Analisis Regresi Nonparametrik Spline Truncated untuk Menganalisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Sulawesi Selatan

Devi Carolin Wongkar; Ruliana Ruliana; Muhammad  Fahmuddin S

doi:10.35580/variansiunm101

Devi Carolin Wongkar Universitas Negeri Makassar
Ruliana Ruliana Department of Statistics, Universitas Negeri Makassar
Muhammad Fahmuddin S Department of Statistics, Universitas Negeri Makassar

DOI: https://doi.org/10.35580/variansiunm101

Keywords: Regresi Nonparametrik Spline Truncated, GCV, Tingkat Pengangguran Terbuka

Abstract

The nonparametric regression analysis is a regression model used to determine the relationship between response variable and independent variables with unknown regression curve shapes. In the nonparametric approach, one of the frequently used estimators is the spline truncated. Spline truncated model is a segmented polynomial truncation model. The advantage of this model is that it is flexible because it has knot points that can show changes in data patterns. The unemployment rate in South Sulawesi Province in 2021 reached 5.72% and became the province with the second highest unemployment rate on Sulawesi Island. Therefore, spline truncated nonparametric regression modelling will be carried out in the case of unemployment rate with each of the factors that are thought to be influential because the regression curve is found not to form a certain pattern. Based on the analysis results, the best truncated spline nonparametric regression model was obtained using three knot points and obtained the minimum GCV value of 0.38 with a coefficient of determination (R²) value of 89%. Factors that have a significant effect on the unemployment rate in South Sulawesi are mean years of schooling (x₁) and labour force participation rate (x₂).

References

Adetya Arjun, D., Sifriani, & Syaripuddin. (2019). Analisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Kalimantan Menggunakan Regresi Nonparametrik Spline Truncated. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya 2019, 1, 115–121.

Badan Pusat Statistik. (2021). Keadaan Ketenagakerjaan Provinsi Sulawesi Selatan Agustus 2021.

Chalifari, Anwar, K., & Muhammad Abdy Yusuf, dan. (2020). Pengaruh Angka Harapan Hidup (AHH) dan Konsumsi Per Kapita Terhadap Pengangguran. Jurnal Ekonomi Dan Pembangunan, 11.

Dani, A. T. R., & Adrianingsih, N. Y. (2021). Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier. Jambura Journal of Mathematics, 3(1), 26–36.

Dani, A. T. R., Ni’matuzzahroh, L., Ratnasari, V., & Budiantara, I. N. (2021). Pemodelan Regresi Nonparametrik Spline Truncated pada Data Longitudinal. Inferensi, 4(1), 47.

Daniel, W. W. (1989). Statistika Nonparametrik Terapan. Gramedia.

Draper, N. R., & Smith, H. (1992). Analisis Regresi Terapan (Second Edition). PT. Gramedia Pustaka Utama.

Draper, N. R., & Smith, H. (2014). Applied Regression Analysis (Third Edition). John Wiley & Sons.

Gatiningsih, & Sutrisno, E. (2017). Kependudukan dan Ketenagakerjaan (Vol. 1). Fakultas Manajemen Pemerintahan IPDN.

Izzah, N., & Budiantara, I. N. (2020). Pemodelan Faktor-faktor yang Mempengaruhi Tingkat Partisipasi Angkatan Kerja Perempuan di Jawa Barat Menggunakan Regresi Nonparametrik Spline Truncated. Inferensi, 3, 21–27.

Kurniawati, N. A., & Budiantara, I. N. (2019). Pemodelan Tingkat Pengangguran Terbuka di Provinsi Jawa Barat Menggunakan Regresi Nonparametrik Spline Truncated. Jurnal Sains Dan Seni ITS, 8, 334–340.

Prahutama, A., Suparti, & Utami, T. W. (2018). Modelling Fourier Regression for Time Series Data- A Case Study: Modelling Inflation in Foods Sector in Indonesia. Journal of Physics: Conference Series, 1–9.

Pratiwi, L. P. S. (2020). Pemilihan Titik Knot Optimal Model Spline Truncated dalam Regresi Nonparametrik Multivariabel dengan GCV. Jurnal Matematika, 10, 78–90.

Rifai, N. A. K. (2019). Pendekatan Regresi Nonparametrik dengan Fungsi Kernel untuk Indeks Harga Saham Gabungan. Jurnal Statistika, 1, 23–61.

Salsabila, N. A., Andriani, S., Mirisda, & Nohe, D. A. (2022). Analisis Pengaruh Tingkat Partisipasi Angkatan Kerja dan Indeks Pembangunan Manusia Terhadap Tingkat Pengangguran Terbuka Menggunakan Regresi Probit dan Logit. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya, 2, 344–353.

Siskawati, N., Surya, R. Z., & Sudeska, E. (2021). Pengaruh Harapan Lama Sekolah, Rata-Rata Lama Sekolah terhadap Tingkat Pengangguran Terbuka di Kabupaten/Kota Provinsi Riau. Jurnal Selodang Mayang, 7, 173–177.

Syam, R., Sanusi, W., & Adawiyah, D. R. (2019). Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar). Journal of Mathematics, Computations and Statistics, 2, 70–81.

Tiro, M. A. (2008). Analisis Regresi dengan Data Kategori (Edisi Ketiga). Andira Publisher.

Tiro, M. A. (2010). Analisis Korelasi dan Regresi (Edisi Ketiga). Andira Publisher.

Tripena, A., & Prabowo, A. (2021). Estimated Spline in Nonparametric Regression with a Generalized Cross Validation and Unbiased Risk Approach. Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, 3788–3798.

Wahba, G. (1990). Spline Models for Observational Data. SIAM.