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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Industri MATEMATIKA Jurnal Ilmu Dasar Jurnal Natur Indonesia Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Journal of the Indonesian Mathematical Society International Journal of Advances in Intelligent Informatics PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Ilmiah Matrik Jurnal Statistika dan Matematika (Statmat)
Subanar .
Department Of Mathematics, Universitas Gadjah Mada
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MODEL REPRESENTASI INFORMASI DAN PENGETAHUAN UNTUK PROYEK-PROYEK PERUSAHAAN DENGAN MENGGUNAKAN SEMANTIK ONTOLOGI Azhari, Azhari; Subanar, Subanar; Wardoyo, Retantyo; Hartati, Sri
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 2, Juli 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.632 KB) | DOI: 10.12962/j24068535.v7i2.a178

Abstract

This paper presents the utilization of knowledge management system for information and knowledge model development of enterprise projects. This information and knowledge management model is based on ontology semantic data model. The ontology data model is new technique for representing information and knowledge base on more semantically conception of meanings of objects, their properties, and relations between them that may arise within certain domain knowledge. The concern of the knowledge management model is to ensure that the model allows the process of creation, access, and utilization of data in a semantically manner (for querying process) and information or knowledge of enterprise projects. The experimentation shows that project ontology model has satisfied all consistent, valid, complete, and correct ontology model criteria and can be used for semantic reasoning computation. A prototype of the proposed model can access information and knowledge from the knowledge ontology model.   Kata Kunci: knowledge management system, semantic data model, ontology model, semantiq query, enterprise projects
NON AUTOMATICALLY EXERCISED (NAE) EUROPEAN CAPPED CALL PRICING THEORY ., Subanar; Guritno, Suryo; S., Zanzawi; ., Abdurakhman
Journal of the Indonesian Mathematical Society Volume 13 Number 2 (October 2007)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.13.2.69.215-221

Abstract

The objective of this paper is to present a methodology for deriving Black Scholes formulae via a simple lognormal distribution approach and introduce European capped non automatically exercise (NAE) call option pricing theory. DOI : http://dx.doi.org/10.22342/jims.13.2.69.215-221
SOME COMMENTS ON THE THEOREM PROVIDING STATIONARITY CONDITION FOR GSTAR MODELS IN THE PAPER BY BOROVKOVA et al. ., Suhartono; ., Subanar
Journal of the Indonesian Mathematical Society Volume 13 Number 1 (April 2007)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.13.1.90.115-122

Abstract

Generalized Space-Time Autoregressive (GSTAR) model is one of the models that usually used for modeling and forecasting space and time series data. The aim of this paper is to study further about the stationarity conditions for parameters in the GSTAR model and the relation to Vector Autoregressive (VAR) model. We focus on the theoretical study about stationarity condition in GSTAR(11) and the relation tothe stationarity condition of parameters in VAR(1). Then, we do an empirical study to give counter examples for the theorem of stationarity condition proposed by Borovkovaet al. The results show that the theorem of stationarity condition of parameters in GSTAR(11) model given by Borovkova et al. is incorrect. Additionally, the empirical results also show that GSTAR(11) model could always be represented in VAR(1) model by applying matrix operation to the space and time parameters. Hence, we can also conclude that VAR model, particularly VAR(1), is an extension of GSTAR(11) model with any possibility values of space and time parameters.DOI : http://dx.doi.org/10.22342/jims.13.1.90.115-122
PENENTUAN RUTE PENGAMBILAN SAMPAH DI KOTA MERAUKE DENGAN METODE SAVING HEURISTIC Perwitasari, Endah Wulan; Subanar, Subanar
JURNAL ILMIAH MATRIK MATRIK Vol.15 No.2 Agustus 2013
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.789 KB)

Abstract

Waste distribution problem has the common characteristics of the poor of scheduling and poor establishing route of waste. The waste distribution problems cover several issues such as the selection the route for the vehicle and the minimizing the distribution cost. The waste collection route is modeled into Vehicle Routing Problem (VRP). VRP is the selection of which route used by the dump trucks. The purpose of VRP is to minimize the time, distance, and distribution cost. There are two methods to deal with the VRP problems, which are the exact and heuristic methods. The exact method aimed to the optimum result, whereas heuristic method put emphasis on near-to-optimum but with quicker computing time. The result obtained by this research is the combination between exact and heuristic method. This combination is successfully implemented and it is able to determine which route to fulfill the problems of waste distribution. Keywords: Waste Collection Route, Algorithm, VRP, and Saving Heustic
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN KURS DOLAR DAN INDEKS HARGA SAHAM GABUNGAN (IHSG) Adnyani, Luh Putu Widya; Subanar, Subanar
PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Vol 4, No 1 (2015): PYTHAGORAS
Publisher : UNIVERSITAS RIAU KEPULAUAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (768.465 KB) | DOI: 10.33373/pythagoras.v4i1.576

