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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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ASYMPTOTIC DISTRIBUTIONS OF ESTIMATORS FOR THE MEAN AND THE VARIANCE OF A COMPOUND CYCLIC POISSON PROCESS Adriani, Ika Reskiana; Mangku, I Wayan; Budiarti, Retno
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0453-0464

Abstract

A stochastic process has an important role in modeling various real phenomena. One special form of the stochastic process is a compound Poisson process. A compound Poisson process model can be extended by generalizing the corresponding Poisson process. One of them is using a cyclic Poisson process. Our goals in this research are to determine the asymptotic distribution of the estimator for the mean and the variance of this process. In this paper, the problems of estimating the mean function and the variance function of a compound cyclic Poisson process are considered. We do not assume any parametric form for the intensity function except that it is periodic. We also consider the case when only a single realization of the cyclic Poisson process is observed in a bounded interval. Consistent estimators for the mean and variance functions of this process have been proposed in respectively. This paper introduces a set of novel theorems that, to the best of our knowledge, are not available in the existing literature and contribute original results to the field. Asymptotic distributions of these estimators are established when the size of the observation interval indefinitely expands. Asymptotic distributions of and are, respectively and as .
INDOOR ACTIVITY RECOGNITION AND DEMENTIA RISK DETECTION USING Wi-Fi RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) AND NAIVE BAYES CLASSIFICATION Rubiani, Hani; Samsoleh, Eddy; Fitri, Sulidar; Taufiq, Muhammad; Amir Fazamin, Wan Mohd
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0465-0480

Abstract

Increasing life expectancy has resulted in a growing elderly population, making neurodegenerative conditions such as dementia a major global health issue. One of the main behavioral symptoms of dementia is wandering, which is characterized by repetitive and purposeless movement. Activity Recognition (AR) technologies, particularly those based on Wireless Sensor Networks (WSN), have gained attention for monitoring human behavior. Among these, Wi-Fi-based tracking using the Received Signal Strength Indicator (RSSI) offers a promising method for indoor activity monitoring and localization. This study aims to monitor the daily routines of elderly individuals, classify their current activity patterns by comparing them with previously recorded behaviors, and track their locations using Wi-Fi RSSI. A Naïve Bayes algorithm is proposed for activity classification and location tracking, while a time-based behavior graph is used to detect potential wandering behavior, aiding in early dementia risk assessment. The research utilizes primary data, which were collected directly through experiments in a controlled indoor environment. The data source comprises RSSI signals obtained from elderly participants. A purposive sampling method was employed to select participants aged 60 years and above, who were physically capable of performing the required tasks. A total of 4150 RSSI data samples were collected and analyzed. The proposed Naïve Bayes model achieved a classification accuracy of 64.60% using cross-validation, with a minimum average localization error of 0.7 meters, demonstrating the potential of this approach for early detection of dementia-related wandering behavior.
NEW CONJUGATE GRADIENT METHOD FOR ACCELERATED CONVERGENCE AND COMPUTATIONAL EFFICIENCY IN UNCONSTRAINED OPTIMIZATION PROBLEMS Hassan, Basim A.; Ibrahim, Alaa Luqman; Ameen, Thaair A.; Sulaiman, Ibrahim Mohammed
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0481-0492

Abstract

Conjugate gradient (CG) algorithms play an important role in solving large-scale unconstrained optimization problems due to their low memory requirements and strong convergence properties. However, many classical CG algorithms suffer from inefficiencies when dealing with complex or ill-conditioned objective functions. This paper addresses this challenge by proposing a new conjugate gradient method that combines the descent direction of traditional CG algorithms with Newton-type updates to improve convergence and computational efficiency. The proposed method is constructed to ensure sufficient descent at each iteration and global convergence under standard assumptions. By integrating the modified Newton update mechanism, the method effectively accelerates convergence without incurring the high computational cost typically associated with full Newton methods. To evaluate the performance of the proposed approach, we conducted extensive numerical experiments using a collection of well-known benchmark functions from the CUTEr test suite. The results show that the new method consistently outperforms the classical Hestenes-Stiefel method in terms of CPU time, number of function evaluations, and iteration count. These findings confirm the method’s potential as an efficient and robust alternative for solving large-scale unconstrained optimization problems.
VISUALIZING AND CLUSTERING FAKE JOB POSTINGS: DATA-DRIVEN INSIGHTS FOR FRAUD DETECTION Ch’ng, Chee Keong; Wong, Xiang Yi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0865-0880

