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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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ESTIMATION OF CLAIM RESERVES USING THE CHAIN LADDER METHOD Yoisangaji, Abdan Maulana Rohat; Pelu, Shelma Maharani; Wijaya, Jofie
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/barekengvol18iss4pp2083-2092

Abstract

One form and effort in dealing with all the risks that might occur in the future is insurance. In managing premiums paid by insurance participants, insurance companies must also consider unexpected claims that may occur in the future. Therefore, a method is needed that can be used by insurance companies to prepare claim reserves for the future. One method that is often used is the Chain Ladder method. This research aims to predict and estimate the claim reserves that insurance companies must prepare in the face of unexpected claims in the future. Based on the results of this study, the amount of claims that the company must prepare to deal with claims that may occur in 2020 is USD 1,110,879.00.
PID PATH FOLLOWING CONTROL SYSTEM DESIGN ON UNMANNED AUTONOMOUS FORKLIFT PROTOTYPE Herlambang, Teguh; Indriawati, Katherin; Akbar, Reza Maliki; Nurhadi, Hendro
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/barekengvol18iss4pp2093-2112

Abstract

One of the technologies often used in material handling and material transport is forklift. Conventionally forklifts are operated by human operators. For efficiency, to improve security, safety, and occupational health and to minimize the risk of operators, forklifts can be automated. Therefore, the idea of unmanned autonomous forklift innovation was introduced. In this final project, a motion control system was designed for an unmanned autonomous forklift prototype both in simulation and hardware using PID control techniques, and the odometry system was equipped with a rotary encoder. In the simulation, the controlled variable was the distance and the manipulated variable was the force on the vehicle. Meanwhile, in the prototype, the controlled variable was distance and the manipulated variable was the motor pulse. In the simulation stage the PID control parameters were applied . with an error of 0.32% in the simulation. The PID control parameters were applied to the prototype, that is, . The distance tests were done with variation of 50 cm to 200 cm (25 cm intervals). One variation of the distance experiment was done 5 times. The average absolute error resulted was 2.36 cm.
THE UTILIZATION OF STRUCTURAL EQUATION MODELLING TO DETERMINE CASUALITY ON CONSUMER DECISION OF E-WALLET Hartati, Hartati; Saluza, Imelda; Iisnawati, Iisnawati; Teguh, Teguh
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/barekengvol18iss4pp2113-2124

Abstract

One of the technological innovations that exemplifies transactional innovation is the use of an E-Wallet as a means of carrying out financial transactions. This invention enhances transactional ease and quickness. The E-Wallet is the most popular means of financial transaction among the general population. The traditional marketing mix has developed into the product, price, place, promotion, personalization, privacy, customer service, community, site, security, and sales promotion (4Ps+P2C2S3). These criteria are used to evaluate their efficacy in persuading customers to adopt an E-Wallet. Structural Equation Modeling (SEM) is used to assess the causal link between e-marketing mix items and customer decisions to use E-Wallet. SEM provides the core capability of validating the validity and reliability of procedures such as Confirmatory Factor Analysis (CFA). by investigating the pattern of linkages between latent variables and showing them with route diagrams. This study employed 200 E-Wallet users in Palembang for modeling. with the criterion of having one of the E-Wallet programs on a smartphone and having used the application at least once. According to the results of the consumer response processing. all constructs have Cronbach's Alpha (CA) > 0.60, indicating that all constructs are valid for use in measurement. As do the Average Variance Extracted (AVE) and Composite Reliability (CR) values > 0.70, indicating that the construct explains more than half of the variance coming from the indicators. Furthermore, we show the results of evaluating the causal relationship between endogenous construct elements that influence customer decisions, such as location, security, and sales promotion.
ANALYSIS OF ECOTOURISM AND DISASTER MITIGATION DATA AT THE KUALA LANGSA MANGROVE FOREST BY USING THE ANALYTIC HIERARCHY PROCESS Arif, Salmawaty; Akmalia, Nanda
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/barekengvol18iss4pp2125-2136

