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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Disease Prevention and Public Health Journal JUITA : Jurnal Informatika Forum Geografi Jurnal Penelitian Pertanian Tanaman Pangan Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Aplikasi Statistika & Komputasi Statistik Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Journal of Data Science and Its Applications ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Xplore: Journal of Statistics Jurnal Meteorologi dan Geofisika Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Anik Djuraidah
Department Of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Transportation Other

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OPTIMASI PENENTUAN LOKASI STASIUN PEMANTAU KUALITAS UDARA AMBIEN DI KOTA SURABAYA Anik . Djuraidah; . . Aunuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 2 (2006)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The ambient air quality monitoring system in Surabaya has five fixed monitoring stations. Monitoring provides important information for public, but is expensive to purchase, utilize, and maintain. Based on result from spatial prediction of spatio-temporal additive model for air pollutant PM10, it is necessary  to move the  existing monitoring stations at other locations. In this study, we develop a methodology for reallocation of existing monitoring network to find an optimal configuration. The result of reallocation shows that new location of monitoring network can increase the accuracy of spatial prediction,  especially at area with high concentration of PM10   Key words::  spatio-temporal data, spatio-temporal additive model, spatial prediction,  reallocation monitoring network
PENGGUNAAN JARINGAN SYARAF TIRUAN UNTUK PENDUGAAN MODEL LINEAR TERAMPAT DENGAN KOEFISIEN KERAGAMAN KONSTAN Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 13 No. 1 (2008)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Secara umum model linear terampat  (GLIM) dapat dipetakan secara ekivalen pada Jaringan Syaraf Tiruan (JST) dengan satu lapisan atau disebut juga dengan perceptron.  Fungsi aktifasi pada JST sama dengan invers dari fungsi hubung. Pada GLIM dengan komponen acak mempunyai sebaran gamma ekivalen dengan JST tanpa lapisan tersembunyi dengan fungsi galat adalah gamma dan fungsi tujuan adalah fungsi kemungkinan maksimum atau devians. Sedangkan fungsi aktifasi untuk model gamma adalah identitas, resiprokal, atau eksponensial. Makalah ini mengkaji pendugaan model pada data yang mempunyai sebaran gamma  dengan metode JST dan seberapa besar perbedaan hasil pendugaannya dibandingkan dengan GLIM. Hasil kajian menunjukkan bahwa  JST  menghasilkan  nilai dugaan yang sama dengan GLIM. Kata kunci : jaringan syaraf tiruan, koefisien keragaman konstan, model gamma
SEJARAH PERKEMBANGAN STATISTIKA DAN APLIKASINYA Sony Sunaryo; Setiawan Setiawan; Anik Djuraidah; Asep Saefuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 8 No. 1 (2003)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Statistika diawali sebagai ilmu untuk mengumpulkan angka (data).  Pada abad 17  statistika deskriptif mulai berkembang, begitu juga ilmu peluang yang awalnya dilahirkan dari meja judi sudah mulai muncul .  Ilmu peluang ini melandasi berkembangnya statistika induktif yang terjadi pada pertukaran abad 19 dan 20 dengan Karl Pearson sebagai pelopornya. Statistika induktif berkembang pesat setelah R. A. Fisher memperkenalkan metode Maximum Likelihood pada tahun 1922. Dengan adanya perkembangan teknologi komputer, metode eksplorasi data dan bootstrap mulai berkembang pada tahun 1970.  Metode ini sebagai awal dari analisis data tanpa model peluang yang populer dengan data driven. Seiring dengan perkembangan statistika induktif, statistika mulai diterapkan pada berbagai bidang  seperti ekonomi, industri, pertanian, sosiologi, psikologi, dan lain-lain.Di bidang ekonomi aplikasi statistika pada ekonometrika, sedangkan di bidang  industri aplikasi yang sangat terkenal adalah metode Quality Control dan metode Six-Sigma.Pada abad 21 diperkirakan metode data mining akan banyak digunakan dalam bidang terapan.  Perkembangan ini akan berpengaruh terhadap model pendidikan dan pengajaran statistika dewasa ini.Di Indonesia penggunaan statistika dipelopori dengan dibukanya  program pendidikan statistika di bawah naungan Jurusan Statistika IPB  (S1 sejak tahun 1967 dan S2 sejak tahun 1975). Peran Jurusan Statistika IPB baik lewat mata kuliah pelayanan pada jurusan lain di lingkungan IPB, maupun para lulusannya yang sudah tersebar di bergai bidang pekerjaan memberikan dampak positif bagi penggunaan statistika sebagai alat bantu analisis.Sekarang selain IPB ada  PTN  dan PTS yang telah membuka jurusan statistika. 
