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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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GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION AND VALUE AT RISK ANALYSIS OF PT BANK NEGARA INDONESIA STOCKS Zakiah, Ainun; Sulistianingsih, Evy; Satyahadewi, Neva
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp617-628

Abstract

Investments in stocks are made to make a profit, where the higher the expected profit, the greater the risk undertaken. The return on investing in stocks can be influenced by changes in the price of stocks that are difficult to predict, which can lead to uncertainty in the value of the return and the risk of the stock. The application of the Geometric Brownian Motion (GBM) model with Jump Diffusion is crucial for enhancing the accuracy of stock price forecasting and risk analysis by incorporating price jumps resulting from external events within complex market dynamics. The data used in this study are the closing price data of the daily stock of PT Bank Negara Indonesia for the period 1 December 2022 to 31 January 2024, where the stock return data has a kurtosis value greater than 3 (leptokurtic) so that the data indicates a jump. The GBM with Jump Diffusion model was implemented to predict the stock price with a simulation repetition of 1000 times. The analysis shows that the GBM model with Jump Diffusion has an excellent accuracy rate with the smallest MAPE value of 0.86%. The average value of the VaR with Monte Carlo simulation obtained at the reliability levels of 80%, 90%, 95%, and 99% in a row is 0.96%, 1.53, 1.97%, and 2.64%. This result shows that the higher the confidence level used, the greater the risk.
PAIR MEAN CORDIAL LABELING OF HURDLE, KEY, LOTUS, AND NECKLACE GRAPHS Ponraj, R; Prabhu, S
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2795-2804

Abstract

Let be a graph with vertices and edges. Define and . Consider a mapping by assigning different labels in to the different elements of when is even and different labels in to elements of V and repeating a label for the remaining one vertex when is odd. The labeling as defined above is said to be a pair mean cordial labeling if for each edge of G, there exists a labeling if is even and if is odd such that | |≤1 where and respectively denote the number of edges labeled with 1 and the number of edges not labeled with 1. A graph G for which there is a pair mean cordial labeling is called pair mean cordial graph(PMC-graph). In this paper, we investigate the pair mean cordial labeling of some graphs like hurdle graph, lotus graph, necklace graph, F-tree, Y-tree, subdivided shell graph, uniform bow graph and key graph.
COMPARISON OF SURVIVAL ANALYSIS USING ACCELERATED FAILURE TIME MODEL AND COX MODEL FOR RECIDIVIST CASE Arfan, Nuraziza; Irfanullah, Asrul; Hamidi, Muhammad Rozzaq; Mukhaiyar, Utriweni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp629-642

Abstract

Recidivists, or ex-prisoners who commit crimes after serving a prior sentence, pose a critical challenge to the criminal justice system. This study examines social and economic factors that may reduce the likelihood of recidivists being re-arrested. Using survival analysis, the probability that a recidivist could survive in society without being re-arrested could be estimated. The purpose of this work is to compare the AFT and Cox models to determine which provides a better fit to identify factors affecting the likelihood of re-arrest within one year after release and to statistically assess the impact of these factors. This study utilizes a stratified Cox model to address variables that violate the proportional hazards (PH) assumption. The analysis is limited to four types of AFT models: Weibull, log-normal, log-logistic, and exponential. Results show that the stratified Cox model provides the best fit, based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). This demonstrates the Cox model's robustness in analyzing survival data, accurately approximating the distribution of survival times without restrictive assumptions, unlike AFT models. The study reveals that recidivists who received financial aid upon release have a lower risk of re-arrest compared to those who did not, and each additional prior theft arrest increased the risk of re-arrest by 1.09193 times.
3D MODELING COMPUTATION TO EVALUATE GROYNE STRUCTURE PERFORMANCE: CASE STUDY OF PASSO COASTAL AREA Salamena, Ganisa Elsina; Salamena, Gianita Anastasia; Loupatty, Grace; Palembang, Citra Fathia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp643-654

