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TRANSFORMASI BOX-COX UNTUK KENORMALAN KOMPONEN UTAMA (KASUS BEBERAPA DATA PERTANIAN) Komalig, Hanny AH; Siswadi, .; Suhardjo, Budi; Wigena, Aji Hamim
FORUM STATISTIKA DAN KOMPUTASI Vol. 6 No. 1 (2001)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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

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Abstract

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.
REGRESI KUADRAT TERKECIL PARSIAL MULTI RESPON UNTUK STATISTICAL DOWNSCALING (MULTI RESPONSE PARTIAL LEAST SQUARE FOR STATISTICAL DOWNSCALING) Wigena, Aji Hamim
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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
MODEL VEKTOR AUTOREGRESSIVE UNTUK PERAMALAN CURAH HUJAN DI INDRAMAYU (VECTOR AUTOREGRESSIVE MODEL FOR FORECAST RAINFALL IN INDRAMAYU ) Saputro, Dewi Retno Sari; Wigena, Aji Hamim; Djuraidah, Anik
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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
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 ., Ismah; Wigena, Aji Hamim; Djuraidah, Anik
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 1 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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
APLIKASI PROJECTION PURSUIT DAN JARINGAN SYARAF TIRUAN DALAM PEMODELAN STATISTICAL-DOWNSCALING Wigena, Aji Hamim; Aunuddin, Aunuddin
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.869

Abstract

Model statistical-downscaling digunakan untuk mengetahui hubungan antara peubah iklim skala global (data GCM)dengan peubah iklim skala lokal (data curah hujan). Metode Projection Pursuit digunakan untuk mereduksi dimensi besardata GCM menjadi dimensi kecil, serupa dengan Analisis Komponen Utama. Pendugaan model dilakukan dengan metodeProjection Pursuit dan jaringan syaraf tiruan (ANN). Dugaan curah hujan dengan ANN lebih baik daripada dugaan curahhujan tidak dengan ANN. Keduanya dibandingkan berdasarkan RMSE dan korelasi curah hujan aktual dengan dugaannya.
IDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN MAHASISWA PUTUS KULIAH DI IPB ANGKATAN 2008 MENGGUNAKAN ANALISIS SURVIVAL Imran, Fadjrian; Susetyo, Budi; Wigena, Aji Hamim
Xplore: Journal of Statistics Vol. 1 No. 2 (2013)
Publisher : Departemen Statistika IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.126 KB) | DOI: 10.29244/xplore.v1i2.12404

Abstract

Dropped out of college not only cause harm to students, but also the college. The more the number of students dropping out of college can be a portrait of the quality of higher education, so that the information and analysis needed to determine the factors associated with dropping out of college. Time of the incident dropped out of college can occur at any time, while the observation time has a time limit so that it takes a specific method to be completed. Appropriate method to resolve these problems one of which is survival analysis using the principles of censored data or not censored data. Response used is drop out and resigned, while the explanatory variables, there were 13 variables. Results Cox proportional hazard regression model with variable selection method using the forward yield drop out conclusions on the response produced the best model with three explanatory variables gender, GPA and faculty. Response resign produce the best model with two explanatory variables GPA and faculty. Male student has a chance to drop out faster than female studentsKeywords-survival analysis, proportional hazard, censored, not censored
PEMBOBOTAN SUB DIMENSION INDICATOR INDEX UNTUK PENGGABUNGAN CURAH HUJAN (STUDI KASUS : 15 STASIUN PENAKAR CURAH HUJAN DI KABUPATEN INDRAMAYU) Syazwina, Fildzah Hanum; Wigena, Aji Hamim; Aidi, Muhammad Nur
Xplore: Journal of Statistics Vol. 1 No. 1 (2013)
Publisher : Departemen Statistika IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.873 KB) | DOI: 10.29244/xplore.v1i1.12403

Abstract

In recent years, combining of rainfall in a region is using average method. Beside average method, there are Sub Dimension Indicator Index (SDII) Range Equalisation (RE) and SDII Division by Mean (DM) that can be used to combine rainfall. The aim of this research is to compare three methods above based on forecasting result of time series data analysis. The data set of this research is monthly rainfall data from the period 1979 to 2008 located on 15 stations in the district of Indramayu, divided into two seasonal forecast regions. The data are grouped by month (January until December) then the next step is to calculate average and also weighted average based on SDII RE and SDII DM. The result of this research shows that three methods gave similar cross-correlation values, that are between 0.88 and 0.90.Keywords-DPM, SDII, time series data analysis
REGRESSION FOR EXPLORING RAINFALL PATTERN IN INDRAMAYU REGENCY Djuraidah, Anik; Wigena, Aji Hamim
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.
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) Fadli, Geri Zanuar; Aunuddin, _; Wigena, Aji Hamim
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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