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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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PERFORMANCE COMPARISON OF SARIMA INTERVENTION AND PROPHET MODELS FOR FORECASTING THE NUMBER OF AIRLINE PASSENGER AT SOEKARNO-HATTA INTERNATIONAL AIRPORT Nur Aziza, Vivin; Moh'd, Fatma Hilali; Maghfiroh, Firda Aulia; Notodiputro, Khairil Anwar; Angraini, Yenni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2107-2120

Abstract

The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.
PANEL DATA REGRESSION MODEL FOR PREDICTING ECONOMIC GROWTH BEFORE AND DURING THE COVID-19 PANDEMIC IN EAST JAVA PROVINCE Susetyoko, Ronny; Satriyanto, Edi; Fadliana, Alfi; Humaira, Fitrah Maharani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2121-2134

Abstract

Gross Regional Domestic Product (GRDP) is one of the key indicators to determine the economic condition of a region in a certain period. GRDP at constant prices has a positive and significant effect on economic growth . This study aims to predict economic growth in East Java before and during the Covid-19 pandemic based on the structural components of regional revenue and expenditure budget realization using panel data regression with data sources from the Directorate General of Fiscal Balance. The results of this study, the best model is the Fixed Effect Model (FEM) with R-Squared 0.99991 and Adj. R-Squared 0.99987. MSE, MAD and MAPE values on the training data are 338724.9919, 259.7182 and 0.6296 respectively. While the MSE, MAD and MAPE values in the testing data are 1716324.2736, 445.7959 and 1.0692 respectively. At the 95% confidence level, Locally Generated Revenue (LGR), Transfers to Regional and Village Funds (TRVF), and Other Revenue (OR) are not significant in the model or have little effect. But at the 99.9% confidence level, all factors (cross section) have a very significant effect. This can be interpreted that local wisdom, or the characteristics of each region/city has a major contribution to economic growth.
COMPARING GAUSSIAN KERNEL AND QUADRATIC SPLINE OF NONPARAMETRIC REGRESSION IN MODELING INFECTIOUS DISEASES Adityaningrum, Amanda; Ladjali, Sri Indriani; Djakaria, Ismail; Yahya, Lailany; Payu, Muhammad Rezky Friesta; Nashar, La Ode; Jusuf, Herlina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2135-2146

Abstract

The regression curve for nonparametric regression is assumed to belong to some infinite-dimensional collection of functions, which allows great flexibility in the form of the curve. This research intends to compare the Gaussian Kernel and Quadratic Spline regressions in four infectious diseases in Indonesia by 2021. The data used is secondary data from the Central Bureau of Statistics and the Ministry of Health, Indonesia, and the sample consists of four infectious diseases in Indonesia by 2021 (Tuberculosis, Diarrhoeal, Pneumonia, and COVID-19). Considering the correlation value, it was found that the independent and dependent variables of the four infectious diseases are all highly correlated (r values are more than 0.7). Furthermore, the scatter plots for four infectious diseases do not follow a particular pattern; due to this, parametric regression cannot be used to analyze the data. Therefore, nonparametric regression was applied in this research . According to the analysis, the Gaussian Kernel is the best regression technique for modeling four infectious diseases in Indonesia by 2021, which its R2 values are 99.85% (Tuberculosis), 100% (Diarrhoeal), 99.91% (Pneumonia), and 99.99% (COVID-19).
ANALYSIS OF THE COVID-19 EPIDEMIC MODEL WITH SELF-ISOLATION AND HOSPITAL ISOLATION Manaqib, Muhammad; Padilah, Tesa Nur; Maulana, Iqbal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2147-2160

Abstract

This research developed the SIR model with self-isolation and hospital isolation. The analysis is carried out through the disease-free and endemic equilibrium point analysis and the sensitivity analysis of the basic reproduction number. Based on the disease-free equilibrium point analysis, for a certain period of time the population will be free from COVID-19 if the basic reproduction number is less than 1. If the basic reproduction number is more than 1, the disease will persist in the population, this will lead to an endemic equilibrium point. Based on the sensitivity analysis of parameter values on the basic reproduction number, the parameter for the isolation rate of individually infected individuals in hospitals is -0.4615166040, and the self-isolation rate at home is -0.01853667767. This indicates that isolation in hospitals is more effective than self-isolation in suppressing the spread of COVID-19.
APPLICATION OF EXPECTED CREDIT LOSS MODEL AND MARKOV CHAIN TO CALCULATE NET SINGLE PREMIUM OF UNSECURED CREDIT INSURANCE Lieus, Hansen Juni; Tedja, Devin; Joewita, Vanessa; Hidayat, Agus Sofian Eka; Silalahi, Alexander R. J
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2161-2170