Abstract

The problem in the management of a public area is to determine aspects that contribute most as its best practices. This paper explores the contributions of the Kuala Langsa Mangrove Forest located in Langsa City, Aceh Province, for ecotourism and disaster mitigation. This research was intended to analyze the aspects of forest management that has successfully reformed dry coast into an ecotourism site. The Analytic Hierarchy Process (AHP) as a multicriteria decision making method was used to analyze the best indicators data. This method was selected as the suitable method since it utilizes the objectivity of mathematics in comparing some subjective criteria to obtain the best indicators. The implementation of the AHP method consists of three stages: the development of the criteria’s hierarchy table; the development of pairwise matrix related to the intention of giving weights for the criterion’s elements; the logical concistency verification of the matrix. Indicators were written on a five point scale related to a value judgement. The results of the research show that as an ecotourism site, the activities of the mangrove forest are motivated and based on the existing rules which attract people to become interested. The role of the local government has resulted: developed the programs according to the existing mangrove management law that reached 67% among other law related indicators; budget allocation priority by self-generating which get priority of 59% among budget indicators; oriented on the increasing of fishermen, obtained 49% of economical function elements; encouraged the involvement and partnership of the local resources around the mangrove forest which reached 31% among other programs offered and sustainability approach by considering ecological functions aspect. For physical contribution for disaster mitigation, the largest dissipation is given by Rhizophora mucronata due to its height compared to the other types.
MODELING THE BENEFITS OF A MARRIAGE REVERSE ANNUITY CONTRACT WITH DEPENDENCY ASSUMPTIONS USING ARCHIMEDEAN COPULA Lundy, Arnhilda Aspasia; Novita, Mila; Fithriani, Ida
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/barekengvol18iss4pp2137-2152

Abstract

Social security benefits may not be enough for retirement. Equity release products like marriage reverse annuities can boost retirement income for older couples. Marriage reverse annuity’s contract convert all or part of the real estate value of elderly spouses while they are living (joint life status) or after one partner dies (last survivor status). Since husband and wife face the same death risk, the chance of death between spouses is believed to be dependent for realism. Thus, copula models the future dependency model of a husband and wife. Sklar's theorem states that copulas link bivariate distribution and marginal cumulative functions. One of the most common copulas is Archimedean copula. Clayton, Gumbel, and Frank are Archimedean copula that will be used in this investigation. The Indonesian Mortality Table IV data is used to obtain the marginal distribution of the male and female which will then be used to construct copulas (Clayton, Gumbel, and Frank) that combine two marginal distributions into a joint distribution. The marginal distribution of Indonesian Mortality Table IV is uncertain, hence Canonical Maximum Likelihood parameter estimation is utilized to estimate the parameter of copulas. Multiple-state models depict the marriage reverse annuity model for joint life and last survivor status. The probability structure is based on Sklar's theorem and copula survival function. The contract benefits calculation utilizing copulas (Clayton, Gumbel, and Frank) shows that joint life status benefits are higher than last survivor status. Joint life status uses the dependence assumption with Frank's copula to calculate the smallest annual benefit value of a marriage reverse annuity contract, while last survivor status uses the independence assumption (without copula).
COMPARISON OF NAÏVE BAYES AND K-NEAREST NEIGHBOR MODELS FOR IDENTIFYING THE HIGHEST PREVALENCE OF STUNTING CASES IN EAST JAVA Herlambang, Teguh; Asy'ari, Vaizal; Rahayu, Ragil Puji; Firdaus, Aji Akbar; Juniarta, Nyoman
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/barekengvol18iss4pp2153-2164

