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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
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STATISTIKA INFERENSI MENGGUNAKAN RPLUGIN.SPSS Dedi Rosadi, Hendra Perdana,
SEMIRATA 2015 Prosiding Bidang Matematika
Publisher : SEMIRATA 2015

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.658 KB)

Abstract

Tujuan utama dari statistik inferensial adalah untuk membuat kesimpulan tentang populasi berdasarkan sampel dari data populasi tersebut. Salah satu yang paling umum digunakan pada teknik inferensi adalah pengujian hipotesis. Hipotesis statistika adalah keyakinan tentang parameter populasi seperti rata-rata, proporsi atau varians. Dengan kata lain, kita memiliki keyakinan tentang parameter populasi dan untuk mengkonfirmasi keyakinan tersebut, kita menjalankan tes hipotesis untuk melihat apakah keyakinan ini benar atau tidak. Dalam tulisan ini, akan dikenalkan penggunaan R untuk statistika inferensi. Sebagai ilustrasi, kita akan menggunakan R untuk uji t-independen menggunakan versi R-GUI (Graphical User Interface). R-GUI yang digunakan adalah bagian daripaket Rplugin. SPSS yang saat ini sedang dalam tahap pengembangan, dengan tampilan daftar menu mengikuti format menu yang tersedia pada SPSS. Kata kunci: R Commander plug-ins, Statistika Inferensi, Open Source, SPSS
THE COVARIATION FUNCTION FOR SYMMETRIC Α-STABLE RANDOM VARIABLES WITH FINITE FIRST MOMENTS Rosadi, Dedi
Journal of the Indonesian Mathematical Society Volume 15 Number 1 (April 2009)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.15.1.39.1-12

Abstract

In this paper, we discuss a generalized dependence measure which is designed to measure dependence of two symmetric alpha;-stable random variables with finite mean(1alpha;=2) and contains the covariance function as the special case (when alpha;=2). Weshortly discuss some basic properties of the function and consider several methods to estimate the function and further investigate the numerical properties of the estimatorusing the simulated data. We show how to apply this function to measure dependence of some stock returns on the composite index LQ45 in Indonesia Stock Exchange.DOI : http://dx.doi.org/10.22342/jims.15.1.39.1-12
Optimisasi Robust Melalui Second Order Cone Programming dengan Aplikasi pada Penentuan Portofolio Optimal Supandi, Epha Diana; Rosadi, Dedi; Abdurakhman, Abdurakhman
Jurnal Matematika dan Sains Vol 19 No 3 (2014)
Publisher : Institut Teknologi Bandung

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Abstract

Pada makalah ini, kami meneliti mengenai optimisasi robust (robust optimization), metode ini berguna untuk menangani  masalah optimisasi dimana data permasalahan tidak diketahui dengan pasti tetapi diasumsikan berada dalam suatu himpunan ketidakpastian (uncertainty set). Selanjutnya Second Order Cone Programming (SOCP) digunakan untuk menyelesaikan masalah  optimisasi robust.  SOCP adalah masalah pemrograman konveks dimana fungsi tujuannya berbentuk linear dengan kendala second order cone. Penerapan SOCP pada pembentukan masalah portofolio mean variance berhasil dilakukan. Berdasarkan studi kasus, portofolio robust melalui SOCP lebih unggul dibandingkan portofolio klasik ditinjau dari capital gain. Kata Kunci : Optimisasi robust, Second order cone programming, Portofolio mean-variance.   Robust Optimization Through Second Order Cone Programming with Applications on the Establishment of Optimal Portfolio   Abstract In this paper, we studied about robust optimization, this method is useful for dealing with optimization problems where data are not known certainly but assumed belong to uncertainty set. Furthermore, Second Order Cone Programming (SOCP) is used to solve the robust optimization problems.  SOCP is a convex programming problem where the objective function in the form of linear with constraints in the form of second order cone. Application of SOCP in the formation of mean variance portfolio problem successfully conducted. Based on case studies,  robust portfolios through SOCP are superior compared to classical portfolios in terms of capital gain. Keywords: Robust optimization, Second order cone programming, Mean variance portfolio.
PEMODELAN KURVA IMBAL HASIL DAN KOMPUTASINYA DENGAN PAKET SOFTWARE RCMDRPLUGIN.ECONOMETRICS Rosadi, Dedi
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 (238.617 KB) | DOI: 10.14710/medstat.4.1.47-55

