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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Sosiohumaniora Jurnal Ilmu Dasar Statistika Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Journal of the Indonesian Mathematical Society Jurnal Matematika & Sains Jurnal Optimasi Sistem Industri SEMIRATA 2015 International Journal of Advances in Intelligent Informatics Indonesian Journal of Science and Technology Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal TAM (Technology Acceptance Model) BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Supply Chain Management Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Islamic Monetary Economics and Finance Indonesian Journal of Electrical Engineering and Computer Science JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Prosiding Seminar Nasional Official Statistics Jurnal Matematika Integratif Literatur : Jurnal Bahasa dan Sastra Jurnal TAM (Technology Acceptance Model)
Dedi Rosadi
Department Of Mathematics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data Darmawan, Gumgum; Rosadi, Dedi; Ruchjana, Budi N
CAUCHY Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress)
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i2.14136

Abstract

Hybrid models between Singular Spectrum Analysis (SSA) and Autoregressive Integrated Moving Average (ARIMA) have been developed by several researchers. In the SSA-ARIMA hybrid model, SSA is used in the decomposition and reconstruction process, while forecasting is done through the ARIMA model. In this paper, hybrid SSA-ARIMA uses two auto grouping models. The first model, namely the Alexandrov method and the second method, is alternative auto grouping with a long memory approach. The two-hybrid models were tested for two types of seasonal patterns, multiplicative and additive seasonal time series data. The analysis results using both methods give accurate results; as seen from the MAPE generated the 12 observations for the future, the value is below 5%. The hybrid SSA-ARIMA method with Alexandrov auto grouping is more accurate for an additive seasonal pattern, but the hybrid SSA-ARIMA with alternative auto grouping is more accurate for a multiplicative seasonal pattern.
PREDICTION OF FOREST FIRE USING NEURAL NETWORKS WITH BACKPROPAGATION LEARNING AND EXREME LEARNING MACHINE APPROACH USING METEOROLOGICAL AND WEATHER INDEX VARIABLES Rosadi, Dedi; Arisanty, Deasy; Agustina, Dina
MEDIA STATISTIKA Vol 14, No 2 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.2.118-124

Abstract

Forest fire is one of important catastrophic events and have great impact on environment, infrastructure and human life. In this study, we discuss the method for prediction of the size of the forest fire using the hybrid approach between Fuzzy-C-Means clustering (FCM) and Neural Networks (NN) classification with backpropagation learning and extreme learning machine approach. For comparison purpose, we consider a similar hybrid approach, i.e., FCM with the classical Support Vector Machine (SVM) classification approach. In the empirical study, we apply the considered methods using several meteorological and Forest Weather Index (FWI) variables. We found that the best approach will be obtained using hybrid FCM-SVM for data training, where the best performance obtains for hybrid FCM-NN-backpropagation for data testing.
VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION Hoyyi, Abdul; Abdurakhman, Abdurakhman; Rosadi, Dedi
MEDIA STATISTIKA Vol 14, No 2 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.2.183-193

Abstract

The Option is widely applied in the financial sector.  The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the  process exponential model. The  process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the  process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK).  The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various  values until convergent result was obtained.
K-means and bayesian networks to determine building damage levels Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.11756

Abstract

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.
Discretization methods for Bayesian networks in the case of the earthquake Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2007

Abstract

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.
DETERMINAN PENINGKATAN PENDAPATAN ASLI DAERAH: SISTEM PERENCANAAN TERPADU Dedi Rosadi; Dede Maryana
Sosiohumaniora Vol 3, No 1 (2001): SOSIOHUMANIORA, MARET 2001
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/sosiohumaniora.v3i1.5193

Abstract

Di dalam konteks otonomi daerah, Pendapatan Asli Daerah (PAD) dari suatu propinsi, kabupaten, maupun kota merupakan salah satu ukuran kemampuan kemandirian suatu daerah otonom. Pajak Pembangunan I (PP I) salah satu sumber PAD yang paling potensial, yang besarnya 10% dari jumlah biaya atas pemakaian jasa layanan hotel dan rumah makan. Namun penerimaan dari sektor PP I ini pada setiap kabupaten di Propinsi Jawa Barat masih belum optimal. Keadaan itu diduga erat kaitannya dengan konsep perencanaan terpadu yang belum dirujuk di dalam upaya optimalisasi perolehan PAD dari sektor pajak daerah, khususnya PP I. Pertanyaan dalam studi ini: pertama, bagaimana perbedaan realisasi penerimaan PP I dibandingkan potensinya; kedua, apakah perencanaan oleh Dipenda kabupaten/kota di Jawa Barat dalam pungutan PP I telah sesuai persyaratan perencanaan terpadu; ketiga, apakah ada pengaruh dari perencanaan terpadu terhadap upaya peningkatan pendapatan asli daerah dari sektor PP I. Hasil studi ini menunjukkan bahwa perencanaan terpadu sangat bermakna dalam upaya pengoptimalan penerimaan daerah dari sektor pajak daerah. Kata Kunci : Perencanaan terpadu, pajak
Teaching descriptive statistics using R Dedi Rosadi; Khabib Mustofa; Iman Sanjaya; Hendra Perdana; Krisna Mutiara Wati
Proceedings of The Annual International Conference, Syiah Kuala University - Life Sciences & Engineering Chapter Vol 3, No 2 (2013): Engineering
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (84.797 KB)

