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Handayani, L
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Mathematics

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SIMULASI PENANGANAN PENCILAN PADA ANALISIS REGRESI MENGGUNAKAN METODE LEAST MEDIAN SQUARE (LMS) Tusilowati, Tusilowati; Handayani, L; Rais, Rais
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 15 No. 2 (2018)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.512 KB) | DOI: 10.22487/2540766X.2018.v15.i2.11362

Abstract

The simulation of handling of outliers on regression analysis used the method which was commonly used to predict the parameter in regression analysis, namely Least Median Square (LMS) due to the simple calculation it had. The data with outliers would result in unbiased parameter estimate. Hence, it was necessary to draw up the robust regression to overcome the outliers. The data used were simulation data of the number of data pairs ( X,Y) by 25 and 100 respectively. The result of the simulation was divided into 5 subsets of data cluster of parameter regression prediction by Ordinary Least Square (OLS) and Least Median Square (LMS) methods. The prediction result of the parameter of each method on each subset of data cluster was tested with both method to discover the which better one. Based on the research findings, it was found that The Least Median Square (LMS) method was known better than Ordinary Least Square (OLS) method in predicting the regression parameter on the data which had up to 3% of the percentage of the outlier.
Rice Production Estimation In Central Sulawesi Through The Utilization Of Rainfall By Using Generalized Additive Model Handayani, L; Amelia, R; Putera, F H A
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 2 (2019)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.833 KB) | DOI: 10.22487/2540766X.2019.v16.i2.13997

Abstract

Climate models that are able to simulate rainfall in Indonesia so far have not been found. The highly complex topography and interaction of the sea, land and atmosphere adds to the complexity of simulations and predictions of rainfall in Indonesia, particularly in Central Sulawesi. This research focuses on utilizing the results of prediction or forecast rainfall. Rainfall forecasting results obtained are then modeled with data on the level of rice production, so we can predict the future supply of rice (rice). This study examines statistical downscaling modeling with a generalized additive model approach to describe the rainfall events that occur within a certain time period. The data used is rainfall data in Central Sulawesi Province, because this region is a supplier of rice in Sulawesi.
Co-Authors Amelia, R Putera, F H A Rais Rais Tusilowati, Tusilowati