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International Journal of Applied Mathematics and Computing.
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30481988     EISSN : 3047146X     DOI : 10.62951
Core Subject : Science, Education,
Computer Science & IT Mathematics
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. This journal is a peer-reviewed and open access journal of Mathematics and Computing
Arjuna Subject : Matematika - Matematika Komputer
Articles 32 Documents
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Application of Conjoint Analysis with Attributes Determined Against the Selection of Expedition Services Putu Rama Hari Bagaskara P.; Ni Luh Putu Suciptawati; Made Susilawati
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.201

Abstract

This study aims to determine the level, attribute, and stimulus most considered by respondents in choosing expedition services. The method used is conjoint analysis by applying the conjoint analysis full-profile method to design the stimulus. The attributes and levels used are delivery type (ECO, REG, express, and same day), payment methods (cash and online transfers), service promotions (cost discounts and goods pick-up), and service responsiveness (responses to damage or loss of goods and responses to late delivery). The results of research conducted on 150 respondents showed that delivery type is the most preferred attribute with a value of 0,324. The levels with the largest part-worth of each attribute are REG (0.335), online transfers (0.210), cost discounts (0.270), and response to damage or loss of goods (0.250). The most popular stimulus is expedition services with standard shipping (REG), online transfers, cost discounts, and responses to damage or loss of goods.
Identification of Risk Factors for Chronic Kidney Disease Using Binary Logistic Regression Kosasih, Eva; Asmara Santhi, Ni Kadek Wulanda; Febriyanti, Ni Wayan Atik; Br Barus, Eka Valencia; Susilawati, Made
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.222

Abstract

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy
Using Mathematical Programming to Analyze and Improve Robust Queue Management in Healthcare Systems Hasanain Hamed Ahmed
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.229

Abstract

Efficient management of patient queues is essential in healthcare systems to ensure timely care, optimize resource utilization, and enhance patient satisfaction. Mathematical programming, particularly when applied in conjunction with queuing theory and optimization models, provides a rigorous framework for analyzing and improving healthcare service delivery. This approach involves modeling arrivals and service processes, applying queuing models (such as single-server, multi-server, and priority queues), and formulating optimization objectives—often to minimize total costs, patient waiting times, or resource idling. Recent research demonstrates that combining queuing theory with mixed-integer programming and simulation techniques enables healthcare managers to allocate resources dynamically, set staffing levels, and assign priorities among different patient categories. For example, the use of mixed-integer programming can determine the optimal number of servers, beds, and service rates based on patient flow and priority needs, striking a balance between reducing waiting times for critical cases and controlling operational costs. These mathematical models also account for practical constraints and stochastic variability inherent in clinical settings. Applications span emergency departments, outpatient clinics, and even pharmacy and blood service centers—showing significant improvements in system efficiency, reduced patient wait times, and enhanced overall care quality. Thus, mathematical programming is a powerful decision-support tool for queue management, offering evidence-based strategies to address congestion and resource allocation challenges in complex healthcare environments.
Analysis of Bottled Water Brand Market Share Using the Markov Chain Method in the Faculty of Mathematics and Natural Sciences, Udayana University Silvia Helena Ngantung; Ni Luh Putu Marina Atlanticia; Ni Made Jenni Prabayanti
International Journal of Applied Mathematics and Computing Vol. 3 No. 1 (2026): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i1.248

Abstract

This study analyzes bottled water consumer traits and the market share of various brands over five years at the University of Udayana using the Markov chain method. Primary data from questionnaires show most consumers are female informatics students in dorms, consuming over 2 liters daily, mostly purchasing from stores. Decisions consider quality and brand, influenced by TV ads over peer recommendations. Market share in period 1: Aqua led with 52%, followed by Le Minerale (28%), Club (13%), Cleo (7%), and others (0%). In period 2, Aqua maintained 52%, Le Minerale rose to 36%, while Club and Cleo declined to 2% and 3%. Period 3 saw Aqua at 49%, Le Minerale at 33%, and Club/Cleo at 2% and 1%. In period 4, Aqua led with 45%, Le Minerale at 31%, and Club/Cleo/others at 2%, 1%, and 7%. Finally, in period 5, Aqua remained at 41%, Le Minerale fell to 28%, while others decreased to 6%, and Club/Cleo remained at 2% and 1%.
Adaptive Algorithmic Simulation for Nonlinear Eigenvalue Problems in Mathematical Physics Abid Nurhuda; Ali Anhar Syi’bul Huda; Syeda Azwa Asif
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.265

