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Ricky Yordani

Unknown Affiliation

Author-ID : 3680995

Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Penerapan Model Inferensi Bayesian dengan Variational Bayesian Principal Component Analysis (VBPCA) dalam mengatasi Missing Data Analisis Komponen Utama Ricky Yordani
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 1 (2016): Journal of Statistical Application & Statistical Computing
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

In standard Principal Component Analysis (PCA) comes one problem in addressing the set of incomplete data. The standard PCA procedure on incomplete data is to eliminate (listwise deletion procedure) or using the mean of the variable, this procedure may result in loss information from these observations. Another method used is to integrate Expectation Maximization (EM) to the method of Probabilistic Principal Component Analysis (PPCA). But PPCA can produce overfitting response prediction. In this study discussed the Variational Bayesian Principal Component Analysis (VBPCA) which is a method of development of PPCA method by incorporating prior information from the distribution of the principal components of the model parameters. From the simulation studies by eliminating the data through the concept of missing at random (MAR), obtained results that the value of the correlation scores principal components complete data with the principal component score predicted results PPCA method is superior when compared with VBPCA, as well as to the value of the correlation scores for the various percentages are generally incomplete data. However, judging from the size of a match between the response to predictions by the size normalized root mean square error of prediction (NRMSEP) VBPCA method produces better than PPCA.

Visualisasi Penggerombolan Wilayah Berdasarkan Teori Pertumbuhan Ekonomi Menggunakan Aplikasi Integrasi Self Organizing Map (SOM) dan Sistem Informasi Geografis Ricky Yordani; Hafshoh Mahmudah
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 7 No 2 (2015): Journal of Statistical Aplication and Statistical Computing
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Economic growth is one of factor that is critical to determining the welfare of a region. However, differences in geographical conditions and the potential of the area led to differences in economic conditions differ between regions. The case studies conducted on Central Java Province because it is one of the largest contributors to GDP in Indonesia, which still has economic inequality between cities and between districts. To make more easy for visualize the economic growth, researcher then made an application that is able to easily see the effect of growth and clustering in the province of Central Java. There are many methods that can be used for cluster analysis. One of the most common methods used are the K-Means. However, K-Means has some drawbacks. One alternative method is using the Self Organizing Map (SOM) which is capable clustering accompanied by visualization of multidimensional data with techniques Unsupervised Artificial Neural Network. This application allows visualization and analysis because it is integrated with Geographic Information Systems (GIS). Applications are made subsequently used to analyze clustering with case study data of Central Java province. The resulting visualization capable of showing a pattern of economic growth in Central Java Province

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