Prameswari, Nadia Elvina (2026) Analisis Faktor-Faktor yang Memengaruhi Persentase Penduduk Miskin di Nusa Tenggara Timur dengan Regresi Nonparametrik Spline Truncated. Diploma thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kemiskinan merupakan permasalahan multidimensional yang masih menjadi tantangan utama di Provinsi Nusa Tenggara Timur (NTT). Tingginya tingkat kemiskinan diduga dipengaruhi oleh faktor pendidikan, kesehatan, dan ekonomi. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi persentase penduduk miskin di Provinsi Nusa Tenggara Timur menggunakan regresi nonparametrik spline truncated. Metode ini dipilih karena pola hubungan antara variabel respon dan variabel prediktor tidak mengikuti bentuk fungsi tertentu serta menunjukkan perubahan pola pada beberapa sub-interval data. Data yang digunakan merupakan data sekunder tahun 2024 yang diperoleh dari Badan Pusat Statistik Provinsi Nusa Tenggara Timur dengan unit penelitian sebanyak 22 kabupaten/kota. Variabel yang digunakan meliputi Rata-rata Lama Sekolah, Angka Harapan Hidup, PDRB per Kapita ADHK, dan Gini Ratio. Pemilihan titik knot optimal dilakukan menggunakan metode Generalized Cross Validation (GCV). Hasil penelitian menunjukkan bahwa model terbaik diperoleh pada kombinasi knot (1,1,1,1) dengan nilai GCV minimum sebesar 17,55387277170. Model yang dihasilkan memiliki koefisien determinasi sebesar 85,43%, yang menunjukkan bahwa model mampu menjelaskan variasi persentase penduduk miskin dengan sangat baik. Hasil pengujian parameter menunjukkan bahwa Rata-rata Lama Sekolah, PDRB per Kapita ADHK, dan Gini Ratio berpengaruh signifikan terhadap persentase penduduk miskin di Provinsi Nusa Tenggara Timur tahun 2024, sedangkan variabel Angka Harapan Hidup tidak berpengaruh signifikan. Selain itu, model yang diperoleh telah memenuhi asumsi residual identik, independen, dan berdistribusi normal (IIDN), sehingga layak digunakan untuk memodelkan persentase penduduk miskin di Nusa Tenggara Timur.
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Poverty is a multidimensional issue that remains a major challenge in East Nusa Tenggara Province. The high poverty rate is presumed to be influenced by educational, health, and economic factors. This study aims to analyze the factors affecting the percentage of the poor population in East Nusa Tenggara Province using truncated spline nonparametric regression. This method was selected because the relationship between the response variable and the predictor variables does not follow a specific functional form and exhibits different patterns across several data sub-intervals. The data used were secondary data for 2024 obtained from Statistics Indonesia of East Nusa Tenggara Province, with 22 regencies/cities as the units of analysis. The variables included mean years of schooling, life expectancy, gross regional domestic product (GRDP) per capita at constant prices, and the Gini ratio. The optimal knot points were determined using the Generalized Cross Validation (GCV) method. The results showed that the best model was obtained using the knot combination of (1,1,1,1), with a minimum GCV value of 17,55387277170. The resulting model produced a coefficient of determination of 85,43%, indicating that it explained the variation in the percentage of the poor population very well. The parameter significance test revealed that mean years of schooling, GRDP per capita at constant prices, and the Gini ratio had a significant effect on the percentage of the poor population in East Nusa Tenggara Province in 2024, whereas life expectancy had no significant effect. Furthermore, the resulting model satisfied the assumptions of identically distributed, independent, and normally distributed residuals (IIDN), indicating that it is appropriate for modeling the percentage of the poor population in East Nusa Tenggara Province.
| Item Type: | Thesis (Diploma) |
|---|---|
| Uncontrolled Keywords: | Kemiskinan, Regresi Nonparametrik, Spline Truncated, Nusa Tenggara Timur, Poverty, Nonparametric Regression, Spline Truncated, East Nusa Tenggara |
| Subjects: | H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Nadia Elvina Prameswari |
| Date Deposited: | 29 Jul 2026 03:04 |
| Last Modified: | 29 Jul 2026 03:04 |
| URI: | http://repository.its.ac.id/id/eprint/139006 |
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