PENDEKATAN KUADRAT TERKECIL PARSIAL KEKAR UNTUK PENANGANAN PENCILAN PADA DATA KALIBRASI Enny Keristiana Sinaga; Anik Djuraidah; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The serious problems in the calibration of multivariate estimation are multicollinearity and outliers. Partial Least Squares (PLS) is one of the statistical method used in chemometrics, to handle high or perfect multicollinearity in independent variables. Straightforward Implementation Partial Least Squares (SIMPLS) is the extension of PLS regression proposed by De Jong (1993). The SIMPLS algorithm is based on the empirical cross-variance matrix between the independent variables and the regressors. This method does not resistant toward outlier observations. Robust PLS method is used to handle the multicollinearity and outliers in the data sets. This method can be classified in two groups, there are iteratively reweighting technique and robustication of covariance matrix. Partial Regression-M (PRM) method is one of the robust PLS methods used the idea of iteratively reweighting technique that proposed by Serneels et al. (2005). Robust SIMPLS (RSIMPLS) method is one of the robust PLS methods used the idea of robustication of covariance that proposed by Huber and Branden (2003). A modified RSIMPLS used M estimator with the Huber weight function called RSIMPLS-M was proposed by Ismah (2010). These two methods (RSIMPLS-M and PRM) are applied to Fish data (Naes 1985) to know their performances. The research results indicated that the values of R2 and RMSEP of RSIMPLS-M are higher than those of PRM method. Whereas based on the confidence interval estimation of the regression coefficients by jackknife method, estimation of PRM is narrower than that RSIMPLS-M method. Therefore RSIMPLS-M method is better than PRM method for prediction, whereas PRM method is better than RSIMPLS-M method for estimation.Keywords : Partial least squares regression robust (PLSRR), partial robust M-regression (PRM), straightforward implementation partial least squares robust (RSIMPLS)
PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH RATA-RATA DATA SIRKULAR (Bootstrap Confidence Interval Estimation of Mean Direction for Circular Data) Cici Suhaeni; I Made Sumertajaya; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The confidence interval is an estimator based on the sampling distribution. When the sampling distribution can not be derived from population distribution, the bootstrap method can be used to estimate it. Three methods used to estimate the bootstrap confidence interval for circular data were equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). In this study, three methods were evaluated through simulation study. The most important criterion to evaluate them were true coverage and interval width. The simulation results indicated in all methods, the interval width shortened when the concentration parameter increased. True coverage approached confidence level when the concentration parameter were one or more. For small concentration parameter, all three methods appeared unstable. Based on the true coverage, SYMA was the best, while in terms the interval width, LBA was the best one. For both criterion could be summarized that ETA is the best result. ETA applicated for estimate the period of Dengue Fever outbreaks in Bengkulu. The estimation showed that Dengue Fever outbreaks in 2009 were October through January. In 2010, it were January through March, and in 2011, it were June through September.Keywords : Circular, Bootstrap confidence interval, Equal-tailed arc, Symmetric arc, Likelihood-based arc.
MODEL REGRESI BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA KEMATIAN BAYI (Studi Kasus 38 Kabupaten/Kota di Jawa Timur) (Geographically Weighted Negative Binomial Regression for Infant Mortality Data) (Case Study 38 Regency/City in East Java) Lusi Eka Afri; _ Aunuddin; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Negative binomial regression model is used to overcome the overdispersion in Poisson regression model. This model can be used to model the relationship of the infant mortality and the factors incidence. Geographical conditions, socio cultural and economic differ one of location another location causes the factors that influence infant mortality is different locally. Geographically Weighted Negative Binomial Regression (GWNBR) is one of methods for modeling that count data have spatial heterogeneity and overdispersion. The basic idea of this model considers of geography or location as the weight in parameter estimation. The parameter estimator is obtained from Iteratively Newton Raphson method. This research will determine the factors that influence infant mortality. GWNBR model with a weighting adaptive bi-square kernel function classifies regency/city in East Java into 16 groups based on the factors that significantly influence the number of infant mortality. This model is better used to analyze the number of infant mortality in East Java in 2008 due to a smallest deviance value.Keywords : Negative binomial regression, geographically weighted negative binomial regression, adaptive bi-square, overdispersion
GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) INCLUDED THE DATA CONTAINING MULTICOLLINEARITY Ira Yulita; Anik Djuraidah; Aji Hamin Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

