32243000 32243200 32243400 32243600 32243800 32244000 32244200 32244400 32244600
21950000 21950500 21951000 21951500 21952000 21952500 21953000 21953500
X
Y
Tabel 5.4 Contoh Hasil Prakiraan Parameter Phi_1 GSTAR Kriging antara 2 Sumur
Distance Gamma Lambda phi_1 hat
d(x1,x2) d(x1,V) d(x2,V) g(x1,x2) g(x1,V) g(x2,V) Lambda1 Lambda2
3178.05
288.91 2889.14
0.002521
0.00023 0.00229 0.91 0.09 0.14
577.83 2600.22 0.00046 0.00206 0.82 0.18 0.15
866.74 2311.31 0.00069 0.00183 0.73 0.27 0.16
1155.65 2022.40 0.00092 0.00160 0.64 0.36 0.16 1444.57 1733.48 0.00115 0.00137 0.55 0.45 0.17 1733.48 1444.57 0.00137 0.00115 0.45 0.55 0.18 2022.40 1155.65 0.00160 0.00092 0.36 0.64 0.18
2311.31 866.74 0.00183 0.00069 0.27 0.73 0.19
2600.22 577.83 0.00206 0.00046 0.18 0.82 0.20
2889.14 288.91 0.00229 0.00023 0.09 0.91 0.20
6. Simpulan
Dalam paper ini dikaji pendekatan model linier untuk proses stokastik berupa model spasial berdasarkan data cross section berupa model SAR dan ekspansi SAR serta data time series berupa model GSTAR. Kekurangan model-model tersebut tidak dapat digunbakan untuk prakiraan di lokasi-lokasi tidak tersampel. Oleh karena itu, dikembangkan model SAR-Kriging dan GSTAR-Kriging yang merupakan gabungan model kausal dengan pendekatan model linier dengan metode kriging untuk prakiraan di lokasi tidak tersampel. Kedua pengembangan model tersebut diterapkan pada data lapangan, khususnya pada mutu pendidikan untuk data cross section dan data produksi bulanan sumur-sumur minyak bumi untuk data time series. Secara keseleuruhan kedua pengmbangan model spasial tersebut dapat menggambarkan fenomena lapangan dalam menggambarkan pengaruh antar lokasi, maupun pengaruh interaksi antara lokasi dan waktu.
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