Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Dasar Jurnal Matematika Sains dan Teknologi
Zanzawi Soejoeti
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Control & Systems Engineering Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SUATU STUDI TENTANG UJI HIDUP DIPERCEPAT TEGANGANBERTINGKAT: PERKEMBANGAN MUTAKHIR Zanzawi Soejoeti
Jurnal Matematika Sains dan Teknologi Vol. 4 No. 1 (2003)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (539.88 KB) | DOI: 10.33830/jmst.v4i1.671.2003

Abstract

This paper is reviewing the development of life testing data analysis of lie testing conducted in normal stress condition; stronger than normal stress (or constant stress accelerated life testing); and step stress accelerated life testing. It used exponential life test distribution model, log-normal accelerated model and tempered failure rate step stress model. It is completed by describing several problem for the future research.
Statistical Inference for Modeling Neural Network in Multivariate Time Series Dhoriva Urwatul Wutsqa; Subanar Subanar; Suryo Guritno; Zanzawi Soejoeti
Jurnal ILMU DASAR Vol 9 No 1 (2008)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (136.781 KB)

Abstract

We present a statistical procedure based on hypothesis test to build neural networks model in multivariate time series case. The method involved strategies for specifying the number of hidden units and the input variables in the model using inference of R2 increment. We draw on forward approach starting from empty model to gain the optimal neural networks model. The empirical study was employed relied on simulation data to examine the effectiveness of inference procedure. The result showed that the statistical inference could be applied successfully for modeling neural networks in multivariate time series analysis.
Co-Authors Dhoriva Urwatul Wutsqa subanar subanar Suryo Guritno