTK WIN p-2017; Pembimbing : I.Entit Puspita, II.Fitriani Agustina.
Uncontrolled Keywords: Model VAR-GSTAR, Bobot Lokasi, Peramalan, VAR-GSTAR Model, Weights Locations, Forecasting.
Subjects: Q Science > QA Mathematics
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Matematika
Depositing User: Mr mhsinf 2017
Date Deposited: 22 Jan 2018 02:12
Last Modified: 22 Jan 2018 02:12
URI: http://repository.upi.edu/id/eprint/28630

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