Abstract

Nonlinear eigenvalue problems (NEPs) pose significant challenges in mathematical physics and other computational applications due to their nonlinear nature, which makes analytical solutions difficult to obtain. NEPs are encountered in various scientific and engineering fields, including signal processing, electronic structure calculations, and structural optimization. This study aims to explore the application of adaptive algorithms in solving nonlinear eigenvalue problems, with a primary focus on improving accuracy and computational efficiency. The proposed method combines an iterative solver with adaptive step-size adjustment, where the step size is dynamically adjusted during the iteration based on error estimates calculated at each step. This approach enables faster convergence and significant reductions in computational time without compromising accuracy. In experiments conducted on large-scale problems, the adaptive algorithm reduced computational time by 40% faster compared to fixed-step iterative methods. The comparison between the adaptive algorithm and traditional methods showed that the adaptive algorithm is not only more efficient but also more robust when dealing with high-complexity problems. Additionally, the adaptive algorithm provides more accurate error estimates, allowing better error control throughout the iteration process. Overall, this study concludes that adaptive algorithms offer a more effective and efficient solution for complex nonlinear eigenvalue problems and can be adapted to various types of problems in scientific and engineering applications. Further research could focus on optimizing the implementation of this algorithm for larger and more complex scales.
Numerical Analysis and Computational Algorithms in the Simulation of Integral Equations in Applied Mathematics Jimmi Ari Duri; Yuniana Cahyaningrum; Syed Anfal Asif
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.266

Abstract

Integral equations are essential tools in applied mathematics, with wide-ranging applications in fields such as physics, engineering, and finance. However, solving these equations presents significant challenges, particularly when dealing with complex, high-dimensional, or singular problems. Traditional methods, such as manual analytical techniques or direct numerical approaches, often struggle with computational efficiency, especially for large-scale systems, and may not be suitable for handling ill-conditioned problems. This study aims to develop an efficient numerical method for solving integral equations by combining adaptive quadrature techniques with Python-based iterative solvers. The adaptive quadrature method adjusts the step size dynamically based on error estimates, ensuring high accuracy even in the presence of singularities or near-singularities, which are common in many real-world problems. The iterative solver, based on Krylov subspace methods, enhances computational efficiency by reducing memory usage and improving the convergence speed of the solution. By using these techniques together, the proposed method significantly improves the computational time required to solve large-scale and complex systems of integral equations, while maintaining satisfactory accuracy. The results demonstrate that the adaptive quadrature technique, when combined with the Python-based iterative solver, offers a substantial advantage in both speed and precision compared to traditional methods. The proposed method is especially effective in handling complex, high-dimensional systems and ill-conditioned problems, making it a powerful tool for applied mathematics, physics, and engineering applications. In conclusion, this study presents a robust and efficient approach for solving integral equations, with potential for future research in solving non-linear and multi-dimensional integral equations.
Computational Modeling and Simulation of Nonlinear Dynamical System Stability in Applied Mathematics Aji Priyambodo; Hariyono Rakhmad; Muhammad Shakir
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.271

