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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 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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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.

Modeling Annual Parasite Incidence of Malaria in Indonesia of 2017 using Spatial Regime Anik Djuraidah; Pika Silvianti; Bimandra Djaafara; Siti Nur Laila
Indonesian Journal of Geography Vol 53, No 2 (2021): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.53290

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.

Analisis Risiko Operasional Bank XXX dengan Metode Teori Nilai Ekstrim Anik Djuraidah; Pika Silvianti; Aris Yaman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 2 (2011)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v11i2.1054

Bank in its operations are always exposed to risks that are closely related, because of its position as afinancial 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 theminimum capital adequacy, in addition to credit and market risk. There are three approaches forsetting capital charges for operational risk, are Basic Indicator Approach, Standardized Approach andAdvanced Measurement Approach. This research used the Advanced Measurement Approach inparticular the use of Extreme Value Theory (EVT) to measure the bank XXX operational risk, this isbecause the distribution of operational risk data have a tendency panhandle. Extreme valueidentification method used is the Peaks over Threshold (POT) method. The results showed that theamount of funds bank XXX must reserve to cover the possibility of operational risk in the period of2010 amounted to Rp 737,210,874, - at 99.9% confidence level. Backtesting results demonstrate thatviable models to be used as a means of measuring operational risk by 99.9% confidence level, for alltypes of operational risk events.

CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH Rahma Anisa; Bagus Sartono; Pika Silvianti; Aam Alamudi; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.198

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study. The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.

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