EL FAHMI, ELOK FAIZ FATMA (2016) MODEL REGRESI PROBIT SPASIAL PADA INDEKS PEMBANGUNAN MANUSIA (IPM) DI JAWA TIMUR. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Indeks Pembangunan Manusia (IPM) merupakan salah satu tolak ukur untuk
melihat aspek-aspek yang relevan dengan pembangunan manusia. Pada penelitian
ini, IPM di Jawa Timur dikategorikan menjadi dua berdasarkan rata-rata. Salah
satu metode yang digunakan untuk analisis data kategorik adalah model regresi
probit. Model regresi probit yang digunakan pada penelitian ini
mempertimbangkan efek spasial yaitu disebut model regresi probit spasial. Untuk
mengetahui kontribusi spasial, maka model regresi probit spasial dibandingkan
dengan model probit. Pemodelan IPM menggunakan model regresi probit
memberikan hasil bahwa faktor yang mempengaruhi IPM di Jawa Timur adalah
persentase penduduk miskin. Sedangkan untuk model regresi probit spasial, faktor
yang berpengaruh adalah tidak hanya persentase penduduk miskin, melainkan
tingkat pengangguran terbuka, dan laju PDRB atas harga konstan juga signifikan.
Ketepatan klasifikasi dari model probit sebesar 39,4%, sedangkan model probit
spasial 44,7%. Berdasarkan persentase ketepatan klasifikasi masing-masing
model, model yang lebih baik dalam mengklasifikasikan IPM dengan benar
adalah model regresi probit spasial
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Human Development Index (HDI) is a benchmark to see aspects that are relevant
to human development. In this study, IPM at East Java are categorized into two
based on the average. One of the methods used for analysis of categorical data is a
probit regression model. Probit regression model used in this study consider the
spatial effect is called spatial probit regression model. To determine the
contribution of spatial, then the spatial probit regression model compared to the
probit model. IPM modeling using probit regression model gives results that the
factors that affect IPM in East Java is the percentage of poor people. As for the
spatial probit regression model, factors that affect not only the percentage of the
population is poor, but the open unemployment rate, and the rate of GDP at
constant prices was also significant. The accuracy of the classification of probit
model by 39.4%, while 44.7% of spatial probit model. Based on the percentage of
classification accuracy of each model, a model that better classify correctly IPM is
a spatial probit regression model.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTSt 619.536 Fah m |
| Uncontrolled Keywords: | Indeks Pembangunan Manusia (IPM), Regresi Probit, Regresi Probit Spasial. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49101-(S2) Master Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 09 Jan 2017 04:09 |
| Last Modified: | 26 Dec 2018 07:23 |
| URI: | http://repository.its.ac.id/id/eprint/1399 |
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