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All Journal ILKOM Jurnal Ilmiah Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Operations Research: International Conference Series Tensor: Pure and Applied Mathematics Journal InSTEM
Merysa Puspita Sari
Universitas Jember
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Physics Other

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Application of Metaheuristic Algorithm for Solving Fully Fuzzy Linear Equations System Puspita Sari, Merysa; Pradjaningsih, Agustina; Ubaidillah, Firdaus
Operations Research: International Conference Series Vol. 3 No. 3 (2022): Operations Research International Conference Series (ORICS), September 2022
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v3i3.170

Abstract

A linear equation is an equation in which each term contains a constant with a variable of degree one or single and can be described as a straight line in a Cartesian coordinate system. A Linear equations system is a collection of several linear equations. A system of linear equations whose coefficients and variables are fuzzy numbers is called a fully fuzzy linear equation system. This study aims to apply a metaheuristic algorithm to solve a system of fully fuzzy linear equations. The objective function used is the minimization objective function. At the same time, the metaheuristic algorithms used in this research are Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Cuckoo Search (CS). The input in this research is a fully fuzzy linear equation system matrix and parameters of the PSO, FA, and CS algorithms. The resulting output is the best objective function and the variable value of the fully fuzzy linear equations system. The work was compared for accuracy with the Gauss-Jordan elimination method from previous studies with the help of the Matlab programming language. The results obtained indicate that the Particle Swarm Optimization (PSO) algorithm is better at solving fully fuzzy linear equation systems than the Firefly Algorithm (FA) and Cuckoo Search (CS). This case can be seen from the value of the resulting objective function close to the value of the Gauss-Jordan elimination methodKeywords: Mathematics, investation
Optimal Control for a COVID-19 and Tuberculosis Co-Infection Model with Asymptomatic COVID-19 Carriers Rizka, Sailah Ar; Ayu, Regina Wahyudyah Sonata; Ainurrofiqoh, Dewi Ika; Sari, Merysa Puspita; Kholifia, Nadia
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.31076

Abstract

This study applies optimal control theory to a deterministic co-infection model of COVID-19 and tuberculosis (TB) with asymptomatic COVID-19 carriers, who are assumed to be less infectious. The optimal control strategy aims to minimize intervention costs and reduce infections by implementing five control measures, including prevention and vaccination of COVID-19, treatment of both symptomatic and asymptomatic COVID-19-infected individuals, treatment of COVID-19 and active TB co-infected individuals, and prevention of treatment failure in active TB cases. Pontryagin's minimum principle is used to characterize the necessary conditions for optimal control in reducing infections. Numerical results demonstrate the effectiveness of the optimal control strategy in suppressing diseases. The incremental cost-effectiveness ratio (ICER) for different combinations of control measures is evaluated, showing that the intervention strategy performs best when all control measures are used.
Batas Perturbasi Mutlak Nilai Eigen dari Matriks Normal Ainurrofiqoh, Dewi Ika; Sari, Merysa Puspita; Rizka, Sailah Ar; Kholifia, Nadia
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.31084

Abstract

The eigenvalue problem in matrices is an important topic in numerical computation, particularly in analyzing the sensitivity of eigenvalues to disturbances or perturbations. This study discusses the absolute perturbation bounds on the eigenvalues of a matrix, focusing on normal matrices and their relationship to the condition of normal matrices. Based on existing theorems, the absolute perturbation bounds are presented in various forms involving the Frobenius norm and the condition number of the matrix eigenvectors. This research provides a detailed discussion of results concerning the absolute perturbation bounds on eigenvalues and their applications to normal matrices. Ultimately, an important result on the error bounds of eigenvalues in the case of normal matrices affected by perturbations is fully explained, proving the connection between the absolute error bound and the Frobenius norm of the perturbations.
Training on Creating Functions using GeoGebra at SMAN 5 Jember Merysa Puspita Sari; Ainurrofiqoh, Dewi Ika; Zahro, Millatuz; Mulyaningsih, Woro; Helmi, Muhammad Lutfi
Jurnal Inovasi Sains dan Teknologi untuk Masyarakat Vol. 3 No. 1 (2025): Mei
Publisher : Faculty of Mathematics and Natural Sciences, University of Jember. Jl. Kalimantan No.37, Krajan Timur, Jemberlor, Kec. Sumbersari, Jember Regency, East Java 68121

