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Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
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Jambura Journal of Mathematics
Published by Universitas Negeri Gorontalo
ISSN : 26545616     EISSN : 26561344     DOI : https://doi.org/10.34312/jjom
Core Subject : Education,
Mathematics
Jambura Journal of Mathematics (JJoM) is a peer-reviewed journal published by Department of Mathematics, State University of Gorontalo. This journal is available in print and online and highly respects the publication ethic and avoids any type of plagiarism. JJoM is intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in research. The scope of the articles published in this journal deal with a broad range of topics, including: Mathematics; Applied Mathematics; Statistics; Applied Statistics.
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Articles 4 Documents
 1 
Search results for , issue "Vol 8, No 1: February 2026" : 4 Documents clear
Application of the Laguerre Perturbed Galerkin Analysis Method for Solving Higher-Order Integro-Differential Equations Adebisi, Ajimot Folasade; Ojurongbe, Taiwo Adetola; Okunola, Kazeem Adekunle
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.34001

Abstract

This study presents the development and implementation of a novel numerical method, the Laguerre Perturbed Galerkin (LPG) method, for solving higher-order integro-differential equations. The method leverages the advantages of Laguerre polynomials as basis functions while incorporating Chebyshev polynomials as perturbation terms to enhance both accuracy and efficiency. In the LPG method, the solution is approximated using Laguerre polynomials of degree N, with the residual error minimized via the Galerkin approach. Chebyshev polynomials are introduced as perturbation terms to further refine the solution. The residual is systematically reduced to a system of (N + 1) equations, which is then solved to determine the unknown coefficients of the approximating Laguerre polynomials. Comparative analyses demonstrate that the LPG method achieves superior accuracy and faster convergence rates compared to existing techniques, particularly for higher-order integro-differential equations. The findings contribute to the advancement of numerical methods in this domain, providing a powerful computational tool for scientists and engineers.
Analisis Kinerja dan Efisiensi Energi k-means dan Gaussian Mixture Model Terdistribusi pada Klaster Single Board Computer dan Personal Computer dengan Apache Spark Noer, Deffin Purnama; Liebenlito, Muhaza; Sutanto, Taufik Edy
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35198

Abstract

This study aims to evaluate the performance and energy efficiency of distributed unsupervised learning algorithms on two types of clusters, namely Single Board Computers (SBC) and Personal Computers (PC), using Apache Spark. Two algorithms were tested—k-means and Gaussian Mixture Model (GMM)—executed across varying dataset sizes and numbers of processor cores to observe scalability. The results show that PCs consistently achieved faster execution times, particularly with k-means on large datasets. On the other hand, SBCs demonstrated higher energy efficiency in all scenarios, with energy savings of up to 93% for k-means and 86% for GMM compared to the highest-consumption configuration on PC. These findings affirm the potential of SBCs as a low-power and cost-efficient solution for green or sustainable computing, particularly for learning, academic experimentation, and small-scale edge computing development, and are relevant to sustainability efforts through their contribution to the Sustainable Development Goals (SDGs).
Pelabelan Prima pada Kelas Graf Hasil Operasi Perkalian Tensor Triwahyuniti, Suci; Rahmadani, Desi
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.34596

Abstract

A graph  with a vertex set   is said to be a prime graph if there exists a bijective mapping , where  denotes the number of vertices in , such that for any two adjacent vertices  and  in  have . Tensor Product graph is a way to combine (compose) two graphs into one larger and more complex graph. The result is a new graph that reflects the connection properties of the two original graphs, but in a very specific and more complex way than other graph operations. Therefore, this research aims to determine whether there is prime labeling in the class of graphs resulting from the Tensor Product of the path graph  and the cycle graph . The research employed analytical and exploratory methods with a trial-and-error strategy to determine the labeling that possesses a prime property. The results of this study prove that two classes of the Tensor Product graph  for , and graph , for  are prime graph. This finding expands the results on classes of graphs that admit prime labeling  and provides a basis for further research on graph labeling in other graph operations
A Hybrid Grey Wolf Optimizer–Zebra Optimization Algorithm for Solving Optimization Problems Ali, Ayad
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.34499

Abstract

Metaheuristic algorithms are widely applied to complex optimization problems, yet many suffer from premature convergence or slow search efficiency. To report these limitations, this paper proposes a new hybrid algorithm, Grey Wolf Optimizer–Zebra Optimization Algorithm (GWO–ZOA). The algorithm integrates the exploitation ability of the Grey Wolf Optimizer with the exploration capability of the Zebra Optimization Algorithm in a sequential framework, thereby enhancing both convergence accuracy and global search ability. The performance of GWO–ZOA is first evaluated on 23 standard benchmark functions, where it demonstrates competitive results in both unimodal and multimodal landscapes. Further validation is carried out on the CEC2017 and CEC2020 benchmark suites, confirming the hybrid’s robustness across higher-dimensional and more challenging composite problems. In all three benchmark categories, the Friedman statistical test ranks GWO–ZOA first among the compared algorithms, highlighting its superior overall performance. Finally, the algorithm is applied to two real-world engineering design problems, where it consistently achieves high-quality feasible solutions and demonstrates practical effectiveness. These results confirm that the proposed GWO–ZOA algorithm is both robust and reliable for solving diverse and complex optimization tasks.

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