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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 179 Documents
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Constructing DNA Codes with Larger Distance from Quaternary Words Pradipta, Benediktus Panji; Puspita, Nikken Prima
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33132

Abstract

In this work, we propose a novel method to construct DNA codes from quaternary words. The method uses permutation groups that act in the set {1, 2, …, 4n}, representing the coordinate and coordinate value of quaternary words. The DNA code is obtained by finding a clique of the construction graph and mapping it using the bijective map 0 → A, 1 → C, 2 → T, 3 → G. We present a new approach to construct DNA codes with larger Hamming distance and reverse-complement distance compared to previously obtained DNA codes. This is achieved by using a modified construction graph tailored to the desired distance parameter. As a result, we can refine a DNA code to have improved Hamming distance and reverse-complement distance while maintaining the fixed GC-content constraint. This method simplifies the search for DNA codes with large distance parameters.
Performance Comparison of VGG16, MobileNetV2, and InceptionV3 Convolutional Neural Networks in Classifying Facial Dermatological Conditions Nadiyah, Fadilah Karamun Nisaa; Alifah, Nayla Nur; Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33082

Abstract

This study investigates the performance of three convolutional neural network (CNN) architectures (VGG16, MobileNetV2 and InceptionV3) in classifying two common facial dermatological conditions: acne and dark spots. A dataset of 235 facial skin images was augmented, then used to train and evaluate each model using standard classification metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that MobileNetV2 achieved the highest classification accuracy of 93.13% while maintaining a relatively low computational cost. The model exhibited perfect precision (1.00) for the acne class and a high recall of 0.99 for the dark spots class, indicating its strong capability in accurately and sensitively identifying both lesion types. All three models demonstrated acceptable classification performance for both acne and dark spots classes, as evidenced by their precision, recall, and F1-scores exceeding 70%. This indicates that each model was capable of capturing relevant discriminative features of both lesion types.
Graf Konjugasi dari Hasil Kali Langsung Grup Alternating A4 dan Grup Simetri S3 Muammar, Muhammad Fikri; Faisol, Ahmad; Fitriani, Fitriani
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.32926

Abstract

This study investigates the structure of conjugacy graphs formed from the conjugacy classes in the alternating group A4, the symmetric group S3, and their direct product A4 × S3. Using Mathematica, the conjugacy classes of each group are determined, and the corresponding conjugacy graphs are constructed to represent the relationships between the classes. The results show that the conjugacy graphs of A4 × S3 form a complete graph Kᵢ×ⱼ, where i and j are the number of conjugacy classes in A4 and S3, respectively. These findings indicate that the conjugacy structure of the direct product exhibits a distinctive combinatorial complexity derived from its component groups.
Optimalitas Rute pada Pengiriman Multiperjalanan dengan Armada Kendaraan Listrik Heterogen Salsabilla, Kamilia; Bakhtiar, Toni; Hanum, Farida
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23993

Abstract

Electric vehicles are emerging as a key trend in sustainable mobility, mitigating emissions, and reducing dependence on fossil fuels. The challenge in optimizing route modeling lies in some limitations, such as battery range, charging time, and the diversity of electric vehicle types. This article explores the optimality of routes in a multiple-trip distribution system using a heterogeneous fleet of electric vehicles. The electric vehicle routing problem is formulated as a mixed-integer linear programming aiming to find the most cost-efficient optimal route. A notable feature of the model allows electric vehicle fleets to undertake additional travel to complete distribution tasks, i.e., multiple trips. The model is implemented in two illustrative examples involving the delivery of goods using homogeneous and heterogeneous electric vehicle fleets characterized by loading and battery capacities. Each case includes one depot, 8 and 10 customers, and 2 battery swapping stations, solved using the branch-and-bound method through Lingo 18.0. Simulation results indicate that battery capacity and the presence of battery swapping stations significantly influence the routes selection.
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.
Pemodelan Faktor Risiko Stunting Berbasis Titik Menggunakan Geographically Weighted Logistic Regression di Kabupaten Bone Bolango Akolo, Ingka Rizkyani; Djafar, Fatimah; Paembonan, Maya
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.v8i1.35871

Abstract

Stunting remains a major public health issue in Indonesia, including in Kabupaten Bone Bolango, which recorded a high prevalence in 2023. Variations in social, economic, and environmental conditions across observation locations indicate the need for analyses that account for spatial differences between areas. This study aims to identify the spatial variation in the effects of significant stunting risk factors using the Geographically Weighted Logistic Regression (GWLR) method. The data were obtained from the Health Office of Kabupaten Bone Bolango in 2019. The independent variables included complete basic immunization (X1), the percentage of low-birth-weight infants (X2), and the percentage of exclusive breastfeeding (X3), with the response variable defined as high stunting prevalence (1) and low stunting prevalence (0). The analysis comprised multicollinearity testing, the Breusch-Pagan spatial heterogeneity test, bandwidth selection using cross-validation, construction of an adaptive Gaussian kernel weighting matrix, and parameter estimation via maximum likelihood with the Newton-Raphson method. The multicollinearity test indicated that all variables were free from collinearity (VIF 10). The Breusch-Pagan test revealed the presence of spatial heterogeneity (p 0.10), confirming the appropriateness of the GWLR model. The results showed that the percentage of exclusive breastfeeding was significantly higher in Bone Raya, Bulawa, Bone, Bone Pantai, and Kabila Bone, whereas complete basic immunization and the percentage of low-birth-weight infants were not significantly different. These findings indicate that exclusive breastfeeding is a risk factor for stunting, with significant spatial variation, suggesting that stunting intervention strategies should be designed on a point-by-point, location-specific basis, taking into account the local characteristics of each observation point.
Parameter Estimation of Generalized Modified Weibull Using the Maximum Likelihood on Simulation and Real-World Data Putera, Muhammad Luthfi Setiarno; Purhadi, Purhadi
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.v8i1.36518

Abstract

This study estimates parameters of the generalized modified Weibull (GM Weibull) distribution using the Maximum Likelihood Estimation (MLE) method with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. The GM Weibull distribution, which includes four parameters (lambda, theta, phi, tau), offers greater flexibility than Weibull distribution in modeling data with monotonic and bathtub-shaped hazard patterns. Parameter estimation was conducted on three datasets: simulated data with sample sizes of 50, 200, and 500 observations; survival data from 45 heart transplant patients; and health indicator data from 27 districts/cities in Central and South Kalimantan provinces. The results demonstrate that while the standard Weibull remains a parsimonious choice for simple monotonic data, the GM Weibull produces parameter estimates closer to theoretical values in small-to-medium samples and significantly lower deviance in complex datasets. Specifically, for the heart transplant data, the GM Weibull offered better modeling long-term survival tails (800--1,000 days), while for the health indicator data, it effectively accommodated central tendencies within asymmetric distributions. Although AIC and BIC favor standard Weibull, the GM Weibull accurately identifies underlying structural fluctuations and non-monotonic failure characteristics. This study confirms that the MLE-based GM Weibull distribution is one of the robust tools for researchers requiring a more representative model for complex survival and health data.

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