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Redaksi BAREKENG: Jurnal ilmu matematika dan terapan, Ex. UT Building, 2nd Floor, Mathematic Department, Faculty of Mathematics and Natural Sciences, University of Pattimura Jln. Ir. M. Putuhena, Kampus Unpatti, Poka - Ambon 97233, Provinsi Maluku, Indonesia Website: https://ojs3.unpatti.ac.id/index.php/barekeng/ Contact us : +62 85243358669 (Yopi) e-mail: barekeng.math@yahoo.com
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BAREKENG: Jurnal Ilmu Matematika dan Terapan
Published by Universitas Pattimura
ISSN : 19787227     EISSN : 26153017     DOI : https://search.crossref.org/?q=barekeng
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation
BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure Mathematics (analysis, algebra & number theory), - Applied Mathematics (Fuzzy, Artificial Neural Network, Mathematics Modeling & Simulation, Control & Optimization, Ethno-mathematics, etc.), - Statistics, - Actuarial Science, - Logic, - Geometry & Topology, - Numerical Analysis, - Mathematic Computation and - Mathematics Education. The meaning word of "BAREKENG" is one of the words from Moluccas language which means "Counting" or "Calculating". Counting is one of the main and fundamental activities in the field of Mathematics. Therefore we tried to promote the word "Barekeng" as the name of our scientific journal also to promote the culture of the Maluku Area. BAREKENG: Jurnal ilmu Matematika dan Terapan is published four (4) times a year in March, June, September and December, since 2020 and each issue consists of 15 articles. The first published since 2007 in printed version (p-ISSN: 1978-7227) and then in 2018 BAREKENG journal has published in online version (e-ISSN: 2615-3017) on website: (https://ojs3.unpatti.ac.id/index.php/barekeng/). This journal system is currently using OJS3.1.1.4 from PKP. BAREKENG: Jurnal ilmu Matematika dan Terapan has been nationally accredited at Level 3 (SINTA 3) since December 2018, based on the Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia, with Decree No. : 34 / E / KPT / 2018. In 2019, BAREKENG: Jurnal ilmu Matematika dan Terapan has been re-accredited by Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia and accredited in level 3 (SINTA 3), with Decree No.: 29 / E / KPT / 2019. BAREKENG: Jurnal ilmu Matematika dan Terapan was published by: Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura Website: http://matematika.fmipa.unpatti.ac.id
Arjuna Subject : Matematika - Matematika Terapan
Articles 1,248 Documents
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CLASSIFICATION OF SKELETAL MALOCCLUSION USING CONVENTIONAL NEURAL NETWORK (CNN) WITH VISION ATTENTION Ronny Eka Wicaksana, I Putu; Wibowo, Antoni; Rojali, Rojali; A Samah, Azurah; Alias, Aspalilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2709-2726

Abstract

Skeletal malocclusion, a common orthodontic condition, affects jaw function and dental health. It is often caused by genetic factors, abnormal growth, bad habits, or trauma. Conventional diagnostic models often fail to generalize across diverse datasets, leading to overfitting and poor test performance. This study aimed to improve diagnostic accuracy by incorporating Vision Attention mechanisms into a custom Convolutional Neural Network (CNN), enabling the model to focus on critical regions in X-ray images. A total of 491 radiographic images depicting facial skeletal structures with various malocclusion types (Classes 1, 2, and 3) were used in this study. A custom CNN was developed and evaluated both with and without attention mechanisms—specifically, Scaled Dot Product Attention and Multihead Attention—to assess their impact on classification performance. The baseline CNN without attention achieved an accuracy of 0.68. With Scaled Dot Product Attention, accuracy improved to 0.72, while Multihead Attention achieved the highest accuracy of 0.76. Evaluation using weighted average precision, recall, and F1-score showed that attention mechanisms significantly enhanced the model’s ability to differentiate between malocclusion classes. Notably, the Multihead Attention model yielded the best performance, reducing misclassification errors and improving generalization. Confusion matrix analysis revealed that it had the lowest classification errors, especially in distinguishing between Class 0 and Class 1. These results suggest that incorporating attention mechanisms, particularly Multihead Attention, enhances CNN performance by improving feature extraction and classification accuracy. Future research should explore more diverse datasets and implement advanced augmentation techniques to improve clinical reliability.
THE LOCATING RAINBOW CONNECTION NUMBERS OF LOLLIPOP AND BARBELL GRAPHS Bustan, Ariestha Widyastuty; Talib, Taufan; Laamena, Novita Serly; Saputra, Lamanisa Rasid; Nurhayati, Nurhayati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2727-2738

