PEMODELAN REGRESI NONPARAMETRIK DENGAN ESTIMATOR SPLINE POLYNOMIAL TRUNCATED PADA DATA JUMLAH WISATAWAN NUSANTARA

Agym Nastiar Arman, Ryo Lemido, S. Siswanto, Anisa Kalondeng
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Abstract

The nonparametric regression approach is a statistical method used to determine the relationship between predictor variables and the dependent variable when the assumed pattern is unknown. Truncated spline is an estimator used in nonparametric regression to handle data with varying behaviors. Nonparametric regression modeling with truncated polynomial spline was applied to local Indonesian tourist visitation data obtained from BPS for the years 2017-2019, for each month. The optimal knot points were selected based on the smallest Gross Cross Validation values. Based on the analysis, the optimal model is a second-order spline with the smallest Gross Cross Validation value of 17,95 and the optimal knot points are in the 2nd, 6th, and 7th months. The goodness of the model is evident from an  value of 81,88% and an MSE of 12,46. The best model obtained shows a fairly accurate ability to explain the estimated number of domestic tourists so that it can be a basis for stakeholders to make key decisions in planning and managing the tourism industry as an effort to increase domestic tourism interest.
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非参数回归模型与截断多项式样条线估计法(spline polynomial truncated pemodelan regresi nonparametric dengan estimator spline polynomial truncated pada data jumlah wisatawan nusantara
非参数回归法是一种统计方法,用于在假设模式未知的情况下确定预测变量与因变量之间的关系。截断样条曲线是非参数回归中的一种估计器,用于处理行为各异的数据。使用截断多项式样条线的非参数回归模型被应用于从 BPS 获得的 2017-2019 年印尼本地游客访问量数据中的每个月。根据最小的交叉验证总值选择最佳结点。根据分析,最佳模型为二阶样条曲线,其最小交叉验证值为 17.95,最佳结点位于第 2、6 和 7 个月。从 81.88% 的值和 12.46% 的 MSE 可以看出模型的良好性。所获得的最佳模型表明,该模型能够相当准确地解释国内游客的估计数量,因此可以作为利益相关者在规划和管理旅游业时做出关键决策的依据,以努力提高国内游客的兴趣。
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