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/instem.v3i1.5504

Abstract

Mathematics education at the Senior High School level plays an important role in forming the foundational understanding of more complex mathematical concepts, one of which is algebraic function graphs. Despite its importance, many students face difficulties in understanding and drawing algebraic function graphs due to the limitations of traditional teaching tools. GeoGebra, a computer-based mathematics application, can be a solution to address this issue. GeoGebra offers advanced visual aids, allowing students to draw and analyze graphs interactively. The use of GeoGebra makes it easier to visualize various elements of graphs, such as intersection points, slopes, and extrema, and allows students to conduct experiments by manipulating function parameters. The aim of this community service activity is to introduce the concept of algebraic function graphs, teach the use of GeoGebra as a visual aid, and enhance students' analytical skills and learning motivation at SMAN 5 Jember. The methods applied includes the socialization and introduvtion of GeoGebra, interactive workshops, independent practice, and student understanding evaluation. The results of this activity show that students are more motivated and have a better understanding of algebraic function graphs after using GeoGebra. Therefore, the use of GeoGebra can be an effective alternative for teaching complex mathematical concepts at the Senior High School level.
Prediction of Rice Production in Jember Regency Using Adaptive Neuro Fuzzy Inference System (ANFIS) Riski, Abduh; Putriana, Novia Ayu; Fadri, Firda; Kamsyakawuni, Ahmad; Pradjaningsih, Agustina; Santoso, Kiswara Agung; Sari, Merysa Puspita
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2797.262-275

Abstract

Jember Regency is the fourth largest rice-producing regency/city in East Java, so Jember Regency dramatically contributes to increasing the agricultural sector in East Java Province. However, the level of rice production can fluctuate, which is influenced by other factors such as rainfall. A prediction system is needed to anticipate a decrease in rice production. This research aims to predict rice production in the Jember Regency using the Adaptive Neuro Fuzzy Inference System (ANFIS), highlighting the impact of key variables like rainfall, harvested area, and land productivity. This research consists of three stages: training, testing, and prediction. The input variables used in this research are rainfall (mm), harvested area (Ha.), and land productivity (Kw/Ha.), while the output variable is rice production (tons). The membership functions used are generalized Bell and Gaussian, with several combinations of many membership functions. The best model obtained from this research is a model that uses generalized bell membership functions with three membership functions for rainfall variables and two membership functions for harvest area and land productivity variables. The epoch (iteration) used to achieve minimum error is 100 epochs. The best model achieved high accuracy, producing a MAPE value of 0.080% in training and 1.525% in testing, indicating its strong potential for reliable agricultural production forecasting. The predicted amount of rice production in Jember Regency in 2024 was 922,136.8317 tons.
Performance Analysis of Grey Wolf Optimizer for Solving Nonlinear Systems with Complex Roots Merysa Puspita Sari; Dewi Ika Ainurrofiqoh; Agustina Pradjaningsih; Sailah Ar Rizka; Nadia Kholifia
Tensor: Pure and Applied Mathematics Journal Vol 7 No 1 (2026): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol7iss1pp1-8

Abstract

Nonlinear systems of equations consist of multiple equations that must be solved simultaneously, and analytical solutions are often difficult to obtain, particularly for complex cases. For this reason, numerical and metaheuristic approaches are frequently employed as practical alternatives. This study investigates the performance of the Grey Wolf Optimizer (GWO) in solving nonlinear systems involving both real and complex roots. The problem is reformulated as an optimization task by minimizing a modulus based objective function derived from the given system. The implementation is carried out in MATLAB using several test cases, and a parameter sensitivity analysis is conducted with respect to the number of search agents, search boundaries, and maximum iterations. To evaluate its performance, the results obtained using GWO are compared with those of the Particle Swarm Optimization (PSO) algorithm reported in previous studies. The findings indicate that GWO is able to produce stable solutions with objective function values close to zero across different cases. However, PSO tends to achieve higher accuracy and faster convergence in certain scenarios. Despite this, GWO demonstrates strong exploration capability, which contributes to its robustness and makes it a viable alternative for solving complex nonlinear systems.
Co-Authors Abduh Riski, Abduh Agustina Pradjaningsih Ahmad Kamsyakawuni Dewi Ika Ainurrofiqoh Fadri, Firda Firdaus Ubaidillah, Firdaus Helmi, Muhammad Lutfi Kholifia, Nadia Kiswara Agung Santoso Mulyaningsih, Woro Putriana, Novia Ayu Regina Wahyudyah Sonata Ayu Rizka, Sailah Ar Zahro, Millatuz