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EIGEN MATHEMATICS JOURNAL
Published by Universitas Mataram
ISSN : 26153599     EISSN : 26153270     DOI : -
Core Subject : Education,
Mathematics
Eigen Mathematics Journal mempublikasikan artikel yang berkontribusi pada informasi baru atau pengetahuan baru terkait Matematika, Statistika, dan Aplikasinya. Selain itu, jurnal ini juga mempublikasikan artikel berbentuk survey dalam rangka memperkenalkan perkembangan terbaru dan memotivasi penelitian selanjutnya dalam bidang matematika, statistika, dan aplikasinya.
Arjuna Subject : -
Articles 124 Documents
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Analisis Optimasi Hiperparameter Bayesian untuk Model Prediksi Kinerja Inovasi Berkelanjutan Puspita, Tika
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.v8i2.266

Abstract

This study examines how well the Gaussian Process Regression (GPR) model performs in interpreting the optimization outcomes achieved through Bayesian Optimization (BO) with Keras Tuner, specifically in the context of Sustainable Innovation Performance (SIP). The GPR surrogate model serves to examine the outcomes of optimization and offers valuable insights into the strategies of exploration and exploitation while seeking the most effective hyperparameters. The evaluation of the effectiveness of GPR involved calculating the Mean Absolute Error (MAE), which was bootstrapped 1000 times to establish a 95\%. Confidence Interval (CI). This study's findings demonstrate the dependability of GPR in forecasting the validation loss generated by BO, characterized by minimal prediction errors and consistent confidence intervals. The results indicate that GPR serves as a dependable statistical method for assessing uncertainty in Bayesian-based optimization. Additionally, they offer valuable perspectives on how exploration and exploitation strategies can be utilized to attain optimal hyperparameter configurations. By strategically balancing exploitation and exploration, Bayesian Optimization can enhance the process of identifying optimal hyperparameter configurations within continuous innovation prediction models.
Prediksi Curah Hujan di Provinsi Lampung Menggunakan Distribusi Tweedie Campuran dengan Reduksi PCA Utami, Sari; Hayati, Ma’rufah; Permatasari, Reni
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.v8i2.280

Abstract

Accurate rainfall prediction is crucial for supporting the agricultural sector in Lampung Province. This research employs the Exponential Dispersion Model (EDM), a special case of the Generalized Linear Model (GLM), incorporating a Tweedie mixture distribution with Principal Component Analysis (PCA) to reduce correlated variables. Rainfall data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) through twelve rain observation posts (2013-2022), and supplemented with precipitation data from the General Circulation Model (GCM) obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). The Tweedie mixture distribution was selected for its ability to handle non-normally distributed rainfall data containing zero values. The results show that the Root Mean Square Error of Prediction (RMSEP) for the Tweedie mixture-PCA model at the Gisting Atas station is 163.90, while the Normal-PCA model achieved 169.11. Therefore, the Tweedie mixture-PCA approach is more effective and recommended for improving rainfall prediction in Lampung Province, offering potential benefits for agricultural planning and resource management.
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.
Penerapan Kendali Optimal pada Model Matematika SEITRS Penularan Tuberkulosis dengan Variabel Kendali Sosialisasi dan Terapi Rafiq, Muhammad; Wigantono, Sri; Raming, Indriasri
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.v8i2.322

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, which remains a serious public health concern. The objective of this study is to develop, analyze, and propose an optimal control strategy for the transmission dynamics of TB using an SEITRS mathematical model. The model consists of five population compartments: Susceptible (S), Exposed (E), Infected (I), Treatment (T), and Recovered (R). The methodology involves constructing the SEITRS model, determining the equilibrium points, and analyzing their stability under different conditions of the basic reproduction number. The model has two equilibrium points, namely the non-endemic and endemic equilibrium. If the basic reproduction number is less than one and certain conditions are satisfied, the non-endemic equilibrium is locally asymptotically stable. Conversely, if the basic reproduction number is greater than one and specific conditions are met, the endemic equilibrium becomes locally asymptotically stable. Furthermore, this study provides optimal control strategies in the SEITRS model. We use two control variables in this model, namely socialization and therapy, to reduce the number of infected individuals. The sufficient conditions for the existence of optimal controls are derived using Pontryagin’s Maximum Principle. Numerical simulations are then conducted to examine the impact of applying these controls on the system. The simulation results indicate that the simultaneous implementation of socialization and therapy controls is effective in reducing the number of TB-infected individuals.

Page 13 of 13 | Total Record : 124

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