Abstract

Online job platforms have made it easier to find jobs, but they have also made it easier for scammers to post fake job postings, posing risks to job seekers. These fraudulent activities can lead to severe consequences, such as identity theft, financial loss, and emotional distress for victims. To improve recruitment platform security and safeguard users, it is essential to spot trends in these fake job postings. This study focuses on visualizing patterns within fake job postings through data-driven insights, employing various data visualization techniques to reveal key attributes associated with fraudulent activity. A dataset contains both legitimate and fraudulent job postings. Exploratory data analysis (EDA) is conducted to examine variables including salary category, job function, industry, location, and other related features by using categorical distribution, geographical distribution, and word cloud. This study provides insights for recruitment platform controllers, raises user awareness, and facilitates the early detection of fraudulent job posts by displaying clear and actionable visual patterns. The results highlight how visualization and clustering are used to gain insight into characteristics of fraudulent job postings, like the fraudulent job postings predominantly target customer-facing roles in industries like Oil & Energy and Customer Service, which are concentrated in the United States (especially Texas and California), and rely on vague language and unrealistic promises. These findings contribute to more targeted fraud detection strategies and create safer online job search environments.
BIVARIATE POISSON LOG-NORMAL REGRESSION MODELING ON THE NUMBER OF LEPROSY CASES IN INDONESIA Sirajang, Nasrah; Rahmadhani S, Salsabila; Siswanto, Siswanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0493-0508

Abstract

Bivariate Poisson regression is a method for modeling two correlated count response variables. However, standard Poisson models often assume equidispersion, which is frequently violated in real-world data due to overdispersion. To address this issue, the Bivariate Poisson Log-Normal Regression (BPLNR) model is employed, which incorporates random effects to account for variability beyond that captured by the Poisson distribution. This study applies the BPLNR model to analyze the number of leprosy cases in Indonesia in 2021, categorized by the World Health Organization (WHO) into Paucibacillary (PB) and Multibacillary (MB). These two types are known to be correlated and exhibit overdispersion, rendering standard Bivariate Poisson models inadequate. This research contributes by applying BPLNR to leprosy data in Indonesia—an area that has been underexplored in prior studies, which largely employed univariate or standard Poisson approaches and ignored the correlation and overdispersion structure. Data were obtained from the 2021 Indonesian Health Profile and the Central Statistics Agency. Parameter estimation was conducted using Maximum Likelihood Estimation (MLE) with the Newton-Raphson algorithm, and hypothesis testing was performed using the Maximum Likelihood Ratio Test (MLRT). The results confirm that BPLNR effectively models the joint distribution of PB and MB cases while accounting for overdispersion. Key factors influencing both types of leprosy include population density, poverty rate, access to proper sanitation and drinking water, and availability of medical personnel and health facilities. A limitation of this study is the use of aggregate provincial-level data, which may obscure local variation and spatial effects. Future research could integrate spatial modeling techniques or individual-level data to enhance inference.
HYBRID METHOD IMPLEMENTATION: FUZZY DECISION TREE IN THE CLASSIFICATION OF GENDER INEQUALITY Irawan, Siti Fatimah Marliany; Anggraini, Dewi; Annisa, Selvi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0509-0522

Abstract

The classification of continuous data using the C4.5 decision tree algorithm requires prior discretization based on the calculation of cut points, a process that can be time-consuming and potentially introduce bias. These limitations may negatively impact both the computational efficiency and the classification accuracy of the decision tree model. This study proposes a hybrid method that integrates fuzzy logic with decision tree techniques in the classification process of continuous data types. Fuzzy logic is utilized to manage uncertainty in data variables and enhance flexibility in processing continuous values, while the decision tree plays a role in providing a structured and rule-based framework for decision-making. This proposed method is applied to gender inequality data, encompassing aspects of reproductive health, education and empowerment, and employment across 166 countries worldwide. The results demonstrate that the fuzzy decision tree method, which was constructed using the C4.5 algorithm, achieved a classification accuracy of 91%, while the C4.5 decision tree method without fuzzy only achieved a classification accuracy of 77%. The fuzzy decision tree method successfully improved the classification accuracy by 14%. Additionally, the fuzzy decision tree exhibited more stable and balanced performance in classifying data into four target categories. Therefore, this approach offers an effective and comprehensive alternative for classifying gender inequality, with the potential to support data-driven and targeted policy-making.
MODELING THE DURATION OF MATERNAL LABOR AT ANUTAPURA HAMMER HOSPITAL USING LIN-YING ADDITIVE HAZARD REGRESSION Fadjryani, Fadjryani; Setiawan, Iman; Sain, Hartayuni; Fajri, Mohammad; Gamayanti, Nurul Fiskia; Radi, Aryani; Aisya, Cici
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0523-0540

Abstract

The Central Sulawesi government has a Sustainable Development Goals (SDGs) target for 2020-2024, which sets the maternal mortality rate below 70/100,000 KH. However, in 2018-2022, the maternal mortality rate fluctuated by 128/100,000 KH. One of the factors causing maternal mortality is the duration of the labor process. The factors that are thought to have an influence on the duration of labor are gestational age, maternal age, baby height, parity, and hemoglobin levels. Therefore, this study aims to see what modeling and factors affect the duration of birth using Lin-Ying additive hazard regression analysis. Data were obtained from the medical records of normal deliveries between January and December 2023 at Anutapura Palu Hospital. The results showed that the factors that affect the duration of birth are preterm gestational age, aterm gestational age, maternal age 20-35 years, primigravida mothers, multigravida mothers, and mothers who are not anemic. A limitation of this study is the relatively short data collection period of one year, which may not capture variations or trends in labor outcomes over time.
ENHANCING VOLATILITY MODELING WITH LOG-LINEAR REALIZED GARCH-CJ: EVIDENCE FROM THE TOKYO STOCK PRICE INDEX Nugroho, Didit Budi; Putri, Zefania Sasongko; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0881-0894

