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FORUM STATISTIKA DAN KOMPUTASI

statistika
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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.

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Articles 5 Documents

Search results for , issue "Vol. 15 No. 1 (2010)" : 5 Documents clear

METODE POHON GABUNGAN: SOLUSI PILIHAN UNTUK MENGATASI KELEMAHAN POHON REGRESI DAN KLASIFIKASI TUNGGAL Bagus Sartono; Utami Dyah Syafitri
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Classification and regression tree has been a widely used tool in various applied fields due to its capability to give excellent predictive analysis. Later on, ensemble tree appeared to enhance simple tree analysis and deals with some of the weakness found in simple techniques. The ensemble tree basically combines predictions values of many simple trees into a single prediction value. This paper is intended as an introductory article to give a brief overview of the available ensemble tree methods which might be found in detail in a variety of reading materials.

PEMODELAN RESIKO PENYAKIT KAKI GAJAH (FILARIASIS) DI PROVINSI PAPUA DENGAN REGRESI ZERO-INFLATED POISSON Sri Pingit Wulandari; Brodjol Sutijo; Ika Rahmawati
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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The goverment has established elimination of filariasis tropical disease as one of the priority programs. One of the districts that has become a target is Papua. The total amount of filariasis victim on every regency/city in Papua district can be assumed to follow a Poisson distribution. So Poisson regression method is a suitable method to know the influence factor of filariasis disease. Poisson regression model assumes equidispersion, that is equality of mean and variance of the response variable. Overdispersion test shows that the variance of the response variable exceeds its mean value. So the model is modified into zeroinflated Poisson (ZIP) regression model (logit and log). ZIP logit regression model shows that the quantity of filariasis victim in every regency/city in Papua district with zero count is influenced by the percentage of household members who sleep inside mosquito net, the percentage of household members who sleep inside insecticide musquito net, and the percentage of house-holds who keep pet (dog/cat/rabbit). While ZIP regression on log model shows that the increasing number of percentage household who keeps their pet will enhance the quantity of filariasis victim in Papua district as many as two people. Regencies/cities which need to get special attention through an elimination program of filariasis are Asmat, Tolikara, Supiori, Yapen Waropen, and Jayapura city.

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Multi-locations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction is needed in the selection of genotype to be released. AMMI (Additive Main Effect and Multiplicative Interaction) is one of the statistical techniques used to analyze data from multi-locations trials. The analysis of AMMI is a combination of analysis between additive main effect and principal component analysis. Multi-location sampling data which were collected several years on several planting season used these analyzed separately. To obtain more comprehensive information of multi-location sampling data, an analysis which combines all of the information through out the years are needed. One of the alternatives is the Bayesian approach. This method utilizes initial information on the estimated parameters and information from samples. The simulation states that prediction with Bayesian methods will produce a better estimator, because the MSE of the Bayesian estimator is smaller than the MSE estimator generated using least squares method.

Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions, the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research ar Titin Agustin; Anikk Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions, the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.

Additive Main Effects Multiplicative Interaction (AMMI) is a widely known analysis used in understanding genotype and environment interaction (GEI) in plant breeding research. The interpretation of AMMI based on biplot visualizes the first two component of principle components analysis. Biplot of AMMI is only an exploration analysis and it does not provide the hypothesis testing, so it can conduct different interpretation by plant breeding researchers. The aim of this research is to find a sys Pepi Novianti; Ahmad Ansori Mattjik; I Made Sumertajaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Additive Main Effects Multiplicative Interaction (AMMI) is a widely known analysis used in understanding genotype and environment interaction (GEI) in plant breeding research. The interpretation of AMMI based on biplot visualizes the first two component of principle components analysis. Biplot of AMMI is only an exploration analysis and it does not provide the hypothesis testing, so it can conduct different interpretation by plant breeding researchers. The aim of this research is to find a systematic approach through bootstrap resampling method. Bootstrap resampling method in AMMI model produces confidence region of the first two interaction principle component ( and ) for genotype and environment respectively. Bootstrap confidence region of and estimated the stability of genotype, thus making AMMI analysis more precise and realiable for characterization and selection of genetic environment.

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