Abstract

The concept of the locating rainbow connection number of a graph is an innovation in graph coloring theory that combines the concepts of rainbow vertex coloring and partition dimension on graphs. This concept aims to determine the smallest positive integer such that there exists a locating rainbow -coloring on the graph, ensuring that every vertex has a unique rainbow code. In this study, we investigate the locating rainbow connection number of the lollipop graph and barbell graph . Using a literature study method, hypotheses were formulated and proven through theoretical analysis. The results show that and .
COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE Budiani, Jauhara Rana; Mahmudah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2739-2750

Abstract

The heart is a vital organ in the human body that functions to pump blood throughout the body and to the lungs. The heart is located in the chest cavity. The heart is the main force that drives human life. Therefore, if there is a disturbance in heart function, this can cause a decrease in quality of life to death, one of which is heart failure. Heart failure, if not diagnosed and treated quickly, will result in death. Based on findings showing the high death rate due to heart failure, a classification is needed to predict heart failure using machine learning methods. Machine learning can help predict this disease to improve early detection and more accurate medical decision-making. This study focuses on predicting the likelihood of a patient experiencing heart failure. The machine learning algorithm method used is supervised machine learning classification, including decision trees, random forests, naïve bayes, SVM, and K-NN. The results showed that the best method for predicting heart failure was Random Forest with an accuracy of 74.35%, followed by SVM with an accuracy of 69.23%. Meanwhile, Naïve Bayes had the lowest accuracy of 51.28%. Based on these findings, Random Forest is recommended as the best method for heart failure prediction due to its ability to handle data complexity and provide more stable results. Once the best algorithm is obtained, the prediction results and early detection of heart failure will be more accurate.
OPTIMIZATION MODEL FOR MULTI-DEPOT ELECTRIC VEHICLE ROUTING PROBLEM WITH SOFT TIME WINDOWS WITH SCENARIO-BASED ANALYSIS Tan, Elfina; Bakhtiar, Toni; Jaharuddin, Jaharuddin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2751-2764

Abstract

The adoption of electric vehicles has increased due to their cost-efficiency and environmental impact. However, limited battery capacity requires careful route planning to ensure vehicles complete deliveries efficiently. This study focuses on the Multi-Depot Electric Vehicle Routing Problem with Soft Time Windows (MDEVRPSTW), where electric vehicles can depart from and return to multiple depots, while serving customers within predefined time windows that allow limited violations with penalty costs. The model is formulated using Mixed Integer Linear Programming (MILP) and solved using the exact branch-and-bound method in Lingo 20.0. Two operational scenarios are considered: (1) vehicles must return to their original depot, and (2) vehicles are allowed to return to any depot. Hypothetical data is used to simulate delivery routes with varied time windows and battery capacity constraints. Results show that both scenarios produce feasible, cost-minimizing solutions. Allowing flexible depot return (scenario 2) consistently reduces total travel cost, highlighting the practical benefit of depot flexibility in real-world logistics. This model contributes to the EV routing literature by integrating multiple depots—both fixed and flexible return options—soft time windows, and battery constraints into a single formulation. However, it assumes constant travel speeds and does not account for charging durations, which presents an opportunity for future research.
COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN YOGYAKARTA Utari, Dina Tri; Palage, Ghalang Rambu Putera; Fadhlirobby, Faiz; Nuswantoro, Artheta Bimo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2765-2776

