Regresi Nonparametrik Spline Truncated untuk Memodelkan Tingkat Pengangguran Terbuka di Pulau Kalimantan

Abstract

Nonparametric regression is a statistical technique employed to discern the relationship pattern between a predictor variable and a response variable in the absence of prior information about the form of the regression function or when the pattern of the regression curve is unknown. Truncated spline nonparametric regression represents an approach for aligning data, considering the curve's smoothness. It possesses continuous segmented characteristics, offering flexibility and adeptly accommodating the explanation of local data function features. The study aims to identify the influencing factors on the open unemployment rate in Kalimantan Island during 2020. Additionally, it seeks to derive a spline truncated nonparametric regression model for Open Unemployment Rate data in Kalimantan Island for the same year. The study employs a nonparametric regression model with a spline truncated method, determining optimal knot points based on the minimum Generalized Cross Validation (GCV) value. The study reveals that the most effective spline truncated nonparametric regression model features two knot points. Significant factors influencing the open unemployment rate include the labor force participation rate, school year expectations, regional gross domestic product at current prices, and the human development index.