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All Journal UNP Journal of Statistics and Data Science
Rahmadani Iswat
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Multivariate Adaptive Regression Spline Method for Study Timeliness of the 2017 FMIPA UNP Student Rahmadani Iswat; Fadhilah Fitri; Atus Amadi Putra; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1120.783 KB) | DOI: 10.24036/ujsds/vol1-iss2/23

Abstract

The punctuality of study is the time period to complete an education, for undergraduate students is 4 years. One of the quality’s determining of higher education is students’ ability to complete their education on time. The purpose of this study is to see the best modeling results and the accuracy of the punctuality of study of class 2017 FMIPA UNP undergraduate students using MARS. MARS is a method of multivariate nonparametric regression between response variables and predictor variables. The type of research used is applied research. The predictor variables used in this study are Grade Point Average (GPA), gender, university entrance, major, school origin status and place of origin. While the response variable is punctuality of learning time. The results of trial and error showed that the best model was obtained from a combination (BF = 18, MI = 3 and MO = 2), with a minimum GCV value of 0.23182 and R2 value of 0.10045. From the model, it can be seen that the factors that significantly affect punctuality of learning time for FMIPA UNP students class 2017 are the X4 (majors) with an importance level of 100%, the X1 (GPA) with an importance level of 96.61%, X3 (university entrance) and the X5 (school origin status) with an importance level of 16.78 %. The classification accuracy on the 2017 student study timeliness is 64% based on graduating on time and not on time, with a classification error rate of 36%.
Co-Authors Atus Amadi Putra Fadhilah Fitri Zilrahmi, Zilrahmi