Abstract

Precise rainfall classification is most important for meteorological forecasting and disaster risk mitigation, particularly in regions such as Yogyakarta, which are vulnerable to extreme weather events. Although previous studies have examined rainfall classification through the lens of meteorological variables, a notable lack of research has systematically evaluated the effectiveness of diverse machine learning algorithms for categorizing rainfall types within this specific locale. This study aims to rectify this gap by incorporating essential weather variables, specifically temperature, humidity, atmospheric pressure, and precipitation, into predictive models that utilize K-Nearest Neighbors (KNN), decision trees, and logistic regression techniques. Among the evaluated models, the decision tree demonstrated the highest degree of accuracy across both training and testing datasets. An examination of feature significance indicated that precipitation emerged as the most pivotal variable, aligning with the fundamental physical mechanisms associated with rainfall. This study contributes significantly to the evolving field of weather informatics by illustrating the utility of machine learning approaches in classifying regional rainfall. However, the parameters of this research are limited to specific meteorological variables and do not account for spatial or temporal variations, which could potentially influence the model’s broader applicability. Future research endeavors could augment this framework by integrating remote sensing data and methodologies for spatiotemporal modeling.
BREAKING BARRIERS IN OPTIMIZATION: CHAOTIC MAP-INTEGRATED ALGORITHMS FOR PRACTICAL CHALLENGE Luangpaiboon, Pongchanun; Visuwan, Danupun; Nanphang, Atiwat; Ruekkasaem, Lakkana; Aungkulanon, Pasura
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2777-2790

Abstract

Real-world applications frequently necessitate optimization of chaotic response surfaces and constrained functions, which present difficult challenges for conventional methods. In order to effectively manage constraints and uncertainty, these complexities necessitate sophisticated algorithms. The objective of this research is to optimize the Rider Optimization Algorithm (ROA) by incorporating chaotic maps—namely, Logistic, Sinusoidal, and Iterative—to enhance exploration and exploitation. The chaotic ROA consistently outperforms the standard ROA in convergence speed, accuracy, and robustness, as evidenced by benchmark evaluations. For instance, in the multiple disk clutch brake design problem, the chaotic ROA obtained the highest objective value of 0.2352, which was equivalent to or greater than the leading algorithms TSO, MFO, and WOA. The chaotic ROA variants (ROAC1, ROAC2, ROAC3) exhibited superior stability by achieving low standard deviations (e.g., 0.3321 in the Branin function at high noise levels) across noisy response surface benchmarks. The integration of constraint-handling mechanisms guaranteed that practicable solutions were achieved without sacrificing optimality. The chaotic ROA is established as a robust and adaptable solution for complex, noisy, and constrained optimization challenges in industrial scheduling, resource allocation, and engineering design by the proposed approach.
PARTICLE SWARM OPTIMIZATION FOR CUTTING ALUMINUM STOCK AND ITS COMPARISON WITH THE EXACT METHOD Silalahi, Bib Paruhum; Aminah, Siti; Mayyani, Hidayatul; Aman, Amril
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2791-2802

Abstract

The Cutting Stock Problem (CSP) is a common challenge in many industries, involving the optimization of material cutting to minimize waste while meeting customer demands. Various methods can be used to address this issue. This paper applies the heuristic Particle Swarm Optimization (PSO) method to solve CSP in the case of one-dimensional aluminum roll cutting. First, we identify feasible cutting pattern combinations. A mathematical model and constraints are then formulated based on these patterns. Next, the PSO algorithm is employed to determine the optimal combination of cutting patterns, minimizing material waste. The results yield the optimal aluminum roller cutting pattern. Furthermore, we compare the results between the PSO method and the exact method.
AN IMPROVED HYBRID CONJUGATE GRADIENT METHOD WITH SPECTRAL STRATEGY AND ITS APPLICATIONS IN COVID-19 PREDICTION Kamfa, Kamilu; Yunus, Rabiu Bashir; Lawan, Muhammad Auwal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2803-2814

