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<![CDATA[A-Optimal Pada Mixture Amount Design Dengan Modifikasi Rancangan Petak Terbagi Menggunakan Algoritma Point-Exchange ]]>
<![CDATA[Sari, Mutia Dwi Permata]]>;
<![CDATA[Syafitri, Utami Dyah]]>;
<![CDATA[Djuraidah, Anik]]>
<![CDATA[ Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 2 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam ]]>
Publisher : <![CDATA[Prodi Pendidikan Matematika FTIK IAIN Palopo ]]>
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DOI: 10.24256/jpmipa.v12i2.4072
<![CDATA[Abstract:A Mixture Amount Experiment (MAE) is a design that consists of a mixture variable and the total amount variable. In practice, the composition of the mixture is run on each total amount of mixture, which consequently cannot be completely randomized, so that a split-plot design approach is needed. This research aims to develop an algorithm to find a A-Optimal design for a mixture amount experiment with a modified split-plot design. A-Optimal design is seeking a design in which minimizing the covariance of the model parameter. The study case of this research involved three ingredients and two total amounts of mixtures with different constraints. In this research, the whole plot factor is the total amount of mixtures, while the subplot factor is the composition of the mixture. The A-Optimal design was generated based on the Second-Order Scheefe model. To Construct the A-optimal design, we used the point exchange algorithm. The result from this algorithm produced an optimum composition in each total amount of mixture. Abstrak:Rancangan Jumlah Campuran (MAE) terdiri dari komponen campuran dan jumlah total. Dalam prakteknya, komposisi campuran dijalankan pada setiap jumlah total campuran, akibatnya tidak dapat diacak sempurna, sehingga diperlukan pendekatan rancangan petak terbagi. Penelitian ini bertujuan untuk mengembangkan suatu algoritma untuk menemukan rancangan dengan kriteria A-Optimal untuk percobaan jumlah campuran dengan menggunakan modifikasi rancangan petak terbagi. Rancangan A-Optimal mencari rancangan yang meminimalkan kovarian parameter model. Studi kasus penelitian ini terdiri dari tiga bahan dan dua jumlah total campuran yang berbeda. Dalam penelitian ini, factor petak utama adalah jumlah total campuran, sedangkan faktor anak petak adalah komposisi campuran. rancangan A-Optimal dihasilkan berdasarkan model Second-Order Scheefe. Untuk Membangun rancangan A-optimal, menggunakan pendekatan algoritma point-exchange. Hasil dari algoritma ini menghasilkan komposisi optimum pada setiap jumlah total campuran.]]>
<![CDATA[STACKING ENSEMBLE APPROACH IN STATISTICAL DOWNSCALING USING CMIP6-DCPP FOR RAINFALL ESTIMATION IN RIAU ]]>
<![CDATA[Mahkya, Dani Al]]>;
<![CDATA[Djuraidah, Anik]]>;
<![CDATA[Wigena, Aji Hamim]]>;
<![CDATA[Sartono, Bagus]]>
<![CDATA[ MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.17.1.1-12
<![CDATA[Rainfall modeling and prediction is one of the important things to do. Rainfall has an important relationship and role with various aspects of the environment. One phenomenon that can be associated with rainfall is forest and land fires. Riau is one of the provinces in Indonesia that has a high potential for forest and land fires. This is because Riau has a large area of peatland. One approach that can be used to estimate rainfall is statistical downscaling. The concept of this approach is to form a functional relationship between global and local data. This research uses CMIP6-DCPP output data that will be used to estimate rainfall at 10 observation stations in Riau. The proposed model in this research is Stacking Ensemble with PC Regression and LASSO Regression in the base model and Multiple Linear Regression in the meta model. This research aims to determine the best CMIP6-DCPP model for estimating rainfall in Riau and increasing the accuracy of rainfall estimates using the Stacking Ensemble approach.]]>
<![CDATA[KAJIAN SIMULASI PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH MEDIAN DATA SIRKULAR ]]>
<![CDATA[Cici Suhaeni]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Anik Djuraidah]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v2i1.64
<![CDATA[The median direction is one of central tendency of circular data. The estimation process usually requires information about sampling distribution of statistic that want to be used as a parameter estimate. Theoretically, sampling distribution derived from population distribution. But, it is not easy to get sampling distribution of median although the population distribution is known. When the sampling distribution cannot be derived easily from population distribution, the bootstrap method can be an alternative to handle it. This study wants to evaluate the effect of increasing concentration parameter to the performance of bootstrap confidence interval estimation for median direction through simulation study. Three methods were used to estimate the interval which are equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). The most important criterion to evaluate them were true coverage and interval width. The simulation results that in general, the increasing of concentration parameter followed by more narrow interval. For small concentration parameter (k<1), all methods give unstable true coverage and interval width. The authors also identify that those three methods produce intervals with identical width when the parameter concentration is 20 or more. In terms of coverage and interval width, the best method was ETA.]]>
<![CDATA[ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015 ]]>
<![CDATA[Intan Lukiswati]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Utami Dyah Syafitri]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i1.331
<![CDATA[The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.]]>
<![CDATA[PEMODELAN AUTOREGRESIF SPASIAL MENGGUNAKAN BAYESIAN MODEL AVERAGING UNTUK DATA PDRB JAWA ]]>
<![CDATA[Sarimah Sarimah]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Aji H Wigena]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v3i3.376
