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All Journal MATHunesa: Jurnal Ilmiah Matematika
Anisa Kalondeng
Program Studi Statistika, FMIPA, Universitas Hasanuddin, Makasar, Indonesia
Mathematics

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PEMODELAN REGRESI NONPARAMETRIK DENGAN ESTIMATOR SPLINE POLYNOMIAL TRUNCATED PADA DATA JUMLAH WISATAWAN NUSANTARA Agym Nastiar Arman; Ryo Lemido; Siswanto Siswanto; Anisa Kalondeng
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 01 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p127-133

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.
OPTIMASI METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PERAMALAN CURAH HUJAN BULANAN DI KOTA DENPASAR Fadia Nailah; Dwi Ina Larasati; Siswanto Siswanto; Anisa Kalondeng
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 01 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p134-140

Abstract

Rainfall is a natural phenomenon that depends on many factors that are an important part of life on earth. The high intensity of rainfall can lead to disasters. Therefore, this study aims to forecast monthly rainfall. The data used was obtained from BMKG Bali Province, namely monthly rainfall data for Denpasar City from 2009 to 2019. The method used is backpropagation artificial neural network. The artificial neural network method is an information processing method inspired by the human nervous system. Optimal backpropagation network architecture is needed so that the prediction results have a low error rate, by optimizing the use of training data and test data taken from sample data. Based on the results of the testing and prediction process with the parameters of one hidden layer with 50 neorons, epoch 11 and learning rate 0.01, the results obtained with the MSE value in network testing are 0.037. So it can be concluded that the backpropagation artificial neural network method has good accuracy results used as a reference for decision making in predicting monthly rainfall in Denpasar City in the future.
Co-Authors Agym Nastiar Arman Dwi Ina Larasati Fadia Nailah Ryo Lemido Siswanto Siswanto