Abstract

Transferring credit risk to an insurance company is a way to mitigate risk. Premiums should be calculated accurately to attain economic value for both the lender and the guarantor. The aim of this study was to determine the net single premium (NSP) values for an unsecured credit insurance product using the expected credit loss (ECL) method from IFRS 9. This study used data generated through simulation of insurance policies issued in 2015 or 2016. Their state classifications were monthly observed from 2016 to 2020. The probability of disbursed claim (PDC) parameter replaced the probability of default parameter on the ECL model, whereas the PDC model was constructed based on the components of a state-transition probability matrix, obtained with the Markov chain approach using the cohort method: = 0.999181, = 0.000130, and = 0.000689. The PDC model validation showed relatively decent results, whereas MSE = 2.457% and zs = 0.608 with a = 5%. These results indicated that the PDC model was a good fit to calculate ECL. 5,000 iterations were done as part of the cash flow simulation process, whereas debtors’ loan amounts were randomly generated during each iteration, and the average NPV of these iterations was -Rp564.419.305. Based on model sensitivity analysis, cash flow values were most sensitive to the variable used to construct the PDC model (). Thus, the 5,000-iteration process was repeated with the newly adjusted PDC value, which were = 0.998924 and = 0.000946. The new average NPV of these iterations was Rp409,877,840, indicating that the constructed ECL model was a good fit to calculate NSP values for unsecured credit insurance products.
SAWI TRANSFORMATION FOR SOLVING A SYSTEM OF LINEAR ORDINARY DIFFERENTIAL EQUATIONS Halim, Elvina; Zakaria, La
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2171-2186

Abstract

There are many problems in nature whose solutions are obtained through mathematical concepts. One of the most common mathematical concepts is a mathematical concept that is classified under initial value problems, such as a system of linear ordinary differential equations equipped with initial values. One tool that can solve the initial value problem is the Sawi transformation. This article describes the study of the initial value problem as a system of linear ordinary differential equations and its solution using the Sawi transformation. In addition, as part of applying the resulting theory, 2 (two) case examples are given (a first-order chemical reaction system with three certain chemicals and a mass-spring system with forced motion) to be solved using the Sawi transformation. So that problem solving can be interpreted and easily understood, in the 2 (two) case studies discussed and focused on the concentration of the chemical reactants, simulations were carried out for several different initial values and reaction rate constants. Compared to other methods (Laplace transform), the results obtained from using the Sawi transformation for the cases discussed show that the analytical solutions for the selected initial values have similar solutions.
SIGNIFICANT FACTORS INFLUENCING HYPERBILIRUBINEMIA AT SANTO YUSUF MOTHER AND CHILD HOSPITAL, NORTH JAKARTA USING BINARY LOGISTIC REGRESSION Pratiwi, Elsa Anna; Nurhayati, Nunung; Sihwaningrum, Idha
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0075-0084

Abstract

Hyperbilirubinemia is a problem that often occurs in newborns. The cause of hyperbilirubinemia is multifactorial including maternal, perinatal or environmental factors that can be risk factors in newborns. Hyperbilirubinemia that occurs in infants is usually due to high bilirubin levels. High bilirubin can be a poison that causes brain damage so hyperbilirubinemia must be treated appropriately so as not to cause chronic complications. This study aims to identify significant factors affecting hyperbilirubinemia in infants at Santo Joseph Mother and Child Hospital, North Jakarta using binary logistic regression. This research was conducted at Santo Joseph Mother and Child Hospital for the first time. Factors that are thought to influence are gestational age, birth weight, childbirth, breastfeeding, and infection status. The results showed that the significant factors affecting hyperbilirubinemia in infants were the process of childbirth, milk feeding and infection status. Based on the odds ratio value for each variable, it can be concluded that babies with abnormal birth processes have a risk of hyperbilirubinemia of 2.9628 times greater than babies with normal births. Meanwhile, formula-fed infants have a risk of hyperbilirubinemia of 4.2854 times less than breastfed babies. Furthermore, infants affected by infection have a risk of developing hyperbilirubinemia of 5.5752 times greater than infants who do not get infection.
ALGEBRAIC CRYPTANALYSIS ON NTRU-HPS AND NTRU-HRSS Paradise, Fadila; Sugeng, Kiki Ariyanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2187-2196

