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All Journal Media Statistika Statistika Historia: Jurnal Pendidik dan Peneliti Sejarah Paramita: Historical Studies Journal Jurnal Matematika & Sains Jurnal Sains Matematika dan Statistika Jurnal Sejarah dan Budaya Indonesian Journal of Artificial Intelligence and Data Mining Islamic Management: Jurnal Manajemen Pendidikan Islam FIKRAH Jurnal Aplikasi Statistika & Komputasi Statistik Fajar Historia: Jurnal Ilmu Sejarah dan Pendidikan Diakronika Handep: Jurnal Sejarah dan Budaya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business West Science Interdisciplinary Studies International Review of Humanities Studies West Science Interdisciplinary Studies Jurnal Sejarah Indonesia Fikrah : Jurnal Pendidikan Agama Islam
Abdurakhman Abdurakhman
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Journal : Media Statistika

 1 
SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING Hermansah, Hermansah; Rosadi, Dedi; Abdurakhman, Abdurakhman; Utami, Herni
MEDIA STATISTIKA Vol 13, No 2 (2020): 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.13.2.116-124

Abstract

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
PEMILIHAN PORTFOLIO ROBUST DENGAN KLROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS ASTERING BERDASARKAN SEKTOR USAHA SAHAM Gubu, La; Rosadi, Dedi; Abdurakhman, Abdurakhman
MEDIA STATISTIKA Vol 14, No 1 (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.14.1.33-43

Abstract

In recent years there have been numerous studies on portfolio selection using cluster analysis in conjunction with Markowitz model which used mean vectors and covariance matrix that are estimated from a highly volatile data. This study presents a more robust way of portfolio selection where stocks are grouped into clusters based on business sector of stocks. A representative from each cluster is selected from each cluster using Sharpe ratio to construct a portfolio and then optimized using robust FCMD and S-estimation. Calculation Sharpe ratio showed that this method works efficiently on large number of data while also robust against outlier in comparison to k-mean clustering. Implementation of this method on stocks listed on the Indonesia Stock Exchange, which included in the LQ-45 indexed for the period of August 2017 to July 2018 showed that portfolio performance obtained using clustering base on business sector of stocks combine with robust FMCD estimation is outperformed the other possible combination of the methods.
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.
CREDIT SPREADS PADA REDUCED-FORM MODEL Di Asih I Maruddani; Dedi Rosadi; Gunardi Gunardi; Abdurakhman Abdurakhman
MEDIA STATISTIKA Vol 4, No 1 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (319.946 KB) | DOI: 10.14710/medstat.4.1.57-63

Abstract

There are two primary types of models in the literature that attempt to describe default processes for debt obligations and other defaultable financial instruments, usually referred to as structural and reduced-form (or intensity) models. Structural models use the evolution of firms’ structural variables, such as asset and debt values, to determine the time of default. Reduced form models do not consider the relation between default and firm value in an explicit manner. Reduced form models assume that the modeler has the same information set as the market - incomplete knowledge of the firm’s condition. that leads to an inaccessible default time. The key distinction between structural and reduced form models is not whether the default time is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology. Credit spreads are used to measure credit premium, which compensates risk-averse investors for assuming credit risk. Therefore, the credit spreads should remain positive. The higher credit risk assumed by the investors, the higher credit premium got be payed by them. In this paper, we have to to determine the credit spreads of reduced-form model.   Keywords: Reduced-Form Model, Hazard Rate, Credit Spreads  
ANALYSIS OF MULTILEVEL STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS METHOD Amanah, Fitri; Abdurakhman, Abdurakhman
MEDIA STATISTIKA Vol 17, No 1 (2024): 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.17.1.81-92

Abstract

Generalized Structured Component Analysis (GSCA) is a component-based SEM. One of the developments of GSCA is the GSCA method for multilevel data known as multilevel GSCA. Multilevel data is data that has a nested, grouped, or nested structure. This study aims to apply multilevel GSCA to the data on factors that affect poverty. The data used is on Indonesia's health, education and poverty in 2023.. The result is that all indicators are significant to the latent variables. The structural model shows that the quality of health has a negative and significant effect on poverty, education has a negative and significant effect on poverty, and the quality of health has a positive and significant effect on education. The results of between group show that health quality has a positive and significant effect on education in all regions, health quality has a negative and significant effect on poverty in Bali & Nusa Tenggara, Sulawesi, as well as Maluku and Papua, education has a negative and significant effect on poverty in Sumatra, Java, and Maluku & Papua. The overall goodness of fit value (FIT) is 0.622, meaning the model can explain 62.2% of data variation.
Co-Authors Abd Rahman Abdul Hoyyi Akmal Akmal Andarir, Andarir Arafah, A Latif Arung Dedi Rosadi Di Asih I Maruddani Di Asih I Maruddani Dwi Mulyatari Efferia, Vallen Emy Siswanah Epha Diana Supandi Febbrizal, Febbrizal Fitri Amanah Gubu, La Haghia, Raisye Soleh Hermansah Hermansah, Hermansah Herni Utami Heru Susetyo Hilimi, Nurindah Indah Manfaati Nur Indi Febriana Suhriani Indradewa, Rhian Ira Apriyanti Israr, Israr Kurohman, M Taofik La Gubu Linda Sunarti Loliyana, Nilan M. Nasor, M. Mahabah, Intan Nasirin, Anas Anwar Nugroho, Tito Tri Nur Hidayah Prihatini, Farida Rahmadhani, Suri Rahman, Syahidah Sumayyah Samosir, Jelita Santosa, Ayi Budi Sihotang, Geisha Siswantari Siswantari, Siswantari Siti Patimah Soraya Soraya Sugiarto, Gabriela Tangkilisan, Yuda B Warisno, A wildan Insan Fauzi Yeni Kurniawati, Yeni Yon Machmudi Zuhdi, Susanto