Abstract

This study compares the Log-linear Realized GARCH (LRG) and its extension with Continuous and Jump components (LRG-CJ) in modeling the volatility of financial assets, using daily data from the Tokyo Stock Price Index (TOPIX) over 2004–2011. The urgency arises from the need for more accurate volatility models during turbulent periods such as the 2008 Global Financial Crisis and the 2011 Great East Japan Earthquake, where markets exhibit both smooth fluctuations and abrupt jumps. Methodologically, the LRG-CJ framework introduces a novel integration of continuous and jump decomposition into the LRG structure, offering an applied innovation to high-frequency volatility modeling. Realized Volatility (RV) was calculated from 1-, 5-, and 10-minute intraday data and decomposed into continuous and jump components. Parameter estimation employed the Adaptive Random Walk Metropolis (ARWM) within a Markov Chain Monte Carlo algorithm, while model performance was assessed using multiple information criteria and out-of-sample forecast evaluations. The empirical results reveal that incorporating continuous and jump components improves volatility modeling accuracy, forecasting, and Value-at-Risk estimation. However, these benefits are frequency-dependent: the LRG-CJ model shows superior in-sample fit for 1-minute RV but provides the strongest out-of-sample forecasting and risk prediction at lower frequencies (5- and 10-minute intervals). This highlights that while jumps are best identified at ultra-high frequencies, their predictive value is most effectively captured in slightly aggregated data. The originality of this study lies in being the first empirical application of LRG-CJ, demonstrating how continuous–jump decomposition interacts with the dual-equation structure of LRG, which has not been examined in TGARCH or APARCH contexts. Limitations include sensitivity to microstructure noise in very high-frequency data and computational challenges in parameter convergence. Overall, the findings underscore the novelty and practical importance of the LRG-CJ framework for risk management, offering actionable guidance for aligning volatility models with data frequency
CHAOS CONTROL IN PERMANENT MAGNET SYNCHRONOUS MOTOR BY SLIDING MODEL CONTROLLER WITH LYAPUNOV OBSERVER UNDER UNKNOWN INPUTS Hamidzadeh, Seyed Mohamad; Aziz, Amiral; Mohamed, Mohamad Afendee; Vaidyanathan, Sundarapandian; Johansyah, Muhamad Deni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0541-0556

Abstract

The control of chaotic and hyper-chaotic systems represents a crucial area of research in the field of nonlinear dynamic systems. In this study, we focus on applying chaos control techniques to a permanent magnet synchronous motor (PMSM), a system known to exhibit chaotic behavior under certain conditions. To achieve this, a sliding mode control (SMC) strategy integrated with a Lyapunov-based observer is proposed. The core concept involves designing a candidate Lyapunov function that governs the application of the control law, ensuring system stability while effectively suppressing chaotic dynamics. Through numerical simulations, the proposed sliding mode controller demonstrates its effectiveness in rapidly eliminating chaotic behavior and stabilizing the PMSM system toward a predefined reference trajectory. Notably, the system achieves error convergence within approximately 0.7 seconds under full control (four channels). When control channels are reduced to two, the system still maintains stability, showing flexibility and cost efficiency. In a further simulation, the chaotic PMSM is subjected to two unknown external disturbances, and the proposed controller continues to maintain stability with only a slight increase in convergence time. These quantitative results affirm the robustness, accuracy, and practicality of the proposed control method. This research confirms that integrating sliding mode control with a Lyapunov observer is an effective approach for chaos suppression in PMSMs, offering promising insights for the development of advanced control strategies in nonlinear electromechanical systems.
ANALYSIS OF APRIORI AND K-NEAREST NEIGHBOR (KNN) ALGORITHM IN RECOMMENDING APPROPRIATE LEARNING METHOD Azizah, Nuril Lutvi; Eviyanti, Ade; Ariyanti, Novia; Wardani, Gita; Diba, Naila Farah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0557-0572

Abstract

The study investigates the utilization of data mining techniques, especially the Apriori algorithm and K-Nearest Neighbor (KNN) classification, in recommending appropriate learning methods based on student data. The purpose of this research is to analyze patterns and groupings in students’ behavior, preferences, and academic performance to support more informed and personalized educational strategies. The Apriori algorithm is used to identify frequent associations among learning related attributes, while KNN classification helps group students with similar learning characteristics. The analysis revealed that the digital learning method is the most preferred by students, with a percentage of 84.29%, followed by the traditional lecture method at 15.70%. These results reflect a notable trend toward technology-driven, flexible learning environments, although conventional approaches continue to hold relevance for a portion of learners. The research concludes that the integration of the Apriori algorithm and KNN clustering proves to be an effective analytical framework for facilitating adaptive learning. This approach allows educators and institutions to make data-driven decisions in tailoring instructional methods that align with the diverse needs and preferences of students.

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