Muhammad Robbani, - (2018) REGRESI LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) PADA KASUS INFLASI DI INDONESIA TAHUN 2014-2017. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Multikolinearitas merupakan salah satu pelanggaran asumsi klasik pada analisis regresi linear berganda yang disebabkan adanya hubungan linear diantara sebagian atau seluruh variabel independen dalam sebuah model regresi. Salah satu metode yang dapat menyelesaikan model regresi linear berganda yang terdapat multikolinearitas yaitu regresi Least Absolute Shrinkage and Selection Operator (LASSO). Regresi LASSO mampu mengatasi masalah multikolinearitas dengan menyusutkan koefisien taksirannya mendekati nol bahkan dapat hingga tepat nol, sehingga regresi LASSO dapat menyeleksi variabel di dalam model regresi. Regresi LASSO tidak memiliki solusi secara eksplisit dalam menentukan koefisien taksirannya sehingga dibutuhkan pemrograman komputasi untuk menyelesaikannya. Algoritma LARS merupakan algoritma yang sangat efektif dalam membantu menyelesaikan solusi regresi LASSO secara komputasi. Dalam penelitian ini, diambil studi kasus mengenai inflasi yang terjadi di Indonesia tahun 2014-2017 sehingga inflasi merupakan variabel dependen dalam penelitian ini. Sedangkan variabel-variabel independen yang terdapat dalam penelitian ini yaitu Produk Domestik Bruto, Ekspor Bersih, Jumlah Uang Beredar, Nilai Tukar Rupiah, Suku Bunga, Harga Beras, Upah Buruh Tani, dan Harga Minyak Dunia. Variabel-variabel independen tersebut termasuk pembahasan ekonomi makro yang besar kemungkinan saling mempengaruhi satu sama lain sehingga besar kemungkinan pula terjadinya multikolinearitas pada model regresi. Tujuan dari penelitian ini adalah menentukan variabel-variabel yang berpengaruh terhadap inflasi di Indonesia tahun 2014-2017 menggunakan regresi LASSO dan dibantu algoritma LARS dalam hal komputasi. Hasil dari penelitian ini diperoleh bahwa variabel-variabel yang berpengaruh terhadap inflasi yaitu Produk Domestik Bruto, Ekspor Bersih, Nilai Tukar Rupiah, Suku Bunga, Harga Beras, dan Upah Buruh Tani.;--Multicollinearity is one of the violations of classical assumptions in multiple linear regression analysis due to a linear relationship between some or all of the independent variables in a regression model. One method that can solve the multiple linear regression model that contains multicollinearity is the regression of Least Absolute Shrinkage and Selection Operator (LASSO). LASSO regression is able to solve the problem of multicollinearity by shrinking the estimated coefficients close to zero and even up to exactly zero, so that LASSO regression can select variables in the regression model. LASSO regression does not have an explicit solution of the estimated coefficient so that computational programming is needed to solve it. The LARS algorithm is a very effective algorithm in helping to solve the solutions of the LASSO regression. In this study, a case study was carried out on inflation that occurred in Indonesia in 2014-2017 so that inflation was the dependent variable in the model. In other side, the independent variables contained in this study are Gross Domestic Product, Net Export, Money Supply, Rupiah Exchange Rate, Interest Rate, Rice Price, Farmer Labor Wages, and World Oil Prices. These independent variables, including macroeconomic discussions, are likely to influence each other so that there is probably contain multicollinearity in the regression model. The purpose of this study is to determine the variables that influence inflation in Indonesia in 2014-2017 using LASSO regression and assisted by LARS algorithm. The results of this study found that the variables that influence inflation are Gross Domestic Product, Net Export, Rupiah Exchange Rate, Interest Rates, Rice Prices, and Farmer Labor Wages.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | No. Panggil : S MAT MUH r-2019; Pembimbing : I. Fitriani Agustina, II. Nar Herrhyanto; NIM : 1403439. |
| Uncontrolled Keywords: | Multikolinearitas, Regresi LASSO, LARS, inflasi, Produk Domestik Bruto, Ekspor Bersih, Jumlah Uang Beredar, Nilai Tukar Rupiah, Suku Bunga, Harga Beras, Upah Buruh Tani, Harga Minyak Dunia, Multicollinearity, LASSO Regression, LARS, inflation, Gross Domestic Product, Net Exports, Money Supply, Rupiah, Exchange Rate, Interest Rates, Rice Prices, Farmer Labor Wages, World Oil Prices. |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Matematika > Program Studi Matematika (non kependidikan) |
| Depositing User: | Cintami Purnama Rimba |
| Date Deposited: | 02 Sep 2019 07:09 |
| Last Modified: | 02 Sep 2019 07:09 |
| URI: | http://repository.upi.edu/id/eprint/39577 |
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