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All Journal Journal of Saintech Transfer Journal of Research in Mathematics Trends and Technology Journal of Mathematics, Computation and Statistics (JMATHCOS)
Maulida Yanti
Department Of Mathematics, Universitas Sumatera Utara, Medan
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Mathematics Other

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Empowerment of cake SMEs in Laras Village through profit optimization Maulida Yanti; Mardiningsih; Putri Khairiah Nasution; Enita Dewi Br Tarigan
Journal of Saintech Transfer Vol. 8 No. 2 (2025): Journal of Saintech Transfer
Publisher : Talenta Publisher Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jst.v8i2.19774

Abstract

In a village, called Laras, there is a small business (SME) that produces wet and dry cookies, marketed on a small scale. The problems faced by the SMEs include the lack of a strategic and easily accessible stall for the public, as well as minimal product promotion being conducted. Several important indicators in optimizing product sales results can be achieved through packaging design, shelf availability, and effective marketing. To address these issues, the Community Service Team provided a workshop to enhance skills related to the making of packaging label and marketing techniques. The abdimas team also provided an attractive shelving unit as a place to display the cakes to facilitate sales and make them visible to the surrounding community. In the end, a paired t-test was used to measure the benefits of the service provided. Based on the questionnaire data, the t-test statistic value obtained is |-65.21| = 65.21 > t-table value 1.699, hence it can be concluded that there was an improvement outcome after the service was conducted.
Stochastic Interest Rate Models in Pension Fund Valuation: A Comparative Study of Vasicek and Cox-Ingersoll-Ross under the Aggregate Cost Method Angel Tiovanny; Maulida Yanti; Citra Dewi Hasibuan
Journal of Research in Mathematics Trends and Technology Vol. 8 No. 1 (2026): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v8i1.22753

Abstract

This research aims to implement the Vasicek and Cox-Ingersoll-Ross (CIR) interest rate models in pension fund calculations using the Aggregate Cost method. These interest rate models are utilized to manage the uncertainty of interest rates influenced by inflation, providing a more realistic projection of future pension contributions. The analyzed data includes the Indonesian Mortality Table IV and historical BI-Rate interest rate data from January 2015 to December 2024. Parameter estimation for the Vasicek and CIR models is conducted using the Maximum Likelihood Estimation (MLE) method, and long-term interest rate simulations are performed using the Milstein method. The findings indicate that the Vasicek model has a MAPE of 14.97%, while the CIR model has a MAPE of 19.06%. Pension fund calculations are conducted for participants with starting ages of 25, 30, and 35 years, revealing that the use of Vasicek and CIR interest rate models yields higher present value of pension benefit compared to a constant interest rate. Additionally, normal contributions for younger participants tend to be lower due to longer working periods. The study concludes that the Vasicek model is more accurate in estimating interest rates for pension fund calculations.
Kajian Metode Simulasi Monte Carlo Mawar Bonita Br Manik; Putri Khairiah Nasution; Suyanto Suyanto; Maulida Yanti
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 Enita Dewi Tarigan; Maulida Yanti; Citra Dewi Hasibuan; Yan Batara Putra Siringoringo; 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 Angel Tiovanny Citra Dewi Hasibuan Enita Dewi Br Tarigan Enita Dewi Tarigan Erwin Erwin Mardiningsih Mawar Bonita Br Manik Putri Khairiah Nasution Putri Khairiah Nasution Suyanto Suyanto Yan Batara Putra Siringoringo