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All Journal Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Dasar Statistika Jurnal Ilmiah Aplikasi Isotop Radiasi Syntax Literate : Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry Indonesian Journal of Statistics and Its Applications Krea-TIF: Jurnal Teknik Informatika JURNAL METEOROLOGI DAN GEOFISIKA SRIWIJAYA JOURNAL OF ENVIRONMENT

Aji Hamim Wigena

Department Of Statistics, Faculty Of Mathematics And Science, Bogor Agricultural University, Dramaga-Bogor

Author-ID : 416912

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Transportation Other

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Pengembangan dan Aplikasi Geoinformatika Bayesian pada Data Kemiskinan di Indonesia (Studi Kasus Jawa Timur) Asep Saefuddin; Aji Hamim Wigena; Nunung Nuryartono
Jurnal Ilmu Pertanian Indonesia Vol. 17 No. 2 (2012): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

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Since a long time ago, poverty has been a problem that can not be solved. Indonesia’s Central Beureu of Statistics (CBS) survey on March 2011 show that there are 30.02 million people or 12.49% of total Indonesian are considered poor. From the point of view of many field of sciences the substance and the method to overcome this problem has become a very interesting topic of research. Based on statistical methods, poverty has become very interesting because there is an issue that there is an autocorrelation between data, spatial autocorrelation, error variance heterogenity, spatial interaction, and other statistical issues. The main objective of this research is to find factors that influence poverty rate in a region by developing spatial bayesian statistics. The methods developed in this research include Simultan Autoregressive (SAR), Conditional Autoregressive (CAR), Geographically Weighted Regression (GWR), Small Area Estimation (SAE) and hotspot detection. Based on the SAR Bayes model it is shown that the percented of people not graduating elementary school has a significant effect on poverty rate. While the increase of spatial autocorrelation will influence the poverty rate by 0.10 in East Java. Beside that by using hierarchical bayes logit normal model with nearest neighboor spatial weighted found that 40.93% of families of Jember is considered poor.

Transformasi BOX-COX untuk Kenormalan Komponen Utama (Kasus Beberapa Data Pertanian) Hanny AH Komalig; . Siswadi; Budi Suhardjo; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 6 No. 1 (2001)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Untuk memperbaiki kenormalan data peubah ganda bagi analisis komponen utama (AKU) dapat dilakukan transformasi Box-Cox. Penelitian ini bertujuan untuk menelusuri perubahan konfigurasi sebagai akibat perbaikan asumsi kenormalan beberapa komponen utama dalam AKU.Penggunaan transformasi Box-Cox dapat memperbaiki kenormalan sejumlah data yang tidak berdistribusi normal. Perbaikan kenormalan dengan transformasi Box-Cox dalam dimensi rendah cenderung merubah konfigurasi dan perubahan ini berkaitan dengan struktur data yang ada gugus data tersebut. Penggunaan transformasi Box-Cox untuk menormalkan komponen utama sangat berkaitan dengan tujuan penggunaan hasil komponen utama tersebut. Apabila tujuannya untuk inferensia mengenai struktur komponen utama, maka transformasi Box-Cox dapat digunakan untuk menormalkan komponen-komponen utama. Tetapi bila komponen utama ditujukan hanya untuk mendapatkan suatu deskripsi sederhana mengenai pengamatan-pengamatan, maka penggunaan transformasi Box-Cox tidak penting untuk dilakukan.

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Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP) merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah algoritma pendugaan RKTP yang tidak resisten terhadap pengamatan pencilan. Hubert and Brande (2003) mengemukakan algoritma RSIMPLS yang bersifat resisten terhadap pencilan. RSIMPLS dibentuk dari matriks ragam-peragam robust dan regresi linear robust. Pada penelitian ini dilakukan modifikasi fungsi bobot pada RSIMPLS dengan penduga-M Huber dimana setiap pengamatan akan diberikan nilai bobot kecil jika jarak robust dan jarak ortogonal pengamatan ke-i melebihi nilai batas yang ditentukan, dan untuk lainnya. Dengan demikian besar tidak hanya 0 dan 1, melainkan . Hasil penelitian menunjukkan RMSEP (root mean square error) pada metode modifikasi bobot lebih kecil dibandingkan RSIMPLS

