Nuraini, Fasha Amellia (2017) Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia (IPM) Di Indonesia Menggunakan Metode Regresi Probit Biner. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Indonesia merupakan Negara dengan jumlah penduduk terbesar ke empat di dunia, sehingga bidang kependudukan di Indonesia harus sangat diperhatikan . Salah satu indikator penting untuk mengukur keberhasilan dalam upaya membangun kualitas hidup penduduk di Indonesia adalah Indeks Pembangunan Manusia (IPM). Pada penelitian ini Indeks Pembangunan Manusia dikategorikan menjadi dua kategori, yaitu dikategorikan sedang jika IPM < 70, dan dikategorikan tinggi jika 70 < IPM ≤ 80. Berdasarkan uraian tersebut diteliti mengenai faktor-faktor yang mempengaruhi nilai IPM dari 34 Provinsi di Indonesia menggunakan regresi probit biner. Di dapatkan hasil bahwa variabel yang mempengaruhi adalah persentase penduduk yang tinggal di perkotaan, persentase penduduk yang berpendidikan di atas SLTP, persentase penduduk miskin, persentase tingkat pengangguran terbuka dan persentase penduduk mengalami ganguan kesehatan. Penggunaan model regresi probit menghasilkan model klasifikasi IPM sebesar 94,12 persen dengan tingkat kesalahan klasifikasi sebesar 5,88 persen
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Indonesia is the country with the fourth largest population in the world, so the field of population in Indonesia should be very con-cerned. One important indicator to measure success in building the quality of life of the population in Indonesia is the Human Develop-ment Index (HDI). In this research Human Development Index is catego-rized into two categories, that is categorized middle if HDI <70, and categorized high if 70 <HDI ≤ 80. Based on the description examined about the factors that influence the value of HDI from 34 provinces in Indonesia using regression Probit binary. The results show that the variables that influence are the percentage of population living in ur-ban areas, the percentage of the population who are educated above junior high, the percentage of the poor, the percentage of open unem-ployment rate and the percentage of the population suffer from health problems. The use of probit regression model resulted in HDI classifica-tion model of 94.12 percent with a classification error rate of 5.88 per-cent.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.536 Nur f |
| Uncontrolled Keywords: | Regresi Probit Biner; Indeks Pembangunan Manusia (IPM); Ketepatan Klasifikasi; Binary Probit Regression; Human Development Index (HDI); Classification Accuracy |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Nuraini Fasha Amellia |
| Date Deposited: | 17 Jan 2018 04:57 |
| Last Modified: | 05 Mar 2019 04:04 |
| URI: | http://repository.its.ac.id/id/eprint/48509 |
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