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Dive into the research topics where Aji Hamim Wigena is active.

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Featured researches published by Aji Hamim Wigena.


IOSR Journal of Mathematics | 2014

Statistical Downscaling with Generalized Additive Model For Extreme Rainfall Estimation

Lilies Handayani; Aji Hamim Wigena; Anik Djuraidah

The analysis of the phenomenon of extreme values of climate, especially rainfall is very important for government to reduce the negative impacts. Global circulation model (GCM) is an important data in the climate system because it can provide information about the climate in the future on a large scale. Techniques to reduce the size of the spatial scale using statistical downscaling (SD). SD modeling method requires a more flexible alternative to the assumption that the resulting models can be used to describe the climate events. Generalized additive model (GAM) is a method that accommodates the influence of linear and nonlinear in extreme rainfall events. The methodology is applied to forecast montly extreme rainfall in Indramayu District.


Applied mathematical sciences | 2018

Mixture designs for quadratic models with constrained experimental regions on ground granulated blast furnace slag concrete

Faula Arina; Aji Hamim Wigena; I Made Sumertajaya; Utami Syafitri

The nature of the ground granulated blast furnace slag (GGBFS) experiment followed a mixture design in which the response can be described by a quadratic model. The five mixture components were water, cement, coarse aggregate, fine aggregate, and percentage GGBFS replaced the cement). Due to the constraints of the components, the experimental region was not a simplex. The constraints of the mixture component affect the experimental region. For the irregular shape, the design points are difficult to identify by hand. The XVERT algorithm can be used for selecting a subset of extreme vertices and centroids when the number of candidates is large. The Fedorov algorithm used for selecting a design of available 1252 Faula Arina et al. vertices and centroids. Through the optimal design of the GGBFS experiment, experimental runs can be reduced more efficiently.


PROCEEDINGS OF THE 7TH SEAMS UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2015: Enhancing the Role of Mathematics in Interdisciplinary Research | 2016

Vector generalized additive models for extreme rainfall data analysis (study case rainfall data in Indramayu)

Eka Putri Nur Utami; Aji Hamim Wigena; Anik Djuraidah

Rainfall pattern are good indicators for potential disasters. Global Circulation Model (GCM) contains global scale information that can be used to predict the rainfall data. Statistical downscaling (SD) utilizes the global scale information to make inferences in the local scale. Essentially, SD can be used to predict local scale variables based on global scale variables. SD requires a method to accommodate non linear effects and extreme values. Extreme value Theory (EVT) can be used to analyze the extreme value. One of methods to identify the extreme events is peak over threshold that follows Generalized Pareto Distribution (GPD). The vector generalized additive model (VGAM) is an extension of the generalized additive model. It is able to accommodate linear or nonlinear effects by involving more than one additive predictors. The advantage of VGAM is to handle multi response models. The key idea of VGAM are iteratively reweighted least square for maximum likelihood estimation, penalized smoothing, fisher s...


PROCEEDINGS OF THE 7TH SEAMS UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2015: Enhancing the Role of Mathematics in Interdisciplinary Research | 2016

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

Dewi Santri; Aji Hamim Wigena; Anik Djuraidah

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be us...


PROCEEDINGS OF THE 7TH SEAMS UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2015: Enhancing the Role of Mathematics in Interdisciplinary Research | 2016

Statistical downscaling with generalized Pareto distribution (Study case: Extreme rainfall estimation)

Shynde Limar Kinanti; Aji Hamim Wigena; Anik Djuraidah

Indonesia has tropical climate with small variation of temperature but quite large variation of rainfall. So the rainfall which is an essential climate element related to climate change has to be observed. Climate change may increase the incidence of extreme rainfall that affects flooding in farmland. In order to anticipate the occurrence of extreme rainfall, the information of rainfall forecast is required. Statistical Downscaling (SD) is a technique to model the relationship between global scale data and local scale data. Global Circulation Model (GCM) output is global scale data and rainfall is local scale data. GCM has characteristic non-linear, high dimension, and multicolinierity. These problem can be overcome by principal component analysis (PCA). One of the primary methods for estimating extreme rainfall is generalize Pareto distribution (GPD) regression based on a threshold. The objective of this study is SD modeling based on GPD to predict extreme rainfall. The result show that GPD models can pr...


2016 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA) | 2016

Gamma distribution linear modeling with statistical downscaling to predict extreme monthly rainfall in Indramayu

Agus Mohamad Soleh; Aji Hamim Wigena; Anik Djuraidah; Asep Saefuddin

Rainfall is an important factor in the agricultural process. Several methods to predict the rainfall have been carried out in Indonesia, such as the modeling of Statistical Downscaling (SDS). SDS models might involve ill-conditioned covariates (large dimension and high correlation/multi collinear). This problem could be solved by a variable selection technique such as L1 regularization/LASSO or a dimension reduction approach such as principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared in order to predict extreme monthly rainfall at 11 rain posts in Indramayu. Simulations were conducted to compare L1 regularization technique and principal component analysis in the prediction of responses. Two scenarios were based on the coefficient of beta and the distribution of response scenarios. The covariates used in this study were in observational data of GPCP version 2.2. The coefficient of beta scenarios were the combination of beta less than 1, equal 0, and greater than 1 vs all betas less than 1. Gamma distributions were used for distribution of response scenario with three different shape parameters. The simulation showed that L1 regularization technique resulted in almost better prediction than principal component analysis as the shape parameter was larger. The Root Mean Square Error (RMSE) of generalized linear model with Gamma distribution was less than that of principal component regression. However, all generalized linear models with Gamma distribution gave the smaller RMSE values for extreme value prediction above outliers. In this case, the quantiles, Q(0.90) and Q(0.95), were better prediction of extreme monthly rainfall.


THE 5TH INTERNATIONAL CONFERENCE ON RESEARCH AND EDUCATION IN MATHEMATICS: ICREM5 | 2012

Nested generalized linear mixed model with ordinal response: Simulation and application on poverty data in Java Island

Yekti Widyaningsih; Asep Saefuddin; Khairil Anwar Notodiputro; Aji Hamim Wigena

The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordin...


Applied mathematical sciences | 2015

Modeling extreme rainfall with Gamma-Pareto distribution

Herlina Hanum; Aji Hamim Wigena; Anik Djuraidah; I Wayan Mangku


Applied mathematical sciences | 2015

Statistical Downscaling to Predict Monthly Rainfall Using Linear Regression with L 1 Regularization (LASSO)

Agus Mohamad Soleh; Aji Hamim Wigena; Anik Djuraidah; Asep Saefuddin


Science Journal of Applied Mathematics and Statistics | 2014

Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall

Aji Hamim Wigena; Anik Djuraidah

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Anik Djuraidah

Bogor Agricultural University

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Asep Saefuddin

Bogor Agricultural University

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I Wayan Mangku

Bogor Agricultural University

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Yonny Koesmaryono

Bogor Agricultural University

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Edvin Aldrian

Bogor Agricultural University

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Hidayat Pawitan

Bogor Agricultural University

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Agus Mohamad Soleh

Bogor Agricultural University

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Hari Wijayanto

Bogor Agricultural University

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