Classification of underdeveloped regions in Maluku using binary MARS

Autor:	Muh. Yahya Matdoan, Marlon Stivo Noya Van Delsen
Rok vydání:	2021
Předmět:	Multivariate statistics Spline (mathematics) Binary Independence Model Statistics Binary number Value (computer science) Statistical analysis Mars Exploration Program Regression Mathematics
Zdroj:	INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT (ICEE 2021).
ISSN:	0094-243X 2015-2019
DOI:	10.1063/5.0060036
Popis:	Based on Presidential Regulation Number 131 of 2015 on Determine of Underdeveloped Regions in 2015-2019, Maluku ranks 4th out of 23 provinces. Maluku has 11 regions, 8 of them are classified as underdeveloped regions. Classification of underdeveloped regions can be done using statistical analysis, namely the Binary Multivariate Adaptive Regression Spline (MARS). So, specific objectives to be achieved in this study are to determine the best Binary MARS model for classification and to calculate the accuracy of the Binary MARS model for the classification of underdeveloped regions in Maluku. After obtaining the classification results, we find out of GCV value for the MARS Binary model was 0.155 and the R2 value is 0.897. This model provided 100% accuracy in classifying the underdeveloped regions in Maluku.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::989954c0de68f0b8ad7cac6a129d828e https://doi.org/10.1063/5.0060036 Zobrazit plný text záznamu