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All Journal Jurnal Teknik Industri Jurnal Natur Indonesia Jurnal Buana Informatika Jurnal Widya Manajemen & Akuntansi Journal of the Indonesian Mathematical Society Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi
Suryo Guritno
Program Pasca Sarjana Fak. Kedokteran Univ. Gajah Mada.
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : Jurnal Natur Indonesia

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Asimtotik Model Multivariate Adaptive Regression Spline Otok, Bambang Widjanarko; Guritno, Suryo; Subanar, Subanar
Jurnal Natur Indonesia Vol 10, No 2 (2008)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (153.379 KB) | DOI: 10.31258/jnat.10.2.112-119

Abstract

Parameter estimation in MARS model executed by minimizing penalized least-squarer (PLS). Through somerequirement, asymtotic estimator characteristic from MARS prediction model has been successfully proven. Theresearch result shows that GCV can work properly to determine the best model that applied on MARS model. Solar’s vehicles produce opacity that exceed the standard limit of emition quality which was adjusted in Kepmen LH No.35 Year 1993, as large as 88 percent from 408 percent. Applying years, cylinder volume, type of machine, andvehicle’s radius are the variables that influences the opacity.
Statistical Significance Test for Neural Network Classification Rezeki, Sri; Subanar, Subanar; Guritno, Suryo
Jurnal Natur Indonesia Vol 11, No 1 (2008)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (102.606 KB) | DOI: 10.31258/jnat.11.1.64-69

Abstract

Model selection in neural networks can be guided by statistical procedures, such as hypothesis tests, informationcriteria and cross validation. Taking a statistical perspective is especially important for nonparametric models likeneural networks, because the reason for applying them is the lack of knowledge about an adequate functionalform. Many researchers have developed model selection strategies for neural networks which are based onstatistical concepts. In this paper, we focused on the model evaluation by implementing statistical significancetest. We used Wald-test to evaluate the relevance of parameters in the networks for classification problem.Parameters with no significance influence on any of the network outputs have to be removed. In general, theresults show that Wald-test work properly to determine significance of each weight from the selected model. Anempirical study by using Iris data yields all parameters in the network are significance, except bias at the firstoutput neuron.
Co-Authors Abdurakhman . Ali Ghufron Mukti Bambang Widjanarko Otok Fredi Suryadi I Nyoman Budiantara Muhammad Syaukani Sri Haryatmi Sri Rezeki Subanar . Subanar, . Yun Astuti Nugroho Zanzawi S.