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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.

Analisis Regresi Logistik Biner dan Random Forest untuk Prediksi Faktor-Faktor Stunting di Pulau Jawa Yuniarsyih R.A, Rizqi Dwi; Muhadi, Rizqi Annafi; Fitrianto, Anwar; Silvianti, Pika
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.31680

This study aimed to compare the performance and variable identification capabilities of Binary Logistic Regression and Random Forest models in classification analysis. The results showed that both methods consistently identified variables X1, X3, and X4 as the most influential factors in predicting outcomes. However, Binary Logistic Regression also identified variable X6 as statistically significant, which was not reflected in the Random Forest model. In terms of model performance, Random Forest outperformed Binary Logistic Regression across all evaluation metrics, including accuracy, precision, sensitivity, specificity, and balanced accuracy. These findings suggested that Random Forest was more effective in handling complex data structures and delivering optimal classification results, while Binary Logistic Regression excelled in providing deeper interpretability of variable relationships. Therefore, the choice of method should have aligned with the analytical objectives, and combining both approaches could have yielded more comprehensive insights.

Missing Value Estimation Using Fuzzy C-Means in Classification of Chronic Kidney Disease: Pendugaan Missing Values Menggunakan Fuzzy C - Means Pada Pengklasifikasian Penyakit Ginjal Kronik Eria, Raisa Nida; Alamudi, Aam; Sulvianti, Itasia Dina; Silvianti, Pika; Rahardiantoro, Septian
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p21-32

Based on World Health Organization (WHO) the cases of death due to Chronic Kidney Disease (CKD) ranked the 10th worldwide in 2020. CKD need to be done prevent early. History data to identify individuals predisposed to CKD in this research. In this research data contains missing values, therefore using Fuzzy C - Means (FCM) method to address it. The percentage of error in clustering CKD using FCM method is 20,25% and balanced accuracy of 84,80%. The result from classification using Classification and Regression Trees (CART) shows that accuracy value of 97,50%; sensitivity of 100,00%; and specificity of 92,86%. Individual suffer from CKD if having (1) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; serum creatinine less than 1,3; albumin 1 or 2 or 3 or 4 or 5; and sugar 0 or 2 or 3 or 4 or 5, (2) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; and serum creatinine more than or equal 1,3, (3) hemoglobin more than or equal 13 and spesific gravity 1,005 or 1,010 or 1,015, (4) hemoglobin less than 13 and red blood cell count less than 5,5.

IDENTIFICATION OF PRIMARY SCHOOL LITERACY ACHIEVEMENT FACTORS IN PROVINCE X USING ORDINAL STEPWISE LOGISTIC Azizah, Siti Nur; Gustiara, Dela; Fitrianto, Anwar; Erfiani; Silvianti, Pika
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09103

Literacy is a foundational skill that underpins students’ academic success and lifelong opportunities. Low literacy skills can result in long-term disadvantages such as limited access to higher education, low productivity, and social inequality. Indonesia continues to face challenges in improving students' literacy achievement, particularly at the primary school level. According to the PISA 2022 results, Indonesia ranked 69th out of 81 countries, indicating that students’ literacy levels remain relatively low. This study aims to identify the factors that influence the literacy achievement of primary school students in Province X. The analytical method employed is ordinal logistic regression with a backward stepwise approach. The dependent variable is the level of literacy achievement (categorized as low, moderate, and good), while the independent variables include learning quality, teacher reflection and improvement, instructional leadership, school climate (including safety, diversity, and inclusiveness), and curriculum type. The results show that the final selected model follows the partial proportional odds assumption and includes only the significant predictors identified through backward stepwise elimination. Variables that positively influence literacy achievement include safety climate, diversity, inclusiveness, curriculum type, and teachers’ reflection and improvement of learning. Model evaluation using AIC, BIC, and accuracy measures indicates good predictive performance. These findings offer valuable insights for policymakers in designing strategies to enhance literacy through strengthening school climate and improving the quality of teaching and learning.

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