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INDONESIA
FORUM STATISTIKA DAN KOMPUTASI
Published by Institut Pertanian Bogor
ISSN : 08538115     EISSN : -     DOI : -
Core Subject : Education,
Mathematics
Forum Statistika dan Komputasi (ISSN:0853-8115) was published scientific papers in the area of statistical science and the applications. It is issued twice in a year. The papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 15 No. 2 (2010)" : 5 Documents clear
APLIKASI REGRESI LOGISTIK ORDINAL MULTILEVEL UNTUK PEMODELAN DAN KLASIFIKASI HURUF MUTU MATA KULIAH METODE STATISTIKA Iin Maena; Indahwati .; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Statistical Methods  (STK211) is an interdept course under coordination of  Statistic Departement Faculty of  Mathematics and Natural Science, Bogor Agricultural University (BAU). The final grade received by student  who follow Statistical Methods is measurement  in ordinal scale,  that is A, B, C, D and E.  In the 2008/2009 academic  year  there  are  7  parallel  classes  in  the  Faculty of  Mathematics  and  Natural  Science,  BAU.  By considering the hierarchical structure contained  in the score of student achievement data, the student (first level) is  nested in a parallel class (second level), hence this study used multilevel ordinal logistic regression analysis  to  model  the  final  score  of  Statistical  Methods  with  the  factors  that  influence  it.  Explanatory variables that significantly affect the final score of Statistical Methods are the GPA of TPB (student’s first year of college) and gender, with the variability of the intercepts across parallel classes in the logit function as 1.184. Percentage classification accuracy obtained by using multilevel ordinal logistic regression model was 56.85%Keywords : hierarchical, multilevel modeling, multilevel ordinal logistic regression, classification
PENGENALAN ALGORITMA GENETIK UNTUK PEMILIHAN PEUBAH PENJELAS DALAM MODEL REGRESI MENGGUNAKAN SAS/IML Bagus Sartono
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Genetic algorithm has been a popular alternative in the various fields of optimization problem.  This paper describes some basic ideas of this algorithm and its application for selecting significant variables in the regression analysis.  Simple SAS/IML commands are presented in order to emphasize how the algorithm works.  It is also available to do some modification in some parts of those commands.
REGRESI TERBOBOTI GEOGRAFIS DENGAN PEMBOBOT KERNEL KUADRAT GANDA UNTUK DATA KEMISKINAN DI KABUPATEN JEMBER Rita Rahmawati; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The determination of whether  rural areas are considered  poor or no are usually based on  the average cost per capita with a global analysis that needs independent observations and the results are applied to all villages. But it is very likely that poverty would be influenced by space and neighboring areas, so the data between observations are rarely independent. One of the statistical analysis that encounters this spatial problem is Geographically Weighted Regression (GWR), which  gives different weights to each geographical observation. In this paper, the weighting used for the GWR model is kernel bi-square, with its bandwidth values respectively. Optimal bandwidth can be obtained by minimizing the value of cross validation coefficient (CV). The results showed that the GWR model is more effective than the regression to analyze the data on average expenditure per capita in Jember.
ALGORITMA GENETIK PENDUGAAN PARAMETER MODEL NONLINEAR JERAPAN FOSFOR (Genetic Algorithm for Parameter Estimation of Phosphorus Adsorption Nonlinear Model) Mohammad Masjkur
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Expectation Maximization (EM) was the best method used to estimate the parameters of phosphorus adsorption in  a  nonlinear  model.    However,  it  is questionable  whether  the  optimum  value  obtained  was  exactly  a  global optimum  value.    Genetic  algorithm  is  an  alternative  procedure  to  estimate  the  phosphorus  adsorption’s parameters  in  a  nonlinear  model.    The  objective  of  this  study  was  to  have  a  better  understanding  in  the  use  of genetic algorithm in maximum likelihood estimation of phosphorus adsorption nonlinear model parameters and compare  it  with  the  EM  algorithm.   This  study  used   data  of  P  adsorption  isotherms  of  kaolinitic  and  smektitic soil  in  three  locations.   Phosphorus  adsorption  nonlinear models  used  are  Freundlich  and  Langmuir. Results showed that the genetic algorithm and EM method produced different values of estimated phosphorus maximum adsorption  and  bonding  energy  parameters.   AIC  and  SBC  values  of  genetic  algorithm  is  lower  than  EM algorithm, both on the Langmuir and Freundlich models.  AIC and SBC values of Langmuir model is lower than Freundlich  model  both  for  genetic  algorithms  and  EM  algorithm. Hence,  the  best  model  for  phosphorus adsorption is Langmuir nonlinear model  with genetic algorithm.  Keywords: nonlinear model, EM, Freundlich, Langmuir, genetic algorithm
MODELLING INGREDIENT OF JAMU TO PREDICT ITS EFFICACY Farit Mochamad Afendi; Sulistiyani .; Aki Hirai; Md. Altaf-Ul-Amin .; Hiroki Takahashi; Kensuke Nakamura; Shigehiko Kanaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Jamu is an Indonesian herbal medicine made from a mixture of several plants.  Nowadays, many jamu are  produced commercially by many industries in Indonesia.  Each producer may have their own jamu formula. However, one is certain; the efficacy of jamu is determined by the composition of the plants used.  Thus, it is interesting to model the ingredient of jamu which consist of plants and use it to predict efficacy of jamu.  In this analysis, Partial Least Squares Discriminant Analysis (PLSDA) is used in modeling jamu ingredients to predict  the  efficacy.  It  is  obtained  that  utilizing the prediction of  y ij obtained  from  PLSDA  directly  rather  than  use  it  to calculate probability of jamu i belong to efficacy j and then use the probability to predict efficacy produces lower False Positive Rate (FPR) in predicting efficacy group.  Keywords: Jamu, PLSDA

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