<![CDATA[Economic data always contains spatial effects. Gross Regional Domestic Product (GRDP) in Java is one of economic data that describes spatial dependence between adjacent districts/cities. The method that is suitable for modeling GDRP is spatial regression with spatial dependence on lags that is spatial autoregressive. GDRP prediction used the Bayesian Model Averaging (BMA) method. The ten autoregressive spatial model that have highest posterior probability was chosen to determined the BMA model by posterior probability. The explanatory variables used in this study were (1) mean years of schooling (2) life expectancy (3) income per capita (4) local revenue (5) number of workers (6) district minimum salary. The results showed that the number of workers was chosen as a predictor for the ten models. The model that have highest posterior probability probability is 0.54 which contains five explanatory variables that are mean years of schooling, income per capita, local revenue, number of workers and district minimum salary and the pseudo R2 of the model is 0.696.]]>
<![CDATA[ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017 ]]>
<![CDATA[Winda Chairani Mastuti]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Erfiani Erfiani]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i1.573
<![CDATA[Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values ​​in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.]]>
<![CDATA[PENGGEROMBOLAN DERET WAKTU DENGAN PENDEKATAN UKURAN KEMIRIPAN PICCOLO UNTUK PERAMALAN CURAH HUJAN PROVINSI BANTEN ]]>
<![CDATA[Sarah Fadhlia]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Anik Djuraidah]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.607
<![CDATA[Time series data modeling can be done by modeling each object one by one. Monthly rainfall data is an example of time series data. The purpose of time series analysis is to find patterns of past data and then forecast the future characteristics of data. The data used in this study is the Banten Province rainfall data which contained 19 rainfall stations. So it will require 19 models to forecast the rainfall data. The pattern of time series data in Banten Province monthly rainfall data in several locations has similarities. So that the similarity of this pattern can be considered in the clusters. In time series clustering, the idea is to investigate the similarity of time series in a cluster. The accuracy of distance similarity size measurements is performed on the generation data generated from 3 models, namely AR (1), AR (2), and AR (3). The piccolo method has an average accuracy of 0.62. While the maharaj method has an average accuracy of 0.41. This means that the Ward hierarchical clustering method using the Piccolo distance approach has a greater accuracy value than the Maharaj distance approach. Furthermore, the Piccolo method can be used as an alternative to the excellent distance method for grouping time series data in case data. The Banten Province rainfall station has 3 optimal clusters. Modeling individual level and cluster level has accuracy values that are not much different.]]>
<![CDATA[KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST ]]>
<![CDATA[Asep Andri Fauzi]]>;
<![CDATA[Agus M. Soleh]]>;
<![CDATA[Anik Djuraidah]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i1.610
<![CDATA[Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.]]>
<![CDATA[Evaluation of Spatial Approaches of Poverty in East Java ]]>
<![CDATA[Agusta, Madania Tetiani]]>;
<![CDATA[Sartono, Bagus]]>;
<![CDATA[Djuraidah, Anik]]>
<![CDATA[ Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025) ]]>
Publisher : <![CDATA[Jurusan Matematika FMIPA UNM ]]>
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DOI: 10.35580/jmathcos.v8i1.7663
<![CDATA[Geographically Weighted Regression (GWR) is the most frequently used for spatial modeling. GWR produces local model parameter estimates for each observed point. Unfortunately, GWR is known to be numerically unstable and can produce extreme coefficient estimates. Spatially Clustered Regression (SCR) and Spatially Constrained Clusterwise Regression (SCCR) are new approaches that combine cluster identification and regression estimation in one stage. This research evaluates these approaches to develop poverty alleviation in East Java with the largest number of poor people in rural areas as per March 2023 according to BPS. The response variable used is the percentage of poor families. While the explanatory variables used are the percentage of female heads of households, the percentage of non-electricity families, the average years of schooling, the percentage of home ownership, and the percentage of agricultural laborers. The results of GWR and K-Means produced three clusters in East Java, SCR produced four clusters in East Java, and SCCR produced three clusters in East Java. Based on the AIC value, the best approach is SCR with a value of 1,614. Based on its grouping, SCR is better in forming cluster with adjacent locations rather than GWR + K-Means and SCCR. The variables that significant to the percentage of poor families are the percentage of agricultural laborers, the percentage of home ownership, and the percentage of female heads of households.]]>
<![CDATA[ENSEMBLE METHODS IN STATISTICAL DOWNSCALING WITH GAMMA-LASSO REGRESSION FOR RAINFALL PREDICTION IN WEST JAVA ]]>
<![CDATA[Sativa, Oryza]]>;
<![CDATA[Djuraidah, Anik]]>;
<![CDATA[Notodiputro, Khairil Anwar]]>
<![CDATA[ Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025) ]]>
Publisher : <![CDATA[Jurusan Matematika FMIPA UNM ]]>
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DOI: 10.35580/jmathcos.v8i1.7748
<![CDATA[Rainfall is a crucial factor in weather and climate studies, particularly in disaster mitigation efforts such as flood and landslide prevention. West Java, with its mountainous topography and high rainfall, requires accurate rainfall predictions as a basis for decision-making. One effective approach is the ensemble method, which provides valuable insights into prediction outcomes and captures uncertainty. This study analyzes rainfall data from six stations in West Java (Cibukamanah, Krangkeng, Kawali, Katulampa, Cibeureum, and Gunung Mas) over the period 1991–2020. The results indicate that applying the ensemble method in Statistical Downscaling modeling using Gamma-Lasso Regression improves rainfall prediction accuracy compared to single models.]]>
