PERAMALAN JUMLAH WISATAWAN TEMPAT WISATA ALAM DI KABUPATEN BANDUNG DENGAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR)

Artianti, Shenni Rizky (2017) PERAMALAN JUMLAH WISATAWAN TEMPAT WISATA ALAM DI KABUPATEN BANDUNG DENGAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR). S1 thesis, Universitas Pendidikan Indonesia.

[img]
Preview
Text
S_MTK_1302036_Title.pdf

Download (392kB) | Preview
[img]
Preview
Text
S_MTK_1302036_Abstract.pdf

Download (343kB) | Preview
[img]
Preview
Text
S_MTK_1302036_Table_of_content.pdf

Download (405kB) | Preview
[img]
Preview
Text
S_MTK_1302036_Chapter1.pdf

Download (291kB) | Preview
[img] Text
S_MTK_1302036_Chapter2.pdf
Restricted to Staf Perpustakaan

Download (443kB)
[img]
Preview
Text
S_MTK_1302036_Chapter3.pdf

Download (536kB) | Preview
[img] Text
S_MTK_1302036_Chapter4.pdf
Restricted to Staf Perpustakaan

Download (574kB)
[img]
Preview
Text
S_MTK_1302036_Chapter5.pdf

Download (601kB) | Preview
[img]
Preview
Text
S_MTK_1302036_Bibliography.pdf

Download (466kB) | Preview
[img] Text
S_MTK_1302036_Appendix.pdf
Restricted to Staf Perpustakaan

Download (1MB)
Official URL: http://www.repository.upi.edu

Abstract

Arus wisatawan yang datang ke Kabupaten Bandung semakin meningkat dari waktu ke waktu. Kunjungan arus wisatawan ini memiliki keterkaitan antara lokasi dan waktu. Berdasarkan hal ini, kunjungan wisatawan dapat diterapkan dalam model ruang waktu seperti generalized space time autoregressive (GSTAR). Model GSTAR mempunyai orde spasial 1 dan orde autoregressive yang ditentukan dari orde model vector autoregressive (VAR). Penentuan orde model VAR menggunakan nilai Akaike’s Information Criterion (AIC). Model GSTAR mempunyai asumsi lokasi heterogen. Penggunaan pembobot lokasi pada model GSTAR menyatakan hubungan antar lokasi. Tujuan penelitian ini menerapkan model GSTAR pada jumlah wisatawan yang datang ke tempat wisata alam di Kabupaten Bandung yaitu Kawah Putih, Ranca Upas, Cimanggu dan Situ Patengan, dengan pembobot invers jarak dan biner. Setelah itu, memilih model GSTAR yang lebih baik untuk data wisatawan tersebut. Hasil dari penelitian ini dengan menerapkan data arus wisatawan diperoleh model GSTAR (21). Karena model GSTAR (21) dengan pembobot invers jarak memiliki nilai root mean square error (RMSE) yang lebih kecil dari model GSTAR (21) dengan pembobot biner , model dengan pembobot invers jarak lebih baik dibandingkan dengan pembobot biner.;---The flow of tourists who come to Bandung Regency is increasing from time to time. The flow of tourists visit has linked location and time. Based on this, tourists visit can be applied in space time model such as Generalized Space Time Autoregressive (GSTAR). GSTAR model has spatial 1 order and autoregressive order determined from Vector Autoregressive (VAR) model order. Determining the VAR model order use Akaike’s Information Criterion (AIC) value. GSTAR model has heterogeneous location assumption. The use of location weighting on GSTAR model showed the relationship between locations. The purpose of this research is to apply GSTAR on the number of tourists who come to the natural tourismin Bandung Regency such as Kawah Putih, Ranca Upas, Cimanggu and Situ Patengan, with inverse distance and binary weighting. After that, choose better GSTAR model for the tourist data. Results from this research by applying tourists flow data obtained GSTAR model (21). Because GSTAR model (21) with inverse distance weighting has Root Mean Square Error (RMSE) value smaller than GSTAR model (21) with binary weighting, model with inverse distance weighting is better than with binary weighting.

Item Type: Skripsi,Tesis,Disertasi (S1)
Additional Information: No.panggil : S MAT ART p-2017; Pembimbing : i.Entit Puspita, II.Fitriani Agustina.
Uncontrolled Keywords: Model GSTAR, Model STAR, lag time, lag spatial, bobot lokasi,GSTAR Model, STAR Model, Time Lag, Spatial Lag, Location Weighting.
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure
Q Science > QA Mathematics
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Matematika > Program Studi Matematika (non kependidikan)
Depositing User: Mr mhsinf 2017
Date Deposited: 22 Jan 2018 01:48
Last Modified: 22 Jan 2018 01:48
URI: http://repository.upi.edu/id/eprint/28563

Actions (login required)

View Item View Item