MODEL VEKTOR AUTOREGRESSIVE UNTUK PERAMALAN CURAH HUJAN DI INDRAMAYU (Vector Autoregressive Model for Forecast Rainfall In Indramayu ) Dewi Retno Sari Saputro; Aji Hamim Wigena; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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There are three regions of rainfall that has been formed, each rainfall regions has a variety of homogeneous and there is a correlation between rainfall stations. In each region can be determined rainfall prediction model simultaneously. The model is a model of Vector Autoregressive (VAR) which is an extension of the autoregressive model (AR). Based on this research, we can determine the VAR model by lag 1 or VAR (1) for each region. Region 1 (Anjatan and Sumurwatu), region 2 (Salamdarma and Gantar) and region 3 (Kedokan Bunder and Sudimampir), each of which has a Root Mean Square Error Prediction (RMSEP) of 3.93; 5:03; 4:48; 5.3; 2:18 and 3:53. Correlation value of observations with predictions of rainfall respectively, 0.71; 0.62; 0:57; 0:59; 0.89, and 0.91. Keywords: AR, VAR, RMSEP, correlation

REGRESI KUADRAT TERKECIL PARSIAL MULTI RESPON UNTUK STATISTICAL DOWNSCALING (Multi Response Partial Least Square for Statistical Downscaling) Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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In climatology partial least square regression (PLSR) can be used as an alternative technique in statistical downscaling based on global circulation model (GCM) output. PLSR is the technique to forecast not only one response but also multi responses to accommodate the correlation among responses. PLSR is compared to PCR (Principal Component Regression). The results show that PLSR is better than PCR and can be used to forecast rainfall simultaneously in more than one rainfall stations relatively as well as in one station. Keywords: statistical downscaling, PLSR, PCR, multi responses

PENENTUAN DOMAIN DENGAN TEKNIK VARIOGRAM Aji Hamim Wigena; Utami Dyah Syafitri; Yuan Millafanti
FORUM STATISTIKA DAN KOMPUTASI Vol. 12 No. 2 (2007)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Dalam banyak kesempatan, penyusunan model skoring untuk memprediksi klasifikasi calon nasabah dilakukan menggunakan model regresi logistik dan beberapa model lain. Proses pengklasifikasian dapat juga dilakukan dengan menerapkan simple naive Bayesian classifier. Meskipun menggunakan asumsi yang secara umum dilanggar oleh data dan proses komputasi yang jauh lebih sederhana, teknik ini mampu menghasilkan akurasi dugaan yang tidak mengecewakan. Paper ini memberikan ilustrasi penggunaan simple naive bayesian classifier pada kasus prediksi klasifikasi status kolektibilitas calon nasabah dan membandingkannya dengan model regresi logistik dan generalized additive model. Kata kunci: simple naive Bayesian classifier

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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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)

PENERAPAN PEMBOBOTAN KOMPONEN UTAMA UNTUK PEREDUKSIAN PEUBAH PADA ADDITIVE MAIN EFFECT AND MULTIPLICATIVE INTERACTION (Application of Weighted Principal Component for Variable Reduction in Additive Main Effect and Multiplicative Interaction) Geri Zanuar Fadli; _ Aunuddin; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Indonesia is the country with the largest level of rice consumption in the world. Therefore, it need to be done an effort to increase the production of rice. One way to increase rice production is land management as well as conducting an intensive new superior varieties which has a high yield. Hybrid rice is a type of rice which has a higher result among superior varieties. Hybrid rice breeding can be done with multi-locations trials that involves two main factors, plant and environmental conditions. AMMI (Additive Main Effects and Multiplicative Interaction) is a method of multivariate used in plant breeding research to examine the interaction of genotype × environment on multi-locations trials. Generally, AMMI analysis is still using a single response. Whereas, the adaptation level of the plant is not only seen from the aspect of its yield. Therefore, this study based on combined response using AMMI analysis. The Data in this study is secondary data multi-locations trials on hybrid rice planting season 2008/2009 which involved four sites and 12 genotype. The measured response are = yield (ton/ha), = 1000 grain weight (gram), = the number of penicles per m2, dan = length of penicle (cm). The merger of response using weighted method by principal component. AMMI analysis with as response produce five stable genotypes in any location, that are IH804, IH805, IH806, Hibrindo, and Ciherang. AMMI is also generating specific genotypes are those that perform good adaptability at certain environment condition. IH802, IH803, and IH809 genotypes in Jember planting season 2, IH808 and Maro genotypes in Ngawi. Keywords : AMMI, the merger of response, weighted principal component method