One of the reasons of spatial effect of each location is spatial variety. Beside of spatial variety, number of independent variable (X) causes local multicolinearity, that is one or more independent variable, which collaborated with other variable in each location of observation. The methods can be used to solve spatial diversity problem and local multicollinearity in Geographically Weighted Regression (GWR) model that is GWPCA. This research aim to examine GWPCAR feasibility model for PDRB data in 2010 at 113 districts/cities in Java. analysis indicate that GWPCA method can overcome local multicollinearity problem, it can be seen from the characteristic value of VIF which is smaller than 10.Key words : Local Multicollinearity, Geographically Weighted Principal Components Analysis.
Modeling Annual Parasite Incidence of Malaria in Indonesia of 2017 using Spatial Regime Anik Djuraidah; Pika Silvianti; Bimandra Djaafara; Siti Nur Laila
Indonesian Journal of Geography Vol 53, No 2 (2021): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.53290

Abstract

Malaria is an infectious disease caused by the Plasmodium parasite and transmitted through infected female Anopheles mosquitoes. The morbidity of malaria is determined by Annual Parasite Incidence (API) per year. A region with high malaria cases can spread malaria to other regions. Therefore, the purpose of this study is to determine the spatial regimes and factors that significantly influence the spread of malaria in Indonesia of 2017. Spatial regime is a method obtained by clustering the coefficient values from the well-known method in modeling spatial varying relationship namely geographically weighted regression (GWR). The data used in this study are malaria Passive Case Detection (PCD) from Puskesmas throughout Indonesia in 2017. The results show three groups which can be classified as regencies/cities with low, medium moderate and high API, while slide positivity rate and annual blood examination are predictors who influent API numbers in Indonesia significantly. 
PENDUGAAN PARAMETER REGRESI LOGISTIK DENGAN JARINGAN SYARAF TIRUAN Anik Djuraidah
PYTHAGORAS Jurnal Pendidikan Matematika Vol 3, No 1: Juni 2007
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (296.347 KB) | DOI: 10.21831/pg.v3i1.642

Abstract

Logistic regression can be mapped equivalent to artificial neural network (ANN) without hidden layer with logistic as activation function, hence logistic regression is subset of ANN. The result study on binary and poly-chotomous response data show that parameter estimation values of ANN and logistic regression are similar. In comparison with ANN, logistic regression has standard procedure for estimation and testing parameter.Keyword : logistic regression, generalized linear model, artificial neural network, activation function, hidden layer
Regression for Exploring Rainfall Pattern in Indramayu Regency Anik Djuraidah; Aji Hamim Wigena
Jurnal ILMU DASAR Vol 12 No 1 (2011)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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Abstract

Quantile regression is an important tool for conditional quantiles estimation of a response Y for a given vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. Regression coefficients for each quantile can be estimated through an objective function which is weighted average absolute errors. Each quantile regression characterizes a particular aspect of a conditional distribution. Thus we can combine different quantile regressions to describe more completely the underlying conditional distribution. The analysis model of quantile regression would be specifically useful when the conditional distribution is not a normal shape, such as an asymmetric distribution or truncated distribution. In general, rainfall in Indramayu regency during 1972-2001 at 23 stations is highly variable in amount across time (month)andspace. So,the first objective of the research is reducing the variability in space using classification of the rainfall stations. The second objective is modelling the variability in time using quantile regression for every cluster of rainfall stations. The result shows that there are two clusters of rainfall stations. The first cluster has higher amount of rainfall than the second cluster. The coefficient of quantile regression for quantile 50 and 75 percent are similar, but for quantile 5 and 90 percent are very different. Exploring pattern of rainfall using quantile regression can detect normal or extreme rainfall that very useful in agricultural.
Co-Authors . . Aunuddin Aam Alamudi Abqorunnisa, Farah Agus M. Soleh Agus Mohamad Soleh Agusta, Madania Tetiani Aji H Wigena Aji Hamim Wigena Aji Hamin Wigena Alfa Nugraha Pradana Alfa Nugraha Pradana Alfan, Tony Alwi Aliu, Muftih Anang Kurnia Anisa, Rahma Ardiansyah, Muhlis Aris Yaman Asep Andri Fauzi ASEP SAEFUDDIN Aunuddin Aunuddin Azizah Desiwari Bagus Sartono Banan Nabila Bimandra Djaafara Cici Suhaeni Cici Suheni Dani Al Mahkya Dewi Retno Sari Saputro, Dewi Retno Erfiani Erfiani Fauziah, Ghina Fitrianto, Anwar Hanifa Izzati Hardinsyah Haryanto, Sugi Herlina Hanum Herlina Hanum, Herlina I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Intan Lukiswati Ira Yulita Ismah . Ismah, Ismah Itasia Dina Sulvianti Lismayani Usman Lusi Eka Afri Mely Amelia Miranti, Ita Miranti, Ita Mohamad Arif Pramarta Muhammad Nur Aidi Novi Hidayat Pusponegoro Oryza Sativa Pigitha, Nindi Pika Silvianti Pranata, Ismail Putri Astrini, Yufan Putri Astrini Rahardiantoro, Septian Rahma Anisa Resti Cahyati Resty Fanny Retna Nurwulan Retno Ariyanti Pratiwi Retsi Firda Maulina Ristiyanti Tarida, Arna Rita Rahmawati Rizki, Akbar Sarah Fadhlia Sari, Mutia Dwi Permata Sarimah Sarimah Septemberini, Cintia Setiawan Setiawan Sinaga, Enny Keristiana Siti Nur Laila Sony Sunaryo Sugi Haryanto Syam, Ummul Auliyah Tarida, Arna Ristiyanti Tasya Meilania, Gusti Titin Agustin Utami Dyah Syafitri Winda Chairani Mastuti Yoga Primanda Zulkarnain, Rizky _ Aunuddin