Abstract

Nonlinear dynamical systems represent a fundamental area of study in applied mathematics due to their relevance across various disciplines, including physics, biology, and engineering. Their inherent complexity, characterized by phenomena such as bifurcation, chaos, and sensitivity to parameter variations, often limits the effectiveness of traditional manual analysis, particularly when addressing high-dimensional or computationally intensive models. This study aims to address these challenges by applying computational modeling and numerical simulation techniques to analyze the stability of nonlinear dynamical systems. The research employs analytical methods, including equilibrium point identification and linearization, which are then validated and extended through the fourth-order Runge-Kutta numerical method. Simulations were conducted to visualize equilibrium points, phase portraits, and parameter-driven bifurcation phenomena. The findings demonstrate a strong correspondence between analytical and numerical approaches, with minimal error margins (≤1%) observed in equilibrium point estimation, thus confirming the reliability of computational methods. Moreover, the bifurcation analysis revealed critical transitions such as pitchfork and Hopf bifurcations, which indicate sudden shifts from stability to instability behaviors that are difficult to capture through manual calculations alone. The integration of computational approaches provides clear advantages, offering systematic exploration of parameter spaces and detailed visualizations of system dynamics, thereby expanding the scope of stability analysis. In conclusion, this study emphasizes that computational modeling is not only an effective complement to analytical methods but also a necessary strategy for advancing the understanding of nonlinear dynamical systems in applied mathematics.
Mathematical and Computational Analysis in the Simulation of Iterative Algorithms for Solving Partial Differential Equations Saugadi Saugadi; Armadi Chairunnas; Bhadrappa Haralayya
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.272

Abstract

This research explores the use of iterative methods in conjunction with the Finite Difference Method (FDM) for solving partial differential equations (PDE). The central challenge addressed is the computational inefficiency and slow convergence that often arise when utilizing traditional numerical methods, particularly in large-scale systems. The study aims to develop a more efficient iterative approach to solve PDEs by minimizing computational time while ensuring the stability of the obtained solutions. The primary methods proposed include iterative solvers such as Gauss-Seidel and Successive Over-Relaxation (SOR), which are applied to numerical solutions derived from FDM. The research demonstrates that iterative methods, especially SOR, achieve faster convergence with fewer iterations compared to conventional methods like the Finite Element Method (FEM), which tends to be slower and more resource-intensive for large-scale problems. The study highlights the advantages of iterative solvers in efficiently handling large, sparse linear systems and reducing computational costs. In addition, it shows that these methods are capable of providing stable solutions, thereby maintaining accuracy with significantly lower computational effort. The results suggest that iterative methods, when applied in combination with FDM, offer a practical and scalable solution for solving complex PDEs. These methods are especially beneficial in engineering and theoretical physics applications where large-scale simulations are prevalent. The study concludes with recommendations for future research, which should focus on further optimizing solver parameters, exploring hybrid approaches, and extending the methods to more complex PDEs with non-linearities or irregular geometries. By doing so, these techniques could contribute to even more efficient and practical solutions for real-world applications.
Computational Simulation and Algorithm Analysis for Solving Combinatorial Optimization Problems in Graph Theory and Discrete Mathematics Dwi Oktaviana; M. Rhifky Wayahdi; Syed Hassan Ali
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.273

Abstract

Combinatorial optimization is a fundamental area in operations research and computer science, focusing on identifying optimal solutions from a finite set of possibilities. This study explores the integration of branch and bound methods with heuristic algorithms to address optimization problems in graph theory and discrete mathematics. Python was employed for algorithm implementation due to its flexibility and comprehensive computational libraries, enabling efficient data analysis and visualization. Several benchmark problems were examined, including the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Graph Coloring. Simulations were conducted using datasets of varying sizes (small, medium, and large) to evaluate performance across different scales. The results demonstrate that the hybrid approach achieves a balance between solution quality and computational efficiency, outperforming brute-force methods in terms of speed while maintaining near-optimal accuracy. Tabulated results and graphical comparisons highlight the reduction in computation time and improved scalability of the proposed method. The findings suggest that combining systematic search strategies with heuristics offers a practical and effective framework for solving complex combinatorial optimization problems. Recommendations for future research include testing scalability with larger datasets, incorporating advanced metaheuristics, and applying the approach to real-world domains such as logistics and network design.
Algorithmic Simulation for Optimization in Combinatorial Mathematics Using Heuristic Techniques Ahmad Budi Trisnawan; Syed Asif Ali; Erlita Sulistiati
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.274

Abstract

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.

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