PENERAPAN REGRESI LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) UNTUK MENGIDENTIFIKASI VARIABEL YANG BERPENGARUH TERHADAP KEJADIAN STUNTING DI INDONESIA

TESA TRILONIKA PARDEDE, . (2022) PENERAPAN REGRESI LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) UNTUK MENGIDENTIFIKASI VARIABEL YANG BERPENGARUH TERHADAP KEJADIAN STUNTING DI INDONESIA. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Analisis regresi linier merupakan metode analisis yang dapat digunakan untuk menganalisis data dan menarik kesimpulan yang berarti tentang ketergantungan suatu variabel terhadap variabel yang lain. Dalam analisis regresi linier terdapat beberapa asumsi yang harus dipenuhi yaitu distribusi normal, tidak ada korelasi antara galat. Ada beberapa kendala yang menyebabkan asumsi tidak terpenuhi, misalkan terjadinya korelasi antar variabel bebas (multikolinearitas). Analisis pada penelitian ini menggunakan metode regresi Least Absolute Shrinkage And Selection Operator (LASSO) dengan algoritma Least Angle Regression (LAR) karena pada data stunting di Indonesia terdapat adanya masalah multikolinearitas di antara variabel bebas yang digunakan. LASSO yang dapat menyelesaikan kasus multikolinearitas pada regresi sekaligus memungkinkan untuk menyusut koefisien regresi dari variabel bebas yang berkorelasi tinggi sampai tepat nol. Koefisien LASSO didapatkan menggunakan pemrograman kuadratik sehingga digunakan algoritma LAR yang lebih efisien dalam komputasi LASSO. Berdasarkan analisis yang telah dilakukan disimpulkan bahwa variabel asi eksklusif (X1), konsumsi protein (X2), imunisasi DPT-HB (X5), tinggi badan ibu (X8) dan diare (X9) berpengaruh terhadap stunting di Indonesia tahun 2018. Linear regression analysis is an analytical method that can be used to analyze data and draw meaningful conclusions about the dependence of one variable on another variable. In linear regression analysis there are several assumptions that must be met, namely normal distribution, there is no correlation between errors. There are several obstacles that cause the assumption to be unfulfilled, for example the occurrence of correlations between independent variables (multicollinearity). The analysis in this study uses the Least Absolute Shrinkage And Selection Operator (LASSO) regression method with the Least Angle Regression (LAR) algorithm because the stunting data in Indonesia has multicollinearity problems among the independent variables used. LASSO which can solve the case of multicollinearity in the regression at the same time it is possible to reduce the regression coefficient from the highly correlated independent variable to exactly zero. The LASSO coefficient obtained uses quadratic so that the LAR algorithm is used which is more efficient in LASSO computing. Based on the analysis that has been carried out, it is concluded that the variables of exclusive breastfeeding (X1), protein consumption (X2), DPT-HB exercise (X5), maternal height (X8) and diarrhea (X9) had an effect on stunting in Indonesia in 2018.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Dr. Ir. Bagus Sumargo, M.Si. 2). Dra. Widyanti Rahayu, M.Si.
Subjects:	Sains > Matematika > Software, Sistem Informasi Komputer
Divisions:	FMIPA > S1 Statistika
Depositing User:	Users 14035 not found.
Date Deposited:	10 Mar 2022 02:58
Last Modified:	10 Mar 2022 02:58
URI:	http://repository.unj.ac.id/id/eprint/23661

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