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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 1,309 Documents
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APPLICATION OF UNCERTAIN MAX PLUS LINEAR FOR SHIP SCHEDULE SAFETY ANALYSIS: A CASE STUDY OF KM. LAMBELU Adhalia H, Nurul Fuady; Rafrin, Mardhiyyah; Pratama, Aditya Putra; Tungga, Rifaldy Atlant; Bayu, Bayu; Tahir, Syahrul Ramadhan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1077-1088

Abstract

This study aims to analyze the safety of the KM. Lambelu passenger ship schedule on its Parepare-Balikpapan route using the uncertain Max-Plus Linear (uMPL) approach. The uMPL model is used to represent the dynamics by considering the uncertainty of travel times between ports. Forward reachability analysis is conducted to verify whether the ship scheduling system meets the established safety criteria. The analysis results show that the analysis indicates that the KM. Lambelu scheduling system has safety vulnerabilities. This finding indicates the presence of potential accident or incident risks and emphasizes the need for evaluation and improvement of scheduling system to ensure ship operation within safe limits. This study identifies potential problems and risks associated with these findings and provides recommendations for improving the ship schedule.
DESIGN OPTIMIZATION OF GAS TRANSMISSION SYSTEM WITH DIFFERENTIAL EVOLUTION ALGORITHM Afdhal, Ahmad; Tasmi, Tasmi; Yunita, Ariana; Noegraha, Rangga Ganzar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1089-1098

Abstract

Gas distribution through a pipeline network is a highly complex process and requires significant financial investment. This network system consists of a source, pipe, the compressor and sink (consumer). The source is the node where the producer has the gas pressure that will be distributed, the pipe is used to connect the producer and the consumer. Between the pipes there is a compressor which functions to increase the pressure. This network system was created at a significant cost, so it is necessary to search for minimal costs, but consumer demand is still met. This research discusses the search for an optimal gas network with minimum costs. This minimum cost depends on several parameters i.e. the length and diameter of pipe, also the pressure on the compressors entry and exit points. There are many optimization methods, but one of the simple and easy to implement methods is the Differential Evolution Algorithm, so this method is used to determine the optimal solution to this problem. Researchers used seven DE variants based on mutation strategies, namely DE/rand/1, DE/best/1, DE/rand/2, DE/best/2, DE/current-to-best/1, DE/current-to- rand/1, and DE/rand-to-best/1. The seven variants have never been used in gas distribution networks by previous researchers. Therefore, the seven variants were compared, and the minimum solution was determined. The results show that the DE/best/2 variant is the variant that produces the minimum total costs compared to the other variants. DE/best/2 achieved the lowest annual operating cost at USD 13.99 million.
A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI Siswanto, Siswanto; Sunusi, Nurtiti; Yunita, Andi Isna; Davala, Muhammad Ridzky; Baso, Andi M. Alfin; Nurfadilah, Nurfadilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1099-1110

Abstract

The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.
MIXED-EFFECTS MODELS WITH GENERALIZED RANDOM FOREST: IMPROVED FOOD INSECURITY ANALYSIS Fransiska, Herlin; Soleh, Agus Mohamad; Notodiputro, Khairil Anwar; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1111-1124

Abstract

Food insecurity is a complex issue that requires a deep understanding of its influencing factors. Accurate predictions are crucial for effective interventions. Machine learning is well-suited to the large and complex data available in the big data era. However, machine learning generally does not accommodate hierarchical or clustered data structures, making them challenging for machine learning modeling. One model that accommodates hierarchical data structures is the mixed-effects model. This study introduces a novel approach to predict food insecurity by integrating mixed-effects models and a generalized random forest. Mixed-effects models capture variations in hierarchical or clustered data, such as differences between regions, and the generalized random forest, as extended and developed from the traditional random forest, is integrated to model fixed effects and improve prediction accuracy. The empirical data used were the food insecurity data from 2021 in West Java, Indonesia. The results show that mixed-effects models with a generalized random forest significantly improve the prediction accuracy compared to other models. The average performance measure shows GMEGRF is a good model and has a balanced accuracy value of 0.6789709, which is the highest result compared to other methods. This methodological advancement offers a new robust model for understanding and potentially mitigating food insecurity, ultimately informing efforts towards SDG 2 (Zero Hunger).
TRADITIONAL LOGISTIC REGRESSION AND MACHINE LEARNING APPROACHES OF SOCIODEMOGRAPHIC AND ANTHROPOMETRIC FACTORS INFLUENCING HYPERTENSION IN ATHLETES Sofro, A'yunin; Maharani, Asri; Mustafidah, Mutia Eva; Khikmah, Khusnia Nurul; Oktaviarina, Affiati; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1125-1138

