Journal of Research in Mathematics Trends and Technology

A Decision-Support Model for Football Squad Optimization: Integrating AHP-WASPAS and Binary Programming

Parapat Gultom — 2025-03-31

Team composition in professional football remains a critical challenge due to subjective biases and the complexity of player performance metrics. This study proposes a multi-criteria decision-making (MCDM) approach to optimize the starting lineup of PSIS Semarang. The Analytical Hierarchy Process (AHP) was used to determine the importance of positional attributes, followed by the Weighted Aggregated Sum Product Assessment (WASPAS) to evaluate individual player preferences. A binary integer programming model was developed to construct the optimal eleven-player formation. The proposed formation was validated through simulation in Football Manager 2023. Results showed that a 4-3-3 formation consistently outperformed alternatives, leading to first-place rankings and a President’s Cup win in simulation trials. This integrated decision-support model demonstrates effectiveness in enhancing team selection strategies in professional football.

Implementation of RShiny in Developing Interactive Learning Media for Analysis of Variance (ANOVA)

Justin Eduardo Simarmata — 2025-03-31

Effective learning is significantly influenced by the methods and media used in the teaching and learning process. However, many students still struggle to understand mathematical concepts due to the limited availability of interactive learning media. Therefore, this study aims to develop and evaluate the effectiveness of a technology-based interactive learning application designed to enhance students' understanding. The development of this application follows several stages, including needs analysis, design, implementation, testing, and evaluation. The application offers various features, such as interactive materials, practice exercises, and visual simulations, to facilitate better comprehension of concepts. The data used in this study consists of pretest and posttest results from 30 students who were given a series of basic questions, including calculating the mean, standard deviation, and performing simple statistical tests. Analysis using the Paired t-Test indicates a significant increase in posttest scores compared to pretest scores (p-value = 3.522e-08). Data visualization in the form of boxplots and line charts also demonstrates a trend of improved student performance after using the application. Thus, this study confirms that the developed interactive application is effective in enhancing student learning outcomes.

Review of Logistics Models for Perishable Goods: Comparative Analysis of Location Planning and Inventory Management under Supply Chain Disruptions

Normalina Napitupulu — 2025-03-31

This study reviews and compares two logistics models tailored to perishable goods, focusing on the spatial and temporal dimensions of supply chain optimization. The first model proposes a population-density-based location planning framework that applies network centrality measures to determine optimal order fulfillment center locations. The second model introduces a two-warehouse inventory policy designed to handle disruption scenarios, such as lockdowns, by balancing demand variation and spoilage rates. Both models were evaluated for their methodological approaches, performance under disruption, and adaptability. The integrated review underscores the necessity of combining spatial efficiency with dynamic stock resilience to improve supply chain effectiveness for perishable goods. Findings suggest that hybrid models leveraging both geographic and temporal decision layers can significantly strengthen logistics adaptability.

The Influence of Leadership, Motivation, and Work Environment on Company Performance at PT PLN (Persero) Sektor Pembangkitan Belawan

Andrew Satria Lubis — 2025-03-31

This study investigates the influence of leadership, motivation, and work environment on company performance at PT PLN (Persero) Sektor Pembangkitan Belawan. Employing a quantitative associative approach, the research utilizes multiple linear regression analysis to examine the relationships among variables. The population comprises 74 employees, with the entire population serving as the sample through a total sampling technique. Data were collected using a Likert-scale questionnaire and analyzed using SPSS software. The results reveal that leadership, motivation, and work environment each have a positive and significant effect on company performance, both partially and simultaneously. The regression equation derived is: Y = 3.203 + 0.275X₁ + 0.263X₂ + 0.197X₃. The model's Adjusted R² value is 0.619, indicating that 61.9% of the variance in company performance can be explained by the three independent variables. These findings underscore the critical role of human resource factors in enhancing organizational performance.

The study contributes to the literature on organizational behavior and human resource management by highlighting the significance of leadership, motivation, and work environment in driving company performance. It also provides practical insights for managers aiming to improve organizational outcomes through strategic human resource interventions.

Copula-Based Blind Detection of Copy-Move Image Forgery: A Robust Mutual Information Approach

Tulus Joseph Marpaung — 2025-03-31

Copula functions are powerful statistical tools for modeling the dependency structure between random variables and have been widely applied in domains such as finance, oceanography, and hydrology. However, their application in image processing, particularly for image forgery detection, remains underexplored. This study proposes a novel blind copy-move forgery detection algorithm based on copula-based mutual information, which evaluates statistical dependencies between overlapping image blocks. By leveraging copula theory, the method accurately identifies duplicated regions within a single image without requiring prior knowledge or external references. Experimental results on the CoMoFoD dataset demonstrate that the proposed method achieves high precision, recall, and F1-scores across various manipulation types, including translation, scaling, and rotation, and shows resilience to post-processing operations such as JPEG compression, blurring, noise, and color reduction. Comparative analysis reveals that the copula-based approach outperforms classical methods such as SIFT, SURF, and DWT-SVD. In addition to quantitative performance, qualitative visualizations confirm that the algorithm effectively localizes forged regions in complex scenes with minimal false detections. These findings highlight the potential of copula functions as a robust and efficient framework for digital image forensics.