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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 60 Documents
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Search results for , issue "Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
DEVELOPMENT OF CLUSTER INTEGRATION WITH VARIAN BASED STRUCTURAL EQUATION MODELING TO MANAGE HETEROGENEOUS DATA Sepriadi, Hanifa; Iriany, Atiek; Solimun, Solimun; Rinaldo Fernandes, Adji Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1193-1202

Abstract

In the application of SEM to multivariate data, the individuals collected not only come from the same population but also from several groups (clusters). This data is heterogeneous. When SEM is applied to heterogeneous data, there will be a risk of bias in estimating equations in the measurement and structural models because there are differences between groups in the data. The purpose of this study is to overcome heterogeneous data in modeling cashless behavior with cluster using a dummy approach. This study used primary data from a survey in Bekasi City using a questionnaire with 100 respondents. Based on the study's results, it is known that using clustering in SEM can overcome heterogeneous data, which is indicated by the high coefficient of determination of 96.12%. Banks can use the results of this study to design products and services that are more in line with customer needs and preferences while encouraging financial inclusion in the digital era.
JOINT DISTRIBUTION AND PROBABILITY DENSITY OF CLIMATE FACTORS IN KALIMANTAN USING NESTED COPULAS Nurdiati, Sri; Mas’oed, Teduh W.; Najib, Mohamad K.; Rahmawati, Dewi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1203-1216

Abstract

In this study, we investigate the joint distribution of local and global climate factors in Kalimantan, Indonesia, using fully and partially nested copula models. The analysis focuses on capturing the dependencies between local factors (precipitation and the number of dry days) and global indices (ENSO and IOD). The methodology involves estimating the marginal distributions of each variable using goodness-of-fit tests, and then modeling the dependence structure between variables with a range of copulas. We used both one-parameter copulas, including Gaussian, Clayton, Gumbel, Joe, and Frank, as well as two-parameter copulas, such as BB1, BB7, and BB8, with rotations of 90°, 180°, and 270° applied to account for negative dependencies. Nested copula structures were employed to model multivariate dependencies, with fully nested and partially nested approaches used to capture interactions between all four variables. The results show that global climate indices, particularly ENSO and IOD, have a more substantial influence during the dry season, impacting drought conditions in Kalimantan. The copula method offers a flexible and efficient way to construct multivariate joint distributions, better representing complex climate relationships than traditional models. Future work could extend this approach to include additional climate variables and use real-time data for forest fire risk prediction.
STRENGTHENING SYARIAH FINANCIAL MARKETS WITH GARCH-BASED STOCK PRICE FORECASTING AND VAR-RISK ASSESSMENT Darmanto, Darmanto; Darti, Isnani; Astutik, Suci; Nurjannah, Nurjannah; Lee, Muhammad Hisyam; Damayanti, Rismania Hartanti Putri Yulianing; Irsandy, Diego
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1217-1236

Abstract

Indonesia, as the largest Muslim-majority country, has significant potential to enhance its Shariah financial sector, which has been growing rapidly, around 7.43% from 2023 to 2024, and contributing to the national economy. However, political and natural disasters have influenced the economy and Shariah-compliant stocks. This study focuses on forecasting Shariah-compliant stock prices using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and estimating investment risks via Value at Risk (VaR) for four Islamic banks listed in IDX: BRIS, BTPS, BANK, and PNBS. The findings indicate that GARCH models effectively capture stock price dynamics and provide accurate 10-day forecasts. Additionally, the models reliably predict VaR, validated through backtesting at various confidence levels. These insights are valuable for financial regulators and risk managers, aiding in policy design to ensure market stability by enabling the implementation of measures such as stricter capital reserve requirements for institutions with high-risk exposure and mandatory adoption of advanced risk management techniques like dynamic stress testing. Such policies not only mitigate systemic risks during periods of financial volatility but also enhance the overall resilience and robustness of the financial system. For investors, accurate risk predictions support informed decision-making, enhance portfolio protection, and optimize risk management.
FORECASTING NICKEL PRICES WITH THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV APPROACH Haris, M. Al; Sari, Wulan; Fauzi, Fatkhurokhman; Sam'an, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1237-1250