Abstract

This paper introduces a hybrid conjugate gradient (CG) method for unconstrained optimization with a spectral strategy, inspired by key advancements in existing CG techniques. The proposed method guarantees a descent direction at every iteration, regardless of the line search scheme employed. Its global convergence is rigorously established under the Wolfe line search conditions. Numerical experiments on benchmark optimization problems demonstrate that the proposed method outperforms the FR and RMIL methods across multiple performance metrics. Furthermore, its effectiveness is showcased in a neural network framework for predicting chickenpox and COVID-19 infection cases, highlighting its practical applicability in real-world scenarios.
PREDICTION OF SOIL PARTICLES USING A SPATIALLY ADAPTIVE GEOGRAPHICALLY WEIGHTED K-NEAREST NEIGHBORS ORDINARY LOGISTIC REGRESSION APPROACH Pramoedyo, Henny; Ngabu, Wigbertus; Iriany, Atiek; Riza, Sativandi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2815-2830

Abstract

Soil particle prediction is crucial in various fields, including agriculture, environmental management, and geotechnical applications. The spatial variation of soil texture significantly affects land fertility, erosion risk, and construction feasibility. However, conventional statistical methods and machine learning techniques often fail to capture the complex spatial heterogeneity in soil distribution. This study proposes the Geographically Weighted K Nearest Neighbors Ordinary Logistic Regression (GWKNNOLR) method to improve the accuracy of soil particle classification by integrating geographically weighted regression with an adaptive spatial weighting mechanism using the K Nearest Neighbors (KNN) algorithm. The objective of this research is to develop and evaluate a spatially adaptive classification model that more accurately predicts soil particle categories, namely sand, silt, and clay, by incorporating local spatial dependencies using GWKNNOLR in the Kalikonto watershed (DAS Kalikonto) in Batu. This study utilizes field measurement data combined with digital terrain modeling to analyze the relationship between local morphological variables and soil texture classification (sand, silt, and clay). The study area includes 50 observation points and 8 test variables. The model's performance is compared to the Ordinary Logistic Regression (OLR) method. The results indicate that GWKNNOLR achieves a classification accuracy of 88 percent, outperforming OLR, which only reaches 80 percent. Integrating KNN as a spatial weighting mechanism enhances adaptability to variations in sample distribution, leading to more accurate predictions. These findings emphasize the importance of considering spatial dependencies in soil texture modeling. The proposed method can support sustainable land resource management, erosion risk mitigation, and precision agriculture by providing more reliable soil classification. Future research may explore further optimization of spatial weighting mechanisms and the application of this method in different geographical regions.
PERSYMMETRIC MATRIX AND ITS APPLICATION IN CODING THEORY Hidayat, Ardi Nur; Krisnawati, Vira Hari; Alghofari, Abdul Rouf
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2831-2842

Abstract

A persymmetric matrix is a square matrix that is symmetric concerning its antidiagonal. This article discusses some characteristics of a persymmetric matrix and its application in coding theory. A persymmetric matrix is used to form a generator matrix of binary reversible self-dual codes. A binary reversible self-dual code is a self-dual code whose reverse element is contained in the code. The methodology involves the implementation of flip transpose and column reversal to ensure the generator matrix satisfies both self-duality and reversibility. We begin with small-sized persymmetric matrices (e.g., 2×2 and 3×3) to extend the construction of the larger matrices. Combining a self-dual code and a reversible self-dual code of shorter length, and embedding persymmetric blocks, we construct new binary reversible self-dual codes of longer length. The novelty of this research lies in developing a new construction method for binary reversible self-dual codes derived directly from self-dual codes in the standard form, which has not been explicitly addressed in previous studies.

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