Abstract

Groyne is very important to protect the coastline with the concept of maintaining the balance of sediment transport. Groyne building in theory can work well if worked in groups or more than one. In this study, the Passo beach location was chosen because there is an existing groyne building that, if seen on Google Earth, has been damaged by the scattering of the constituent rocks. If the groyne cannot work to balance the sediment transport, it may occur that mass destruction to the infrastructure behind the groyne itself, such as regional roads, may occur. To find out the level of damage, an in-depth study needs to be carried out. In this paper, Delft-3D mathematical modeling was carried out to investigate groyne damage by looking at the performance of groyne in maintaining the balance of sediment transport in the Passo beach area. Hydrodynamic and coastal sediment modeling analyses were carried out in wet and dry season conditions. Modeling was carried out over one month with a morphology factor of 12 to obtain sediment transport for one year. In the existing dry season conditions, it shows that at the observation point, there is erosion of 2 meters, and in the wet season sediment transport is balanced. It is implied that the groyne structure must be replaced for being surpass the structure lifetime.
COMPARATIVE ANALYSIS OF TWO-STEP AND QUASI MAXIMUM LIKELIHOOD ESTIMATION IN THE DYNAMIC FACTOR MODEL FOR NOWCASTING GDP GROWTH IN INDONESIA Souisa, Gilbert Alvaro; Leiwakabessy, Reyner M.; Damayanti, Salma; Terim, Mohammad Zanuar F; Pelu, Shelma M
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp655-664

Abstract

Economic activity data is needed quickly to make policy decisions, but this data suffers from publication delays. Gross Domestic Product (GDP) data is released within five weeks after the end of the quarter. An effort that can be made to provide such data is through nowcasting, which is forecasting in the current period using variables that have a higher frequency. This study aims at nowcasting GDP growth. The nowcasting method used is the Dynamic Factor Model (DFM) with Two Step (TS) and Quasi Maximum Likelihood (QML) estimation. The nowcasting results show that the DFM-TS model is better than the DFM-QML because it has a larger adjusted R-squared value and has the smallest RMSE value of 1.71035 compared to the DFM-QML value, which has an RMSE value of 1.71598.
IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH Sumin, Sumin; Prihantono, Prihantono; Khairawati, Khairawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp665-674

Abstract

Manual classification of Single Tuition Fees (STF) has a high risk of misclassification due to the need for a more in-depth assessment of students' economic criteria. This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correct errors in Single Tuition Fee (STF) classification. This study aims to apply the NN model to identify and correct classification errors in the STF clustering of State Islamic Religious Universities in Indonesia (PTKIN). This research was conducted using exploratory methods and quantitative approaches involving a population of PTKIN students throughout Indonesia. A sample of 282 respondents was selected using a simple random sampling method. The results showed that NN-MLP is an effective tool for identifying and correcting misclassification in determining PTKIN tuition fees with significantly improved classification accuracy characterized by an accuracy value of 71.28% and MSE of 0.287; this model can be used as a basis for developing information systems that are fairer and more accurate in managing tuition fees in higher education. This research also proves that the NN method is superior to traditional statistical methods and simple machine learning in handling complex and diverse data. In addition, the Random Forest model successfully identified the most influential input variables in STF classification. Father's occupation, mother's occupation, number of dependents, and utility bills such as water and electricity significantly contributed to the STF classification. In contrast, variables such as vehicle facilities showed a lower contribution.
IMPLEMENTATION OF FEATURE IMPORTANCE XGBOOST ALGORITHM TO DETERMINE THE ACTIVE COMPOUNDS OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) Kusnaeni, Kusnaeni; Adhalia, Nurul Fuady; Zulfattah, Abdul Khaliq
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp675-686

Abstract

Sembung is a medicinal plant native to Indonesia that grows optimally in tropical climates. The secondary metabolite compounds found in the leaves of sembung are biopharmaceutical active ingredients. Fourier Transform Infrared (FTIR) spectroscopy can identify the functional compounds in sembung leaves by analyzing unique peaks in the spectrum, which correspond to specific functional groups of the compounds. In this research, 35 observations were made with 1,866 explanatory variables (wavelengths). Data in which the number of explanatory variables surpasses the number of observations is known as high-dimensional data. One method that can handle high-dimensional problems is to select important variables that affect the objective variable. The XGBoost algorithm can calculate the feature importance score that affects the goal variable so that it does not have to include all variables in the modeling, this can overcome problems in high-dimensional data. The results of the calculation of feature importance found Lignin Skeletal Band, CH, and CH2 aliphatic Stretching Group, C=C, C=N, C–H in ring structure, DNA and RNA backbones, NH2 Aminoacidic Group, C=O Ester Fatty Acid that the active compounds contained in the leaves of sembung.
MODELING CUSTOMER LIFETIME VALUE WITH MARKOV CHAIN IN THE INSURANCE INDUSTRY Mahdiyasa, Adilan Widyawan; Pasaribu, Udjianna Sekteria; Sari, Kurnia Novita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp687-696