Abstract

Nickel was a critical raw material used in a wide range of industries. The price movement of nickel tends to fluctuate and remain uncertain due to market conditions varying over time. Therefore, forecasting nickel prices was essential to understanding future price movements. In this study, we applied the automatic clustering fuzzy time series Markov chain method. The automatic clustering algorithm generates multiple intervals and fuzzy relations. Subsequently, forecasting was based on these fuzzy relations and a Markov chain transition probability matrix involving three stages to enhance forecast accuracy. We use monthly closing futures nickel price data from January 2009 to May 2024. The accuracy of the forecasting model was measured using the mean absolute percentage error (MAPE). The analysis showed that implementing the automatic clustering fuzzy time series Markov chain method results in excellent forecasting accuracy, with a MAPE value of 1.76% (equivalent to 98.24% accuracy). The predicted nickel price for June 2024 was US$ 19,608.5.
ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Novianti, Pepi; Crisdianto, Riki; Karuna, Elisabeth Evelin; Fairuzindah, Athaya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1251-1262

Abstract

Bengkulu Province ranks second in the category of the highest percentage of poor people in the Sumatra region, at 14.62% in March 2022, and sixth in Indonesia, which is undoubtedly one of the fundamental problems that requires mutual attention. The phenomenon of high poverty in Bengkulu Province is inseparable from the lives of people whose main livelihood is in the agricultural sector, especially tenant farmers. Therefore, in this study, the Copula and Gaussian Copula Marginal Regression (GCMR) methods are applied to determine how the agricultural sector affects poverty in Bengkulu Province using secondary data obtained from the Bengkulu Provincial Statistics Agency (Susenas 2022). The results show that the Copula model can identify various types of dependency between the number of poor households in each district/city in Bengkulu Province in 2022 and each of the variables, namely the Number of Agricultural Business Households , the Growth Rate of the Agricultural Sector , the Human Development Index , and the Open Unemployment Rate ( ) by considering the different characteristics of dependency such as top-tail, bottom-tail, or negative dependency. Meanwhile, the GCMR model can provide the direction of influence of the independent variables on the dependent variable Y, where it can be seen that the variables , , and have a negative influence on the variable , whie the variable has a positive impact on the variable . Therefore, in general, it can be concluded that either positive or negative dependencies identified by the Copula model can influence the resulting GCMR model by providing more profound complexity regarding the relationship between the variables analyzed.
A COMPARATIVE STUDY ON NUMERICAL SOLUTIONS OF INITIAL VALUE PROBLEMS OF DIFFERENTIAL EQUATIONS USING THE THREE NUMERICAL METHODS Marsudi, Marsudi; Darti, Isnani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1263-1278

Abstract

Numerical methods are crucial for solving ordinary differential equations (ODEs) that frequently arise in various fields of science and engineering. This study compares three numerical methods: the fourth-order Runge-Kutta method (RK4), the fourth-order Runge-Kutta Contra-harmonic Mean method (CoM4), and the fourth-order Adam-Bashforth-Moulton method (ABM4) in solving initial value problems of ODEs. Three IVPs of ODEs have been solved with varying step sizes using the three methods that have been proposed, and the solutions for each step size are examined. Numerical comparisons between RK4, CoM4, and ABM4 methods have been presented to solve three initial problems of ODE. Simulation results show that each method has advantages and limitations depending on the type of ODE being solved. We find that for very small step sizes, the numerical solutions agree the best with the exact solution. As such, all three proposed approaches are sufficient to solve the IVP ODE accurately and efficiently. Among the three proposed methods, we observe that the mean absolute error for the RK4 method is the smallest, followed by the ABM4 method.
GRAPHICAL REPRESENTATION AND TWO GROUPS ANALYSIS ON DATA MATRIX OF ROBUSTA GREEN CHERRIES PRODUCTION IN TWO HARVEST PERIODS Irmeilyana, Irmeilyana; Suprihatin, Bambang; Desiani, Anita; Ngudiantoro, Ngudiantoro; Maiyanti, Sri Indra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1279-1294

