Pemodelan Persentase Migrasi Risen Masuk Menggunakan Metode Regresi Nonparametrik Spline Truncated (Studi Kasus : Pulau Kalimantan Tahun 2022)

Zahro, Fauziah Mufiddatuz (2024) Pemodelan Persentase Migrasi Risen Masuk Menggunakan Metode Regresi Nonparametrik Spline Truncated (Studi Kasus : Pulau Kalimantan Tahun 2022). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pertumbuhan penduduk di Indonesia menjadi titik awal dalam memahami dinamika migrasi. Migrasi merupakan salah satu faktor yang dapat memengaruhi jumlah penduduk di suatu wilayah. Migrasi didasari oleh tiga faktor yaitu faktor demografi-ketenagakerjaan, faktor sosial kesejahteraan, serta faktor pendidikan. Pulau Kalimantan merupakan salah satu pulau di Indonesia dengan persentase migrasi risen masuk berstatus tinggi pada tahun 2020 yang memiliki 56 kabupaten/kota. Namun, ketiga faktor yang memengaruhi migrasi risen masuk di Kalimantan masih membutuhkan peningkatan, sehingga perlu dilakukan penelitian untuk mengetahui faktor-faktor yang berpengaruh terhadap migrasi risen masuk di Kalimantan. Pada penelitian ini, dilakukan pemodelan migrasi risen masuk di Pulau Kalimantan dengan empat variabel yang diduga berpengaruh menggunakan analisis Regresi Nonparametrik Spline truncated. Hal ini dikarenakan pola hubungan antara migrasi risen masuk dengan variabel yang diduga berpengaruh tidak mengikuti pola tertentu dan berubah-ubah pada sub-sub interval tertentu. Variabel yang digunakan yaitu Tingkat Pengangguran Terbuka (TPT), persentase penduduk miskin, laju pertumbuhan penduduk, dan Rata-rata Lama Sekolah (RLS). Berdasarkan hasil penelitian menggunakan metode regresi nonaparametrik spline truncated diperoleh model terbaik adalah dengan tiga titik knot dengan nilai GCV 2,026887296 yang telah memenuhi asumsi IIDN. Semua variabel prediktor berpengaruh signifikan terhadap migrasi risen masuk di Pulau Kalimantan tahun 2022. Nilai R^2 yang diperoleh sebesar 81,25% dan sisanya sebesar 18,75% dijelaskan oleh variabel lain yang tidak diteliti.
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Population growth in Indonesia is the starting point in understanding the dynamics of migration. Migration is one of the factors that can affect the population in a region. Migration is based on three factors, namely demographic-labor factors, social welfare factors, and educational factors. Kalimantan Island is one of the islands in Indonesia with a high percentage of in-migration in 2020, which has 56 districts / cities. However, the three factors that influence in-migration in Kalimantan still need improvement, so it is necessary to conduct research to determine the factors that affect in-migration in Kalimantan. In this study, the modeling of in-migration in Kalimantan Island is carried out with four variables that are thought to have an effect using Spline truncated Nonparametric Regression analysis. This is because the pattern of the relationship between in-migration and variables that are thought to be influential does not follow a certain pattern and changes at certain sub-intervals. The variables used are Open Unemployment Rate (OUR), percentage of poor people, population growth rate, and Mean Years of Schooling (MYS). Based on the results of research using the truncated spline non-parametric regression method, the best model is obtained with three knot points with a GCV value of 2.026887296 which has met the IIDN assumption. All predictor variables have a significant effect on in-migration in Kalimantan Island in 2022. The R^2 value obtained is 81.25% and the remaining 18.75% is explained by other variables not studied.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Generalized Cross Validation, Migrasi risen masuk, Pulau Kalimantan, Regresi nonparametrik spline truncated, Titik knot, Kalimantan Island, Knot point, Nonparametric Spline truncated Regression, Recent in-migration.
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HA Statistics > HA31.3 Regression. Correlation
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Fauziah Mufiddatuz Zahro
Date Deposited:	10 Aug 2024 15:52
Last Modified:	26 Aug 2024 03:15
URI:	http://repository.its.ac.id/id/eprint/114704

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