Abstract

Abstrak General Regression Neural Network (GRNN) merupakan salah satu metode yang dikembangkan dari konsep jaringan syaraf tiruan yang dapat digunakan untuk peramalan.  Metode ini diaplikasikan untuk memprediksi data time series yang memiliki hubungan kausal dimana metode peramalan yang digunakan sebelumnya (ARIMA BOX - Jenkins) tidak mampu menjelaskan adanya keterkaitan data.Penelitian ini dilakukan dengan mengambil data kurs dollar dan IHSG.  Dengan menggunakan metode GRNN diperoleh suatu prediksi nilai IHSG beberapa periode kedepan.  Keunggulan penggunaan metode ini yaitu lebih cepat dari segi perhitungan dan tidak memerlukan adanya suatu asumsi data.   Metode GRNN menghasilkan nilai prediksi yang lebih akurat dibandingkan dengan metode ARIMA.  Hal itu ditunjukkan dari nilai MSE yang lebih kecil dari metode ARIMA.Kata Kunci: GRNN, Neural Network, GRNN Time Series, GRNN Kurs dan  IHSG. Abstract General Regression Neural Network (GRNN) is one method that was developed from the concept of artificial neural network that can be used for forecasting.  This method was applied to predict the time series data that has a causal relations where the forecasting method used previously (ARIMA BOX-Jenkins)is not able to explain the presence of linkage data.This research was conducting by taking the dollar exchange rate and composite stock price index (IHSG).  By using the GRNN method will obtained the predictive value in some future period.  The advantages using this method is faster in term of computation and doesn?t requaired the presence of a data assumptions.  GRNN method produces more accurate predictive value compared with ARIMA.  It was shown that the MSE value is smaller than ARIMA.Keyword:  GRNN, Neural Network, GRNN Time Series, GRNN Dollar exchange rate and   IHSG.
Optimisasi Portofolio Mean-VaR di bawah CAPM Transformasi Koyck dengan Volatilitas Tak Konstan dan Efek Long Memory Sukono, Sukono; Subanar, Subanar; Rosadi, Dedy
Jurnal Teknik Industri Vol 12, No 2 (2010): DECEMBER 2010
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (193.527 KB) | DOI: 10.9744/jti.12.2.pp. 89-94

Abstract

In this paper we formulated mean-VaR portfolio optimization through CAPM Koyck transformation. We assumed that lagged of risk premium which have highly influence on stock returns is infinite, while model parameters decrease geometrically. We also assumed that rate of return in risk premium market index is not constant, in other word has a non-constant volatility rate, and also has a long memory effect. The later was analyzed using ARFIMA. Non constant volatility rate was modeled via GARCH model. The portfolio optimization was constructed using Langrangian multiplier and the Kuhn-Tucker theorem was employed to obtain the solution by the least square method. Finally, we provide a numerical example of the optimization model based on several stocks traded in Indonesian capital market.
THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES Subanar, Subanar; Suhartono, Suhartono
Jurnal Teknik Industri Vol 8, No 2 (2006): DECEMBER 2006
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (221.66 KB) | DOI: 10.9744/jti.8.2.pp. 156-164

Abstract

Recently, one of the central topics for the neural networks (NN) community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series.
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.
ESTIMASI PARAMETER MODEL TAHAP AWAL AR(1) REGRESI RESPON BINER LONGITUDINAL Fajriyah, Rohmatul; subanar, subanar
MATEMATIKA Vol 5, No 3 (2002): Jurnal Matematika
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (63.202 KB)

Abstract

Data yang diperoleh dari hasil pengukuran berulang pada subyek tertentu, biasanya akan berkorelasi. Pada regresi respon biner, jika digunakan model autoregressif order -, AR(1), maka diperlukan pengetahuan tentang  outcome sebelumnya, , yang tak terobservasi. Model untuk menginferensi data dengan model AR(1), diantaranya adalah model AR(1) kondisional. Pada model ini, nilai  diambil sembarang, yaitu   atau . Model di atas akan dibahas dan dibandingkan hasil estimasinya melalui studi simulasi
Co-Authors Abdurakhman . Abdurakhman . Abdurakhman Abdurakhman Adnyani, Luh Putu Widya Agus Maman Abadi Azhari Azhari Bambang Widjanarko Otok Dedy Rosadi Endah Wulan Perwitasari, Endah Wulan Herni Utami Herni Utami MHD. Reza M.I. Pulungan Muhammad Dermawan Mulyodiputro Muhammad Hisyam Lee Muhammad Hisyam Lee Paulo Canas Rodrigues Putriaji Hendikawati Retantyo Wardoyo Rohmatul Fajriyah Samsubar Saleh Seng Hansun Sri Hartati Sri Rezeki Suhartono . Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Sukono Sukono Suryo Guritno Suryo Guritno Tarno Tarno Tarno Tarno Vemmie Nastiti Lestari Widodo Widodo Winita Sulandari Winita Sulandari Winita Sulandari Wiwin Sulistyo Zanzawi S. Zanzawi S.