Abstract

Several factors that play a role in the productivity of Robusta coffee trees are the influence of pruning techniques and weather elements. This paper discussed the graphical analysis and comparison of two data matrices of Robusta green cherries production, which would enter the ripening process in branch categories for the harvest period in 2023 and 2024. Hypothesis testing on secondary data in the form of daily weather conditions in 2022 and 2023, which include temperature, dew, humidity, wind speed, and cloud cover for the two periods, was significantly different. However, solar radiation and precipitation were not. The data source for each harvest period was primary data, with the object being a sample of 30 trees that were sampled purposively. The research object was in Pagaralam Municipality, South Sumatra. There were 18 variables covering many branch categories based on production year, position, and shape. The PCA (Principal Component Analysis) results on each data matrix show similarities in the dominant variables representing each subspace. The first three PCs in each data matrix for 2023 and 2024 span a subspace and describe the variation of the original data of 77.3% and 68.8%, respectively. The 3rd and 1st-year production branch categories dominate the subspace of each data matrix for 2023 and 2024. Comparison of the two PC subspaces using two groups analysis in 3rd dimension space produces angles of 19.70, 28.80, and 69.10. The bisector components show that the variables that dominate the similarity of the two data matrices are the variables that tend to represent both PC subspaces dominantly. Robusta green cherry production can be represented by the number of secondary branches, which are straight in shape, along with the number of fruit clusters. This study result can be a reference for farmers when considering the composition of the number of branch categories when pruning.
STABILITY ANALYSIS AND PERFORMANCE OF KALMAN FILTERING AND ROBUST KALMAN FILTERING ON UNCERTAIN CONTINUOUS-TIME SYSTEMS Rudianto, Budi; Muhafzan, Muhafzan; Syafwan, Mahdhivan; Sy, Syafrizal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1295-1306

Abstract

This paper discusses the stability analysis of robust Kalman filtering on uncertain continuous-time systems. In real applications, systems often face model uncertainty and noise affecting prediction and estimation accuracy. Therefore, a filtering method is needed to overcome these uncertainties. Robust Kalman filtering is one of the most effective methods for dealing with model uncertainty. In this paper, we discuss the application of this method to continuous-time systems and its stability analysis. Simulation results show that robust Kalman filtering can provide more accurate and stable estimates than the conventional Kalman filter. Robust Kalman filtering can reduce the estimation error to about 30% under uncertain model conditions and maintain stability despite disturbances of up to 20% of the system parameters. However, this research has limitations regarding testing scenarios with more complex uncertainty models and higher disturbance variability. The originality of this research lies in its focus on the stability analysis of robust Kalman filtering on uncertain continuous-time systems, which has rarely been discussed in depth in previous literature.
ON THE SECURITY OF GENERALIZED MULTILINEAR MAPS BASED ON WEIL PAIRING Handayani, Annisa Dini; Wijayanti, Indah Emilia; Isnaini, Uha; Fauzi, Prastudy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1307-1316

Abstract

In 2017, Tran et al. proposed a multilinear map based on Weil pairings to realize the Boneh-Silverberg scheme. They proposed an algorithm to evaluate the Boneh-Silverberg multilinear map and showed that it could be used to establish a shared key in multipartite key exchange for five users. They claimed their scheme was secure and computable in establishing a shared key between 5 users. Unfortunately, they did not prove that their scheme meets three additional computational assumptions proposed by Boneh and Silverberg. In this paper, with some computational modifications, we show that the algorithm proposed by Tran et al. does not satisfy three security assumptions proposed by Boneh and Silverberg. Therefore, every user involved in this multipartite key exchange can obtain the shared key and other users' secret values. We also show that the computation to obtain a shared key is inefficient because it requires a lot of computation and time.
CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Herry Chrisnanto, Yulison; ID Hadiana, Asep; Kusumaningtyas, Valentina Adimurti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1317-1328

Abstract

The cryptocurrency market is experiencing rapid growth in the world. The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. Based on the gap in previous research, the selected model is only based on the accuracy value. In this study, the chosen model must fulfill two criteria: the best-fitting model based on the learning curve diagnosis and the model with the best accuracy value. The selected model is used to forecast the test data. Model selection with these two criteria has resulted in high accuracy in model performance. This research was highly accurate for all tested models with MAPE < 10%. The GRU 30-50 model is best tested with MAE = 867.2598, RMSE = 1330.427, and MAPE = 1.95%. Applying the sliding window technique makes the model accurate and fast in learning the pattern of time series data, resulting in a best-fitting model based on the learning curve diagnosis.

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