NONLINEAR PRINCIPAL COMPONENT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS WITH SUCCESSIVE INTERVAL IN K-MEANS CLUSTER ANALYSIS Arista Marlince Tamonob; Asep Saefuddin; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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K-Means Cluster is a cluster analysis for continuous variables with the concept of distance used is a euclidean distance where that distance is used as observation variables which are uncorrelated with each other. The case with the type data that is correlated categorical can be solved either by Nonlinear Principal Component Analysis or by making categorical data into numerical data by the method called successive interval and then used Principal Component Analysis. By comparing the ratio of the variance within cluster and between cluster in poverty data of East Nusa Tenggara Province in K-Means cluster obtained that Principal Component Analysis with Successive interval has a smaller variance ratio than Nonlinear Principal Component Analysis. Variables that take effect to the clusterformation are toilet, fuel,and job.Keywords: K-Means Cluster Analysis, Nonlinear Principal Component Analysis, Principal Component Analysis, Successive interval.

RIDGE AND LASSO PERFORMANCE IN SPATIAL DATA WITH HETEROGENEITY AND MULTICOLLINEARITY Tiyas Yulita; Asep Saefuddin; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Spatial heterogeneity becomes a separate issue on the analysis of spatial data. GWR (Geographically Weighted Regression) is a statistical technique to explore spatial nonstationarity by form the differrent regression models at different point in observation space. Multicollinearity is a condition that the independent variables in model have linear relationship. It would be a problem for estimation parameters process, because that condition produces unstable model. This problem may be found in GWR models, which allow the linear relationship between independent variables at each location called local multicollinearity. GWRR (Geographically Weighted Ridge Regression) and GWL (Geographically Weighted Lasso) which use the concept of ridge and lasso is shrink the regression coefficient in GWR model. GWRR and GWL techniques are consider to be capable of overcoming local multicollinearity to produce more stable models with lower variance. In this study, GWRR and GWL is used to model Gross Regional Domestic Product (GRDP) in Java using kernel exponential weighted function. The results showed that GWL has better performance to predict GRDP with lower RMSE and higher value than GWRR.Keyword : Spatial Heterogeneity, GWR, Local Multicollinearity, Ridge, Lasso

Co-Authors . Siswadi Aan Kardiana Abdul Kudus Agus Mohamad Soleh Anang Kurnia Anik Djuraidah Arista Marlince Tamonob ASEP SAEFUDDIN Aunuddin Aunuddin Bagus Sartono Budi Suhardjo Budi Susetyo Bunawan Sunarlim Dedi Sucahyono Dewi Retno Sari Saputro, Dewi Retno Edvin Aldrian Erfiani Erfiani Erfiani Erfiani Erwin Eka Syahputra Makmur Fadjrian Imran Faula Arina Fildzah Hanum Syazwina Freza Riana Galih Hedy Saputra Geri Zanuar Fadli Hanny Komalig Hari Wijayanto Herlina Hanum Herlina Hanum, Herlina Hidayat Pawitan I Made Sumertajaya I Wayan Mangku Ismah . Latifah K. Darusman Made Agung Prebawa Parama Artha Maemonah, Maemonah Mia Sri Listiani Ahmad Muhammad Nur Aidi Nunung Nuryartono Pingkan Awalia Sinaga, Enny Keristiana Soeranto Human Tiyas Yulita Trinah Wati Urip Haryoko Utami Dyah Syafitri Utami Syafitri Woro Estiningtyas Wulan Tri Wahyuni Yonny Koesmaryono Yuan Millafanti Yumna Karimah Yunus Subagyo Swarinoto _ Aunuddin

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