Abstract

The type and intensity of exercise performed by athletes play an important role in affecting blood pressure stability, putting them at risk of developing hypertension. Hypertension, or high blood pressure, is a medical condition in which the blood pressure in the arteries rises above normal limits. Hypertension in athletes becomes an essential factor in real cases if not detected early. Therefore, this study aims to model and analyse the sociodemographic and anthropometric factors that influence the incidence of hypertension. The data used in this study are primary data from 200 athlete selection participants at the University of Surabaya and the Indonesian National Sports Committee (INSC) of East Java. This research method proposes to compare the traditional approach with machine learning to prove the accuracy comparison of the model's goodness, where both approaches are proposed by considering the novelty proposed through the machine learning approach but still maximizing the traditional approach. The proposed methods are binary logistic regression, binary logistic regression with the addition of random effects, highly randomized tree, and support vector classification. The binary logistic regression model is better than the binary logistic regression model with random effects, random trees, and support vector classification because the accuracy, sensitivity, specificity, and F1-score value (68.5%, 69%, 68%, and 68.8%) is highest than the others. Other results showed that the waist circumference variable, the father's occupation variable, and the salary variable significantly affected hypertension at the 5% significance level.
HYBRID VECTOR AUTOREGRESSIVE AND LONG SHORT TERM MEMORY MODEL FOR PREDICTING ECONOMIC GROWTH INDICATORS IN INDONESIA: A COMPARISON OF ADAM, NADAM, AND RMSPROP OPTIMIZATION METHODS Ningrum, Ariska Fitriyana; Khaira, Mulil
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1139-1154

Abstract

This study aims to compare the performance of three optimization methods—Adam, Nadam, and RMSProp—in forecasting monthly economic indicators of Indonesia, namely the Consumer Price Index (CPI), Inflation, and Gross Domestic Product (GDP), using a hybrid Vector Autoregressive–Long Short-Term Memory (VAR–LSTM) model. The analysis begins with Vector Autoregression (VAR), where VAR(4) is selected as the best model based on the lowest Akaike Information Criterion (AIC) value of 1.075. Significant parameters from the VAR model are then used as input variables for the LSTM to enhance forecasting accuracy. The experimental results show that all three optimization methods generate similar prediction patterns, with forecasted values closely tracking the actual data. Nevertheless, the best optimizer differs across variables: Nadam performs best for CPI with a Root Mean Square Error (RMSE) of 0.4996, Adam yields the best performance for Inflation with an RMSE of 0.676, and RMSProp performs best for GDP with an RMSE of 1.288. Despite these variations, the overall forecasting performance of the three methods is comparable. These findings indicate that the VAR–LSTM approach can effectively capture the dynamic patterns of multiple economic variables and that the choice of optimization method should be aligned with the specific characteristics of the data, considering both accuracy and computational efficiency.
THE METRIC DIMENSION OF CYCLE BOOK GRAPHS B_(C_(m,n) ) FORMED BY A COMMON PATH P_2 Santoso, Jaya; Darmaji, Darmaji; Muliyana, Ana; Saragih, Asido
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1155-1166

Abstract

This paper investigates the metric dimension of a class of graphs known as cycle books, denoted ​, which feature a shared path ​ across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.This paper investigates the metric dimension of a class of graphs known as cycle books, denoted ​, which feature a shared path ​ across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.This paper investigates the metric dimension of a class of graphs known as cycle books, denoted ​, which feature a shared path ​ across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.
GENERALIZED NESTED COPULA REGRESSION TO UNVEIL THE IMPACT OF EXCHANGE RATES AND NIKKEI 225 ON BANK MANDIRI STOCK PRICE Khairiati, Alfi; Budiarti, Retno; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1167-1184

