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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Statistika Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Indonesian Journal of Statistics and Its Applications
Pika Silvianti
Dept. Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : FORUM STATISTIKA DAN KOMPUTASI

 1 
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 collecte Pika Silvianti; Khairil Anwar Notodiputro; I Made Sumertajaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

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.
KLASIFIKASI GENOTIPE PADA DATA TIDAK LENGKAP DENGAN PENDEKATAN MODEL AMMI Ahmad Anshori Mattjik; I Made Sumertajaya; Pika Silvianti
FORUM STATISTIKA DAN KOMPUTASI Vol. 12 No. 1 (2007)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Percobaan multilokasi mempunyai peranan penting dalam perkembangbiakan tanaman dan penelitian agronomi. Kajian mengenai interaksi antara genotipe dan lingkungan diperlukan dalam penyeleksian genotipe yang akan dilepas. Metode statistika yang biasa digunakan untuk mengolah data hasil percobaan multilokasi salah satunya adalah AMMI (Additive Main effect and Multiplicative Interaction).  Metode ini menggabungkan analisis ragam  aditif bagi pengaruh utama perlakuan dengan analisis komponen utama pada pengaruh interaksinya. Pendekatan AMMI juga sangat baik digunakan untuk uji multilokasi tanpa ulangan. AMMI adalah analisis yang membutuhkan data yang lengkap. Jika ada data yang hilang, maka harus dilakukan pendugaan terhadap data tersebut. Pada kasus data tidak lengkap, diperlukan suatu metode pendugaan data untuk mempermudah analisis. Metode yang dapat  digunakan antara lain connected data dan algoritma EM-AMMI untuk menduga data yang tak lengkap.
Co-Authors Aam Alamudi Ahmad Anshori Mattjik Anik Djuraidah Aris Yaman Bagus Sartono Bimandra Djaafara Erfiani Eria, Raisa Nida Fitrianto, Anwar Gustiara, Dela I Made Sumertajaya Indonesian Journal of Statistics and Its Applications IJSA Itasia Dina Sulvianti Muhadi, Rizqi Annafi Rahardiantoro, Septian Rahma Anisa Rahma Anisa Satria Yudha Herawan Siti Nur Azizah, Siti Nur Siti Nur Laila Yuniarsyih R.A, Rizqi Dwi