Abstract

Indonesia will experience a demographic bonus in 2030, where the productive age group will dominate the population and become a buffer for the economy. However, this potential is in vain if human resources experience stunting. According to WHO (2015), stunting is a disorder of child growth and development due to chronic malnutrition and repeated infections, characterized by below-standard length or height. Based on the background of the problem, the author wants to compare the prediction of the prevalence of the highest stunting cases in East Java using the Naive Bayes method and the K-Nearest Neighbor method. The stages carried out in this study are data collection, initial data processing, advanced data processing using the Naïve Bayes Method and K-Nearest Neighbor, and comparative analysis. The results of the implementation of the Naïve Bayes and K-Nearest Neighbor methods are in the form of stunting prevalence prediction charts with variables that affect LBW and TTD. The results of simulations conducted in 6 regions, the Naive Bayes method gets the highest accuracy value of 83.33% in simulation one and 66.67%. The smallest RMSE value is 0.382 simulation 1 and 0.469 simulation 2. This shows that the Naive Bayes method can predict well.
IMPLEMENTATION OF CROSS-VALIDATION ON HANG SENG INDEX FORECASTING USING HOLT’S EXPONENTIAL SMOOTHING AND AUTO-ARIMA METHOD Sucipto, Christy Sheldy; Sulandari, Winita; Susanti, Yuliana
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/barekengvol19iss1pp13-24

Abstract

This study applies a rolling window cross-validation to evaluate the multi-step forecasts instead of using the traditional single split for Hang Sheng Index (HSI) forecasting. The forecasting methods discussed in this study are Holt's Exponential Smoothing and auto ARIMA, chosen because of their ability to model trend data as in the daily HSI. This research aims to evaluate up to five step forecast values obtained by the two forecasting methods built in the training data with rolling window cross-validation. In the experiment, each of the 21 auto ARIMA and Holt's models was constructed from 84 observations (as in-sample data) obtained from the rolling window cross-validation. The one to five step forecast values of daily HSI are then calculated using those models, and the accuracy of each forecast value is evaluated based on Mean Absolute Percentage Error (MAPE). The results show that the Auto ARIMA model produces a lower MAPE value than Holt's model, namely 2.9196%, 4.6553%, 6.4012%, 8.3083%, and 10.3781%, respectively, for one to five steps ahead. Therefore, auto ARIMA is more recommended for forecasting HSI values up to five steps ahead than Holt's method.
MODEL SELECTION FOR B-SPLINE REGRESSION USING AKAIKE INFORMATION CRITERION (AIC) METHOD FOR IDR-USD EXCHANGE RATE PREDICTION Pratiwi, Indriani Wahyu Nur; Zuliana, Sri Utami
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/barekengvol19iss1pp25-34

Abstract

Exchange rate data is a collection of information about the exchange rate the foreign currency which collected by time. Autoregressive Integrated Moving Average (ARIMA) is a well-known time series analysis. Several assumptions that need to be checked before running the ARIMA model are stationarity, normality, and white noise. B-spline regression is a method of modeling time series data without considering assumptions. This research aims to create a forecasting model for Rupiah exchange rate against US Dollar using B-spline regression. The B-spline regression model was generated with a combination of degrees two to four and a maximum of four knots. After that, the optimal model is selected using the Akaike Information Criterion (AIC) score. The performance of the selected model is validated using Mean Absolute Percentage Error (MAPE) values. The optimal degree is 3 (quadratic) and the optimal number of knot points is two-knot points with an AIC value of 857.8322 and a MAPE value of 0.0148376. The best model is:
PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE Hariyanto, Susilo; Wibawa, Salsabila Gustia; Solikhin, Solikhin
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/barekengvol18iss4pp2165-2180

Abstract

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.
COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 Saifudin, Toha; Salsabila, Fatiha Nadia; Fitriani, Mubadi'ul; Kholidiyah, Azizatul; Auliyah, Nina; Ariani, Fildzah Tri Januar; Suliyanto, Suliyanto
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/barekengvol19iss1pp35-50

Abstract

In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.

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