Abstract

In this paper, we introduce the application of R for teaching statistics descriptive subject which is usually given in the undergraduate statistics course. As an illustration, we will consider the use of R for teaching the subject frequency distribution table, both using the R-CLI and R-GUI version. The R-GUIversion used here is a part of Rplugin.SPSS which is currently under our extensive development. Rplugin.SPSS is a R Commander Plugin, which is a reorganized and an extented version of the menu of Statistics in R-Commander, a SPSS-like version menu. It can be also considered as the extended version of Rplugin.Econometrics (Rosadi, 2010). Further details and further examples can be found in Rosadi (2011,2013).
Portfolio optimization based on self-organizing maps clustering and genetics algorithm Fajri Farid; Dedi Rosadi
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.587

Abstract

In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory.
Automatic time series forecasting using nonlinear autoregressive neural network model with exogenous input Hermansah Hermansah; Dedi Rosadi; Abdurakhman Abdurakhman; Herni Utami
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2862

Abstract

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm sets up the appropriate features, estimate the parameters in the model, and calculate forecasts, without the users’ intervention. The algorithm method used include preprocessing, tests for trends, and the application of first differences. The time series were tested for seasonality, and seasonal differences were obtained from a successful analysis. These series were also linearly scaled to [−1, +1]. The autoregressive lags and hidden neurons were further selected through the stepwise and optimization algorithms, respectively. The 20 NARX models were fitted with different random starting weights, and the forecasts were combined using the ensemble operator, in order to obtain the final product. This proposed method was applied to real data, and its performance was compared with several available automatic models in the literature. The forecasting accuracy was also measured by mean squared error (MSE) and mean absolute percent error (MAPE), and the results showed that the proposed method outperformed the other automatic models.
CAPM (Capital Asset Pricing Model) with Stable Distribution Dedi Rosadi
Jurnal ILMU DASAR Vol 11 No 2 (2010)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (151.258 KB)

Abstract

In the classical finance theory, the CAPM models are developed using the Gaussian framework, that is, weassume the vector of returns can be modeled using the multivariate normal distribution. However, it is foundempirically that typically the financial data, especially the returns of assets, are leptokurtic (i.e., it is heavy tail andpeaked around the center). It has been shown in the literature that the stable distribution, where the normal is of aspecial case, becoming one of the popular model to model leptokurtic data. In this paper, we analyse the CAPMunder the assumption that the data follows the stable non-normal distribution with the index ofstability1 <α < 2 . We finally provide empirical application of the CAPM under the Gaussian and stable casesusing several returns data from Indonesian Stock Market.
Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Adhitya Ronnie Effendie, Adhitya Ronnie Adi Marhadi Asrirawan Asuri, Afriza Budi Nurani Ruchjana Danardono Danardono Danardono, Danardono Dara Puspita Anggraeni Deasy Arisanty Dede Maryana Devni Prima Sari Di Asih I Maruddani Dina Agustina, Dina Edi Faizal Epha Diana Supandi Ezra Putranda Setiawan Ezra Putranda Setiawan Faihatuz Zuhairoh Fajri Farid Firmansyah, Hilmi Gubu, La Gumgum Darmawan Gumgum Darmawan Gunardi Gunardi Hendra Perdana Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah, Hermansah Herni Utami Herni Utami Iman Sanjaya Iskandar, Edi Izza Dinikal Arsy Jana, Padrul Khabib Mustofa La Gubu Malim Muhammad Media Rosha Muhammad Muhajir, Muhammad Pepi Novianti Resa Septiani Pontoh Retno Subekti Rizal, Ahmad Ashril Ruchjana, Budi N Rupilu, Rina Apriany Helen Wite Setialaksana, Wirawan - Sifriyani, Sifriyani Solly Aryza Sri Redjeki Sri Redjeki Ulfasari Rafflesia