Abstract

Fluctuations in exchange rates and foreign stock indices strongly influence domestic stock performance, particularly in the banking sector, which is highly sensitive to global economic dynamics. Traditional financial models often fail to capture the complex, non-linear dependencies between these variables, underscoring the need for more advanced approaches. This study examines the effectiveness of copula-based regression models in predicting Bank Mandiri’s (BMRI) stock price using exchange rates and the Nikkei 225 Index as predictors. Conventional regression methods, such as Linear Regression, cannot adequately capture nonlinear relationships and tail dependencies in financial time series. To address this, we compare Elliptical Copula, Symmetric Archimedean Copula, Asymmetric Archimedean Copula, and Generalized Nested Copula models. Results show that the Generalized Nested Copula Regression achieves the lowest Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Weighted MAPE (wMAPE), effectively modeling asymmetric and tail dependencies that are crucial in financial forecasting. While Elliptical Copula (t-Copula) also provides strong predictive accuracy, Archimedean copulas perform poorly, failing to improve upon linear regression. These findings highlight the importance of flexible statistical models in financial prediction, demonstrating that copula-based regression offers a superior alternative to traditional methods. Unlike prior research that often relied on simpler copula families or linear models, this study introduces a Generalized Nested Copula Regression in the context of the Indonesian banking sector, addressing a gap in emerging market literature. The study assumes correctly specified marginal distributions and a stable dependency structure, which may limit applicability under rapidly changing market conditions. Future work should consider dynamic copula structures and additional economic indicators to further enhance predictive accuracy.
SPATIAL INTERPOLATION OF RAINFALL DATA USING COKRIGING AND RECURRENT NEURAL NETWORKS FOR HYDROLOGICAL APPLICATIONS IN SURABAYA, INDONESIA Ariyanto, Danang; Sofro, A'yunin; Puspitasari, Riskyana Dewi I; Romadhonia, Riska Wahyu; Ombao, Hernando
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1185-1198

Abstract

Urban hydrological challenges, such as flooding and water resource management, require accurate rainfall data to support sustainable development. This study investigates the use of Recurrent Neural Networks (RNN) for spatial interpolation of monthly rainfall data across 31 districts in Surabaya, Indonesia, and compares its performance with the geostatistical method Cokriging. Elevation data were incorporated as an additional variable to account for geographical variability. The dataset was divided into training (26 locations) and testing (5 locations) subsets, with testing locations treated as missing data points to simulate real-world conditions. The results show that the RNN-based interpolation method achieved progressively lower Root Mean Square Error (RMSE) values from January (48.65) to April (13.78), indicating higher accuracy compared to the Cokriging method. These findings underscore the potential of RNN in addressing data gaps and spatial variability, offering robust solutions for hydrological applications in urban environments. This approach not only supports flood risk mitigation strategies but also contributes to optimizing drainage systems and water resource planning. Further research is recommended to incorporate additional environmental variables and extend the application to broader spatial and temporal contexts.
TREE-BASED MIXED EFFECTS MODELING OF TEACHER CERTIFICATION OUTCOMES IN MADRASAH ALIYAH: A COMPARATIVE STUDY OF GLMM TREES AND GMET Syarip, Dodi Irawan; Notodiputro, Khairil Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1199-1214

Abstract

The Teacher Professional Education program, or “Pendidikan Profesi Guru” (PPG), is a continuing education program designed for prospective or in-service teachers to obtain a teaching certificate. PPG is a priority program of the Ministry of Religious Affairs in providing competent and professional madrasah teachers. This study is expected to identify the challenges encountered in the implementation of the Madrasah teacher certification program and provide valuable input to enhance the success rate of Madrasah Aliyah teachers in the PPG program. The main objective of this study is to find the most appropriate tree-based mixed effects model to analyze the effectiveness of PPG for Madrasah Aliyah teachers in 2022. This study applies two tree-based mixed effects modeling methods: generalized linear mixed model trees (GLMM trees) and generalized mixed effects trees (GMET). Both methods model variability across subjects as a random effect. Based on the performance indices measurement results, the GMET model shows superiority over the GLMM trees model. The GMET model has an accuracy index of 0.7653, higher than the GLMM trees model of 0.7306. Substantively, teachers of English and Indonesian Language exhibit higher probabilities of passing than those of other subjects, whereas Arabic and Islamic Cultural History have the lowest estimated probabilities of success. Analysis of the variable importance from both models indicates that teachers’ age is the most influential predictor of PPG graduation among Madrasah Aliyah teachers. Based on these findings, to improve the effectiveness of PPG implementation for madrasah Aliyah teachers, policymakers at the Ministry of Religious Affairs are advised to implement a structured coaching and mentoring program for prospective PPG participants, with a special emphasis on support for senior teachers specializing in Arabic and Islamic Cultural History.

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