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Journal : EIGEN MATHEMATICS JOURNAL

Mathematical Model of Differential Equations to Population Growth Models with Limited Growth in West Nusa Tenggara Province Robbaniyyah, Nuzla Af'idatur; Anjani, Mutia Dewi; Lansuna, Ni Wayan Eka; Ihwani, Ivan Luthfi
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.223

Abstract

Differential equations are often a topic in the field of mathematics which has many applications in mathematical modeling, one of which is population growth. Research on population growth is of course important for an area because the results  of this research can be used in issuing policies such as maintaining the availability of agricultural land, places to live, and many others. In this study, the mathematical model of differential equations was used to find a population growth model for the West Nusa Tenggara Province, then the model was verified and calculations were carried    out using the Mathematica software. Then a model is generated with the equation (?) = 3504006 ?0,012(?−1993) which results in a calculation that the population of NTB will continue to grow so that it is necessary to verify the model which produces a logistics growth model.
Implementation of Fast Fourier Transform and Least Mean Square Algorithms in The Denoising Process of Audio Signal Rayes, Putri Rahmasari; Robbaniyyah, Nuzla Af'idatur; Bahri, Syamsul
Eigen Mathematics Journal Vol 8 No 1 (2025): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i1.255

Abstract

Audio signals play an important role as a medium for storing information, such as lecture materials, interview results, and other archives. However, audio signals are often contaminated by noise, which is unwanted interference that can affect their quality. Therefore, a denoising process is needed to reduce or eliminate noise components in the signal. The Fast Fourier Transform (FFT) and Least Mean Square (LMS) algorithms are frequently used in the denoising process due to their simple and easy-to-implement steps. This research uses primary data, specifically audio signals recorded under two noise conditions: rain noise as Audio Signal 1 and guitar instrument noise as Audio Signal 2, both stored in WAV format. The denoising process was performed using MATLAB software and evaluated based on Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) metrics. Higher SNR values and lower MSE values indicate the success of the denoising process in improving audio signal quality. The results of this study demonstrate the effectiveness of the applied algorithms, where the SNR value reached 38.2596 dB with an MSE of 0.0000028211 for Audio Signal 1, and an SNR value of 38.6881 dB with an MSE of 0.0000014988 for Audio Signal 2. An SNR value between 25 dB and 40 dB is categorized as a very good signal, indicating that the quality of the processed audio signals falls into the very good signal category.
Solusi Numerik pada Persamaan Korteweg-De Vries Equation menggunakan Metode Beda Hingga Haizar, Maulana Rifky; Rizki, Miptahul; Robbaniyyah, Nuzla Af'idatur; Syechah, Bulqis Nebulla; Salwa, Salwa; Awalushaumi, Lailia
Eigen Mathematics Journal Vol 7 No 1 (2024): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i1.190

Abstract

The Korteweg-de Vries (KdV) equation is a nonlinear partial differential equation that has a key role in wave physics and many other disciplines. In this article, we develop numerical solutions of the KdV equation using the finite difference method with the Crank-Nicolson scheme. We explain the basic theory behind the KdV equation and the finite difference method, and outline the implementation of the Crank-Nicolson scheme in this context. We also give an overview of the space and time discretization and initial conditions used in the simulation. The results of these simulations are presented through graphical visualizations, which allow us to understand how the KdV solution evolves over time. Through analysis of the results, we explore the behavior of the solutions and perform comparisons with exact solutions in certain cases. Our conclusion summarizes our findings and discusses the advantages and limitations of the method used. We also provide suggestions for future research in this area.
Simulation of Spring Oscillations in Second-Order Differential Equations Using the Finite Difference Method Al Paqih, Muhammad Imam; Hardi, Rida Al Kausar; Robbaniyyah, Nuzla Af'idatur
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.321

Abstract

This study aims to simulate the motion of a damped spring oscillation, modeled by a second-order ordinary differential equation, using the Finite Difference Method (FDM). The main focus is on implementing the central finite difference scheme to discretize the equation, deriving an explicit iterative formula, and analyzing the oscillation dynamics and the accuracy of the numerical solution. The simulation was conducted with specific parameters (mass m = 1.0 kg, spring constant k = 10.0 N/m, damping coefficient c = 0.5 Ns/m) and various time steps (\Delta t = 0.5 s, 0.1 s, 0.01 s). The simulation results qualitatively show damped oscillatory behavior consistent with physical theory, where the amplitude decreases over time. The accuracy of the numerical solution, measured by the Symmetric Mean Absolute Percentage Error (SMAPE) against the analytical solution, was significantly influenced by \Delta t; the smallest time step (0.01 s) yielded the highest accuracy with a SMAPE of 0.4495%. The Finite Difference Method proved effective in analyzing the spring oscillation system, demonstrating that the proper selection of \Delta t is crucial for balancing accuracy and computational efficiency.