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Pika Silvianti’s Publications

Lestari, G., Suhaeni, C., & Silvianti, P. (2019). Clustering Babyshop at Marketplace X with Cluster Ensemble based on Squeezer Algorithm. Xplore: Journal of Statistics, 8(1).
Anisa, R., Sartono, B., Silvianti, P., Alamudi, A., & others. (2019). CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH. Indonesian Journal of Statistics and Its Applications, 3(1), 78–90.
Fitriani, N. L., Silvianti, P., & Anisa, R. (2018). Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index. Xplore: Journal of Statistics, 7(3).
Silvianti, P., & Fitriani, N. L. (2018). Analisis Pengaruh Kurs USD terhadap Jakarta Islamic Index dengan Menggunakan Model Fungsi Transfer. Xplore: Journal of Statistics, 2(2), 66–72.
Kurnia, A., Kusumaningrum, D., Silvianti, P., & Handayani, D. (2013). Winsorization on Small Area Inference With Positively Skewed Distributions. In Proceedings of The International Conference on Applied Statistics of Padjadjaran University and Statistical Forum for Higher Education (ICAS) (pp. 210–216).
Herawan, S. Y., Sumertajaya, I. M., & Silvianti, P. (2013). UJI MULTILOKASI MELALUI ANALISIS AMMI MULTIRESPON (Studi Kasus: Penelitian Galur Tanaman Tembakau Madura). Xplore: Journal of Statistics, 1(1).
Djuraidah, A., Silvianti, P., & Yaman, A. (2011). Analisis Risiko Operasional Bank XXX dengan Metode Teori Nilai Ekstrim. STATISTIKA: Journal of Theoretical Statistics and Its Applications, 11(2).
Silvianti, P., Notodiputro, K. A., & Sumertajaya, I. M. (2010). 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. In Forum Statistika dan Komputasi (Vol. 15).
Mattjik, A. A., Sumertajaya, I. M., & Silvianti, P. (2007). KLASIFIKASI GENOTIPE PADA DATA TIDAK LENGKAP DENGAN PENDEKATAN MODEL AMMI. In Forum Statistika dan Komputasi (Vol. 12).
October 2020


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