Article Per Year (5 Year)
p-Index From 2020 - 2025
1.152
P-Index
This Author published in this journals
All Journal MES: Journal of Mathematics Education and Science FARABI: Jurnal Matematika dan Pendidikan Matematika Jurnal Terapis Gigi dan Mulut Journal of Mathematics, Computation and Statistics (JMATHCOS) Emerging Statistics and Data Science Journal
Yanti, Maulida
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Mathematics Medicine & Pharmacology

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
Kajian Metode Simulasi Monte Carlo Br Manik, Mawar Bonita; Nasution, Putri Khairiah; Suyanto, Suyanto; Yanti, Maulida
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.2994

Abstract

The Monte Carlo Simulation Method is one of the forecasting methods that uses random numbers, specifically through the use of a Linear Congruential Generator and mathematical equations for prediction, forecasting, estimation, and risk analysis. The Monte Carlo Simulation Method with one iteration has a high level of accuracy, as evidenced by previous research. The more iterations used, the more accurate the forecasting results. Therefore, the author is interested in examining how well the Monte Carlo Simulation Method with N iterations performs in forecasting. The study of the Monte Carlo Simulation Method with N iterations will be conducted on the forecast of the number of visitors to Fort Rotterdam. The aim of this research is to determine the accuracy of the Monte Carlo Simulation Method with N iterations for forecasting the number of visitors to Fort Rotterdam. The MAPE values from 2013 to 2018 using the Monte Carlo Simulation Method with N iterations sequentially are 16%, 13%, 13%, 12%, 1008%, and 31%. The forecasting ability from 2013 to 2016 falls into the good category, the forecasting for 2017 falls into the poor category, and the forecasting for 2018 falls into the fair category.
INVESTMENT GOLD DURING THE COVID-19 PANDEMIC WITH LINEAR REGRESSION, NONLINEAR REGRESSION AND ARIMA Tarigan, Enita Dewi; Yanti, Maulida; Hasibuan, Citra Dewi; Siringoringo, Yan Batara Putra; Erwin, Erwin
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6832

Abstract

As a result of the COVID-19 pandemic, many individuals have turned to low-risk investment options. Common choices include gold, stocks, deposits, and foreign currencies, with gold emerging as a particularly popular investment. This study aims to forecast gold prices using linear regression, nonlinear regression, and ARIMA models, with the most accurate model determined by the lowest Mean Absolute Percentage Error (MAPE). Gold price data was sourced from www.kitco.com. The MAPE for the Linear Regression model was 4.362, the Nonlinear Regression model 3.3428, and the Time Series (ARIMA) model 2.727. Consequently, the ARIMA model demonstrated superior accuracy in forecasting gold prices compared to the Linear and Nonlinear Regression models.
Co-Authors Br Manik, Mawar Bonita Erwin Erwin Feren, Feren Hasibuan, Citra Dewi Mardiningsih Mardiningsih Nasution, Putri Khairiah Saib Suwilo Said, Fahmi Simamora, Dea Melinda Simanjorang, Susan Patricia Siringoringo, Yan Batara Putra Siti Salamah Suyanto Suyanto Syahputra, Muhammad Romi Syahriol Sitorus Tarigan, Enita Dewi