Abstract

NTRU is a lattice-based public-key cryptosystem designed by Hoffstein, Pipher, and Silverman in 1996. NTRU published on Algorithmic Number Theory Symposium (ANTS) in 1998. The ANTS’98 NTRU became the IEEE standard for public key cryptographic techniques based on hard problems over lattices in 2008. NTRU was later redeveloped by NTRU Inc. in 2018 and became one of the finalists in round 3 of the PQC (Post-Quantum Cryptography) standardization process organized by NIST in 2020. There are two types of NTRU algorithms proposed by NTRU Inc., which are classified based on parameter determination, NTRU-HPS (Hoffstein, Pipher, Silverman) and NTRU-HRSS (Hulsing, Rijnveld, Schanck, Schwabe). Algebraic cryptanalysis on ANTS’98 NTRU had previously been carried out in 2009 and 2012. In this paper, algebraic cryptanalysis is performed on NTRU-HPS with q=2048, n=509 (ntruhps2048509) and NTRU-HRSS with n=701 (ntruhrss701). This research aims to evaluate the resistance of NTRU-HPS and NTRU-HRSS algorithms against algebraic cryptanalysis by reconstructing the private key value. As a result, NTRU-HPS and NTRU-HRSS resistance to algebraic cryptanalysis.
COMPARISON OF APARCH-TYPE MODELS: DOES THE CONTINUOUS AND JUMP COMPONENTS OF REALIZED VOLATILITY IMPROVE THE FITTING? Nugroho, Didit B.; Urosidin, Nur I. M.; Parhusip, Hanna A.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0085-0094

Abstract

This study aims to extend an APARCH-X(1,1) model to the APARCH-CJ(1,1) by separating the exogenous variable X into two components: continuous and discontinuous (jump). The study was based on the application of models to 1-min intraday high-frequency data from the Tokyo Stock Price Index from 2004 to 2011, where its dependent variable is daily return and its exogenous variability is Realized Volatility. As a basic framework, the return errors follow a Normal distribution. An Adaptive Random Walk Metropolis (ARWM) method was constructed in the Markov Chain Monte Carlo algorithm to estimate model parameters so that the model fits the observed return time series. By visual inspection, the parameter trace plots showed good convergence of the Markov chains, indicating that the ARWM method is efficient in estimating the studied models. Based on the results of the Akaike Information Criterion for model fitting to data, this study found that APARCH-CJ(1,1) is inferior to APARCH(1,1).
IMPLEMENTATION OF THE BIDIRECTIONAL GATED RECURRENT UNIT ALGORITHM ON CONSUMER PRICE INDEX DATA IN INDONESIA Tanjung, Andjani Ayu Cahaya; Saputro, Dewi Retno Sari; Kurdhi, Nughthoh Arfawi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0095-0104

Abstract

The Consumer Price Index (CPI) is the main index in measuring the inflation rate. Changes in the CPI from time to time reflect inflation and deflation, namely the higher the CPI value, the higher the inflation rate. This study aims to apply Birectional Gated Recurrent Unit (BiGRU) model to the CPI data in Indonesia. BiGRU comprises two GRU layers so it captures sequences that are ignored by the GRU. The research data is in the form of CPI data in Indonesia from January 2006 to December 2022 sourced from the website of the Central Bureau of Statistics totaling 204 data. The data is divided into training data and testing data. Training data was taken from January 2006 to July 2019 as many as 163 data. Data testing was taken from August 2019 to December 2022 as many as 41 data. Before the data is processed, a sliding window process is carried out by dividing the data into segments to reduce the error value. The window size value used is 10. In the sliding window process, the number of segments is 194 data segments. Based on the experiment results, it was concluded that the application of BiGRU to the CPI data was carried out in an experiment with 20 BiGRU architectures. BiGRU architecture was obtained which produced the lowest MAPE value, namely an architecture with two BiGRU layers having 256 neurons and 400 units, and one dense layer. In addition, the epochs used are 200 epochs, the ReLU activation function, and Adam optimization. The experimental results of the BiGRU architecture obtained a MAPE value of 0.24% which indicates that the architectural performance is very good.

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