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All Journal Journal of Information Systems Engineering and Business Intelligence BAREKENG: Jurnal Ilmu Matematika dan Terapan Engineering, Mathematics and Computer Science Journal (EMACS)
Pasaribu, Asysta Amalia
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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Simulation Techniques in Sugarcane Transportation Model Using R Programming Language Yudistira, I Gusti Agung Anom; Pasaribu, Asysta Amalia; Aryusmar, Aryusmar
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12344

Abstract

The R programming language is generally known for its strength in Monte Carlo simulations, and numerical computing. This study will try to utilize R for discrete event simulations, namely in transportation systems, especially sugarcane transportation. The purpose of this paper is to study the performance of the sugarcane transportation system from the plantation to the factory, by utilizing the R programming language. The things that will be studied are to obtain changes in the system parameters so that more optimal performance is obtained. These parameters include the time required for the sugarcane to be in the transportation system, the length of the sugarcane pile in the plantation before being transported, the amount of resources needed for all transportation activities (loading, transporting and unloading), the number of transport equipment, loading equipment and unloading equipment needed, so that the harvest target is met and the waiting time for the sugarcane to be milled is as minimal as possible. As well as the level of utility of all resources provided in the system. The stages in this study include 1) literature review, 2) describing the sugarcane transportation system, 3) building assumptions and system constraints, 4) designing the transportation system conceptually, 5) developing programming code, 6) model testing/verification, 7) model validation, and 8) conducting experiments on the model. The results of the analysis of the model output indicate that the “open source R” programming language can be effectively applied to model the sugarcane transportation system.
Research on The Empirical Analysis of Bitcoin and Gasoline Return Pasaribu, Asysta Amalia
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12042

Abstract

Investment is an activity that is popular nowadays. Profitable investments are the hope of every investor. By investing. investors expect the invested assets to generate returns and to obtain profits for future life In investment studies. the most frequently discussed topic is the fluctuations. whether increases or decreases. of an asset's price (stocks). The risk of investment is loss in financial. The fluctuations of stock prices represent risks in the investment field. One measure used to determine gains and losses from stock prices is the return. To know return from data. we may use the compound return formula. Returns have empirical facts that require several tests. In this study. the empirical facts of returns are that the returns are not autocorrelated (autocorrelation function) and that the returns are leptokurtic distributed (thick-tailed distribution). We use the price data of Bitcoin (BTC) and Gasoline (UGA) from January 1. 2019. to December 31. 2023. The main of purpose of this research is to show empirical analysis of the Bitcoin and Gasoline return data. The results of the empirical analysis show that the return of stock price for Bitcoin (BTC) and Gasoline (UGA) meet the empirical properties of returns so that they can capture a good volatility model.
MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL Pasaribu, Asysta Amalia; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2069-2082

Abstract

Aggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distribution properties. However, not all distributions have joint distribution properties; the joint distributions may be unknown, so motivating the use of the Copulas in this study is needed. Sometimes, the Copula model is introduced to construct joint distribution properties. The Copula model in this research is used in financial policies such as investment. In the investment sector, the aggregate risk comes from the sum of the single risks and returns. The model used in aggregate return is the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. The data used in this study is the closing price data for Apple and Microsoft stocks from January 01, 2010, to January 01, 2024. The best model selection is the model with the GARCH-Bivariate Normal approach with the smallest MSE value. Model GARCH(1,1)-Bivariate Normal is the best model for the volatility model of aggregate return.
COMPARISON OF THE VOLATILITY OF GARCH FAMILY MODEL IN THE CRYPTOCURRENCY MARKET: SYMMETRY VERSUS ASYMMETRY Pasaribu, Asysta Amalia; Sa'adah, Aminatus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2571-2582

Abstract

Cryptocurrencies can be considered an individual asset class due to their distinct risk/return characteristics and low correlation with other asset classes. Volatility is an important measure in financial markets, risk management, and making investment decisions. Different volatility models are beneficial tools to use for various volatility models. The purpose of this study is to compare the accuracy of various volatility models, including GARCH, EGARCH, and GJR-GARCH. This study applies these volatility models to the Bitcoin, Ethereum, and Litecoin return data in the period January 1st, 2020, to December 31st, 2024. The performance of these models is based on the smallest AIC value for each model. The results of the study indicate that the GARCH (1,1) is the most suitable model for Bitcoin, Litecoin, and Ethereum returns.
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values Sa'adah, Aminatus; Sasmito, Ayomi; Pasaribu, Asysta Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.290-301

Abstract

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates. Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases. Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%. Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic.   Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.  
Co-Authors Anang Kurnia Aryusmar, Aryusmar I Gusti Agung Anom Yudistira Sa'adah, Aminatus Sasmito, Ayomi