PENDEKATAN METODE BAYES UNTUK PENDUGAAN PENGARUH INTERAKSI PADA MODEL AMMI

Multi-locations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction needed in selection of genotype to be released. AMMI (Additive Main effect and Multiplicative Interaction) is one of statistical technique to analyze data from multi-locations trials. The analysis of AMMI is a combining analysis between additive main effect and principal component analysis. Multi-location sampling data which were collected several years on several planting season used to be analyzed separately. To obtain more comprehensive information of multi-location sampling data, an analysis which combines all the information in several years is 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 MSE of the Bayesian estimator is smaller the MSE estimator generated using least squares method. Keywords: AMMI, Baye

Analisis Risiko Operasional Bank XXX dengan Metode Teori Nilai Ekstrim

Bank in its operations are always exposed to risks that are closely related, because of its position as a financial intermediary institutions. One of the risks which arise when this is operational risk. Operational risk to be one additional factor that must be measured and taken into account in the minimum capital adequacy, in addition to credit and market risk. There are three approaches for setting capital charges for operational risk, are Basic Indicator Approach, Standardized Approach and Advanced Measurement Approach. This research used the Advanced Measurement Approach in particular the use of Extreme Value Theory (EVT) to measure the bank XXX operational risk, this is because the distribution of operational risk data have a tendency panhandle. Extreme value identification method used is the Peaks over Threshold (POT) method. The results showed that the amount of funds bank XXX must reserve to cover the possibility of operational risk in the period of 2010 amounted to Rp 737,210,874, - at 99.9% confidence level. Backtesting results demonstrate that viable models to be used as a means of measuring operational risk by 99.9% confidence level, for all types of operational risk events

KLASIFIKASI GENOTIPE PADA DATA TIDAK LENGKAP DENGAN PENDEKATAN MODEL AMMI

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

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.

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

Modeling Annual Parasite Incidence of Malaria in Indonesia of 2017 using Spatial Regime

Malaria is an infectious disease caused by the Plasmodium parasite and transmitted through infected female Anopheles mosquitoes. The morbidity of malaria is determined by Annual Parasite Incidence (API) per year. A region with high malaria cases can spread malaria to other regions. Therefore, the purpose of this study is to determine the spatial regimes and factors that significantly influence the spread of malaria in Indonesia of 2017. Spatial regime is a method obtained by clustering the coefficient values from the well-known method in modeling spatial varying relationship namely geographically weighted regression (GWR). The data used in this study are malaria Passive Case Detection (PCD) from Puskesmas throughout Indonesia in 2017. The results show three groups which can be classified as regencies/cities with low, medium moderate and high API, while slide positivity rate and annual blood examination are predictors who influent API numbers in Indonesia significantly.