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Meimela, Aida
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Humanities Economics, Econometrics & Finance Social Sciences

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Pemetaan Kemiskinan Melalui Pendekatan Geographically Weighted Lasso Bangun, Rita Herawaty Br; Meimela, Aida
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 9 No 3 (2020): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v9i3.58

Abstract

Penelitian ini bertujuan menganalisis kemiskinan menurut variasi wilayah dengan pendekatan spasial melalui penerapan metode Geographically Weighted Lasso (GWL). Studi kasus yang diambil adalah Sumatera Utara, salah satu provinsi dengan tingkat kemiskinan tertinggi di Indonesia. Data penelitian bersifat sekunder yang berasal dari publikasi dan laman BPS. Hasil penelitian menunjukkan metode GWL mampu mengatasi multikolinieritas lokal dan heterogenitas data spasial. Sebesar 85,93 persen kemiskinan di Sumatera Utara dapat dijelaskan oleh seluruh variabel prediktor. Variabel yang signifikan adalah persentase rumah tangga dengan luas lantai kurang dari 8 m2, tingkat setengah pengangguran, dan persentase pekerja informal. Pemodelan kemiskinan dengan metode GWL mampu meningkatkan ketepatan estimasi parameter sehingga program pengentasan kemiskinan di Sumatera Utara akan lebih efektif jika disesuaikan dengan karateristikmasing-masing daerah.
MODEL PENGARUH TINGKAT SETENGAH PENGANGGURAN, PEKERJA INFORMAL DAN PENGELUARAN PERKAPITA DISESUAIKAN TERHADAP KEMISKINAN DI INDONESIA TAHUN 2015-2017 Meimela, Aida
Jurnal Ilmu Ekonomi dan Pembangunan Vol 19, No 1 (2019): Jurnal Ilmu Ekonomi dan Pembangunan
Publisher : EP FEB UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jiep.v19i1.25518

Abstract

Indonesia's poverty rate in the last five years has always decreased annually to 10,64 percent (2017). However, this pattern is not followed by all provinces in Indonesia. Some provinces experience fluctuations every year. Even in 2017, poverty has risen in some provinces. This condition is influenced by some factors of both labor and economy. From the existing literature, there is little research on the effects of underemployment rate, informal workers and adjusted per capita spending on poverty in Indonesia. Therefore, this research is very important to do. This study aims to model the underemployment rate, informal workers and per capita expenditure on poverty using the panel data regression analysis of the 2015-2017 period. The result of the research shows that the best model is Random Effect Model (REM). The rate of underemployment has a positive and significant impact on poverty (level of confident 90 percent). In addition, per capita expenditures have negative and significant impact on poverty. The results of the study show that the government is expected to pay more attention to the indicator of the underemployment rate, because this variable has a largest influence(o,04 percent) on poverty compared to all variables.Keywords : labor, panel regression, underemployment, poverty, random effect model
MODEL HUBUNGAN JUMLAH PENGANGGURAN DAN INDEKS KEDALAMAN KEMISKINAN DI PULAU SUMATERA TAHUN 2019 MENGGUNAKAN REGRESI NONPARAMETRIK SPLINES Meimela, Aida
Jurnal Ilmu Ekonomi dan Pembangunan Vol 20, No 2 (2020): Jurnal Ilmu Ekonomi dan Pembangunan
Publisher : EP FEB UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jiep.v20i2.41701

Abstract

Poverty does not only focus on decreasing the number of poor people. There is an important thing that must also be considered, namely the Poverty Gap Index (P1). From year to year, the poverty gap index for all regencies/cities in Sumatra tends to stagnate. While the island of Sumatra is the second island with the largest population in Indonesia. This should be a serious concern for the government. One of the factors that influence the poverty gap index is unemployment. The more people who are unemployed can increase the poverty gap index. Therefore we need to model the relationship between the number of unemployment and poverty gap index. The approach used is nonparametric regression modeling where the residual value is not normally distributed. The model is smoothing splines regression and quantile splines regression (median, τ = 0, 5). Meanwhile, to see the best model performance by looking at the RMSE values of both models. From the results of the study, it was found that the quantile regression smoothing splines model was better because the RMSE value was lower than the regression smoothing splines.Keywords: poverty gap, unemployment, quantile regressionJEL Classification: I32, J64, C21
Co-Authors Bangun, Rita Herawaty Br