Model Mixed Geographically Weighted Regression ; Studi Kasus: Persentase Rumah Tangga Miskin di Kabupaten Mojokerto Tahun 2008

Yasin, Hasbi (2011) Model Mixed Geographically Weighted Regression ; Studi Kasus: Persentase Rumah Tangga Miskin di Kabupaten Mojokerto Tahun 2008. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Analisis regresi merupakan analisis statistik yang bertujuan untuk memodelkan hubungan antara variabel respon dengan variabel prediktor. Geographically Weighted Regression (GWR) adalah bentuk lokal dari regresi dan merupakan metode statistik yang digunakan untuk menganalisis data spatial. Jika sebagian variabel prediktor bersifat global sedangkan yang lainnya bersifat local maka metode yang digunakan adalah Mixed Geographically Weighted Regression (MGWR). Hasil penelitian menunjukkan bahwa estimasi parameter model MGWR dapat digunakan metode Weighted Least Square (WLS). Pemilihan bandwidth optimum digunakan metode Cross Validation (CV). Pengujian kesesuaian model regresi global dan MGWR didekati dengan distribusi F begitu juga pada pengujian parameter global dan parameter lokal secara serentak, sedangkan pengujian parameter model secara parsial menggunakan distribusi t. Aplikasi model MGWR pada persentase rumah tangga miskin di Kabupaten Mojokerto menunjukkan model MGWR berbeda signifikan dengan model regresi global. Berdasarkan nilai Akaike Information Criterion (AIC) antara model regresi global, GWR dan model MGWR, diketahui bahwa model MGWR dengan pembobot fungsi kernel Gaussian merupakan model yang terbaik digunakan untuk menganalisis persentase rumah tangga miskin di Kabupaten Mojokerto tahun 2008 karena memiliki nilai AIC yang terkecil.
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Regression analysis is a statistical analysis that aims to model the relationship between response variables with predictor variables. Geographically Weighted Regression (GWR) is statistical methods used for analyzed the spatial data in local form of regression. Where certain predictor variables influencing the response are global while others are local used the Mixed Geographically Weighted Regression (MGWR) model to solve the problem. The results showed that Weighted Least Square (WLS) can be used to estimate the parameter model and Cross Validation (CV) for the selection of the optimum bandwidth. Goodness of fits tests for a global regression model and MGWR approximated by F distribution as well as on the test of global parameters and local parameters simultaneously and for testing the partial model parameters using the t distribution. The applications of MGWR model in the percentage of poor households in Mojokerto showed that MGWR model differs significantly from the global regression model. Based on Akaike Information Criterion (AIC) values between the global regression model, GWR and MGWR model, it is known that the MGWR model with a weighting Gaussian kernel function is the best model used to analyze the percentage of poor households in Mojokerto (2008) because it has the smallest AIC value.

Item Type:	Thesis (Masters)
Additional Information:	RTSt 519.536 Yas m 2011 3100011045229 (WEEDING)
Uncontrolled Keywords:	Akaike Information Criterion, Cross Validation, Fungsi Kernel Gaussian, Mixed Geographically Weighted Regression, Weighted Least Sguare
Subjects:	H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis.
Divisions:	Faculty of Mathematics and Science > Statistics > 49101-(S2) Master Thesis
Depositing User:	Anis Wulandari
Date Deposited:	02 Feb 2026 08:26
Last Modified:	02 Feb 2026 08:26
URI:	http://repository.its.ac.id/id/eprint/131839

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