Abstract

In the competitive insurance industry, accurately predicting Customer Lifetime Value (CLV) is vital for sustaining long-term profitability and optimizing resource allocation. Traditional static models often fail to capture the dynamic and uncertain nature of customer behavior, which is influenced by factors such as life changes, economic conditions, and evolving product offerings. To address these limitations, this paper proposes an advanced modeling approach that integrates Markov Chains with survival analysis. Markov Chains are well-suited for modeling stochastic processes, where future states depend on current conditions, while survival analysis provides insights into event timing and likelihood for estimating the insurance premium. The proposed model combines these approaches to make a more complete and accurate prediction of CLV. This helps insurers make better decisions and improves the overall performance of their business. We employ the data of customer behavior from the insurance company in Bandung, Indonesia from 1994 to 2020. We found that CLV in the insurance industry is significantly affected by customer behavior.
NONPARAMETRIC REGRESSION MODELING USING THE SPLINE APPROACH TO STUNTING CASES IN INDONESIA Fatekurohman, Mohamat; Nur Khasanah, Siti; Setia Dewi, Yuliani
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/barekengvol19iss2pp697-708

Abstract

Indonesia is the fourth ranked country in the world and second in Southeast Asia with the highest stunting cases of 21.6%. According to the provisions of the World Health Organization (WHO), the maximum tolerance standard for stunted toddlers is 20 percent or one-fifth of the total number of toddlers, so the stunting rate in Indonesia is still relatively high. The high stunting rate in Indonesia can affect the quality of Indonesia's human resources, so early detection and immediate management of stunted toddlers are needed. Stunting is a condition of failure to grow due to chronic malnutrition which is caused by inadequate nutritional intake for a long time, resulting in being shorter than standard. This research aims to determine several factors that influence stunting in toddlers in Indonesia using the nonparametric spline regression method with one knot, two knots, three knots and the best model is found to be the one knot model. The results of regression nonparametric spline modeling with one knot are GCV of 14.32605 and of 81.1%. From the five variables, namely toddlers receiving complete basic immunization babies receiving exclusive breast milk for 6 months , babies born receiving IMD children aged 6-23 months consuming five of the eight food groups and drink throughout the day , households having access to proper sanitation , the following results were obtained: the variable that don’t have a significant effect was toddlers receiving complete basic immunization , while the other four has a significant effect.
PRICING EMPLOYEE STOCK OPTION USING TRINOMIAL TREE METHOD Lesmana, Donny Citra; Ramadhan, Reza Tri Ahmad; Nurjanah, Siti; Dharmawan, Vanaya Syahira
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/barekengvol19iss2pp709-720

Abstract

This study explores the Employee Stock Option (ESO) model proposed by Liao and Lyuu, which provides a robust framework for addressing critical factors such as dilution, early exercise, and employee forfeiture rates. The model is solved using the trinomial tree method, allowing for the consideration of three possible stock price movements: increase, unchanged, or decrease. This approach combines forward and backward calculations to accurately evaluate ESO values by accounting for the complex interactions of these parameters. Dilution effects are modeled by adjusting stock prices based on outstanding shares and strike prices, while early exercise probabilities are addressed using a modified Chi-Square distribution to represent employee behavior. Additionally, the forfeiture rate is dynamically adjusted based on ESO returns and the ratio of stock-to-strike prices. The analysis reveals that ESO price negatively correlates with strike price and forfeiture rate, whereas parameters such as vesting time, maturity date, risk-free rate, volatility, and the number of ESOs granted exhibit positive correlations. This comprehensive methodology demonstrates the practical applicability of the Liao and Lyuu model for real-world ESO valuation. By integrating these critical factors into a unified framework, the study contributes significantly to the literature on financial modeling and provides actionable insights for companies seeking to optimize their ESO programs.

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