Abstract

In this paper discussed the yield curve modeling methodology using the Nelson-Siegel model Svenson (Svensson, 1994) with special application to model the Indonesian Government Securities Yield Curve. The focus of this study is the computation of the yield curve model using the R, especially using a tool called the R-GUI RcmdrPlugin.Econometrics (Rosadi, 2011). For the empirical illustration, also given examples of applications using real data from the Indonesian capital market.   Keywords: Kurva yield, R-GUI, Nelson-Siegel-Svenson
COPULA MODELING TO IDENTIFY THE DEPENDENCY STRUCTURE OF AGRICULTURAL PRODUCTION AND ITS ENVIRONMENT INDICATORS IN INDONESIA Ahdika, Atina; Rosadi, Dedi; Effendie, Adhitya Ronnie; Gunardi, Gunardi
International Journal of Supply Chain Management Vol 7, No 4 (2018): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.803 KB)

Abstract

Agriculture is a very potential field developed in agrarian countries such as Indonesia. The country has abundant natural wealth as a food source for plants. In addition, the natural condition also has an important role on the quality and quantity of agricultural products. This study aims to model dependency structure of rice production and its environment indicators, in this case, includes temperature change, CO2 emission, and rainfall precipitation, in Indonesia using copula model. We identify the linearity of correlation between variables by comparing Pearson correlation with normality assumption and dependency structure modeled by copula function with any marginal distribution. We analyze and discuss how copula model shows the dependency between variables which cannot be identified by linear correlation.
Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia Zuhairoh, Faihatuz; Rosadi, Dedi
Indonesian Journal of Science and Technology Vol 5, No 3 (2020): IJOST: VOLUME 5, ISSUE 3, 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v5i3.26139

Abstract

This paper discussed Real-time Forecasting of the COVID-19 Epidemic using daily cumulative cases of COVID-19 in South Sulawesi. Our aim is to make model for the growth of COVID-19 cases in South Sulawesi in the top 5 provinces with the largest COVID-19 cases in Indonesia and predict when this pandemic reaches the peak of spread and when it ends. This paper used the Richards model, which is an extension of a simple logistic growth model with additional scaling parameters. Data used in the paper as of June 24, 2020 were taken from the official website of the Indonesian government. Our results are that the maximum cumulative number of COVID-19 cases has reached 10,000 to 12,000 cases. The peak of the pandemic is estimated to occur from June to July 2020 while continuing to impose social restrictions. The condition in South Sulawesi shows a sloping curve around October 2020, which means that there are still additional cases but not significant. When entering November, the curve starts to flat which indicates the addition of very small cases until the pandemic ends. The results of the pandemic peak prediction are the same as the Indonesian data; what is different is the prediction of when the pandemic will end. In the best-case scenario, the current data will tend to slow down, with the COVID-19 pandemic in South Sulawesi expected to end in November 2020. Our modeling procedure can provide information about the ongoing COVID-19 pandemic in South Sulawesi that may facilitate real-time public health responses about future disease outbreaks.
SUSCEPTIBLE INFECTED RECOVERED (SIR) MODEL FOR ESTIMATING COVID-19 REPRODUCTION NUMBER IN EAST KALIMANTAN AND SAMARINDA Sifriyani, Sifriyani; Rosadi, Dedi
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.13.2.170-181

Abstract

Modeling and analysis of Covid-19 data, especially on the modeling the spread and the prediction of the total number of cases for Indonesian data, has been conducted by several researchers. However, to the best of our knowledge, it has not been studied specifically for East Kalimantan Province data. The study of the data on the level of provincial and District/City level could help the government in making policies. In this study, we estimate the Covid-19 reproduction number, calculate the rate of recovery, the rate of infection, and the rate of death of East Kalimantan Province and Samarinda City. We also provide a prediction of the peak of the infection cases and forecast the total incidence of Covid-19 cases until the end of 2020. The model used in this research is the Susceptible Infected Recovered (SIR) model and the data used in the study was obtained from the East Kalimantan Public Health Office.
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.
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.
Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Adhitya Ronnie Effendie, Adhitya Ronnie Adi Marhadi Asrirawan Asuri, Afriza Baehaqi Budi Nurani Ruchjana Danardono Danardono Danardono, Danardono Dara Puspita Anggraeni Deasy Arisanty Dede Maryana Devni Prima Sari Di Asih I Maruddani Edi Faizal Epha Diana Supandi Ezra Putranda Setiawan Ezra Putranda Setiawan Faihatuz Zuhairoh Fajri Farid Firmansyah, Hilmi Gubu, La Gumgum Darmawan Gumgum Darmawan Hendra Perdana Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah, Hermansah Herni Utami Herni Utami Iman Sanjaya Indradewa, Rhian 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 Ulfasari Rafflesia