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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
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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 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
STABILITY OF THE DYNAMIC MODEL OF SVPR SEXUAL VIOLENCE CASES Bahri, Susila; Riyandi, Hanif Rayhan; Rudianto, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1719-1728

Abstract

In this study, the stability of the equilibrium points of the Susceptible Violent Punishment Recovered (SVPR) model in the spread of sexual violence cases is analyzed. The model assumes that sexual violence can spread similarly to the transmission of infectious diseases. The constructed model is a system of nonlinear differential equations. The model has two equilibrium points: the equilibrium point with sexual violence-free and the endemic equilibrium point with sexual violence. Stability analysis is then carried out for both fixed points, indicating that the violence-free equilibrium is asymptotically stable if the basic reproduction number . Meanwhile, the endemic equilibrium is asymptotically stable if the condition and four other conditions are satisfied. Numerical simulations are required to observe the implementation of the model using MAPLE software. As for this research, we conclude a model that shows the spread of sexual violence is not widespread because it was found that .
DERIVATION ON SEVERAL RINGS Thomas, Abdiel Bellamy; Puspita, Nikken Prima; Fitriani, Fitriani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1729-1738

Abstract

Research on ring derivation is one of the studies that is quite popular among algebra lovers. The definition of the derivation on the ring is motivated by the derivation in calculus which has Leibniz's rule. The purpose of this paper is to show some of the derivation properties on several rings, namely divisor rings, cartesian product rings, and factor rings. Let be a commutative ring with multiplicative identity and A the set of multiplicative closed that has non-zero divisor. In this paper, we have shown some results of derivation on ring theory. If is a ring derivation of R and is a divisor ring of , we can construct for all , then the map is a derivation on . The concept of embedding one ring into another ring can be used so that the ring of constant of , namely , is a subring of the divisor ring . Related to the ideal on ring theory, if I is an ideal of R, then where is also a derivation on the ring . The last result in this paper comes from the ring of cartesian product, take be a ring with derivation for . The cartesian product ring have a derivation ring defined by for any .
THE NUMERICAL APPROXIMATION OF STATIONARY WAVE SOLUTIONS FOR TWO-COMPONENT SYSTEM OF NONLINEAR SCHRÖDINGER EQUATIONS BY USING GENERALIZATION PETVIASHVILI METHOD Robbaniyyah, Nuzla Af’idatur; Chern, Jann-Long; Abdurahim, Abdurahim
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1739-1752

Abstract

The Petviashvili method is a numerical method for obtaining fundamental solitary wave solutions of stationary scalar nonlinear wave equations with power-law nonlinearity: , where is a positive definite and self-adjoint operator and is constant. Due to the case being a system of solitary nonlinear wave equations, we generalize the Petviashvili method. We apply this generalized method for a two-component system of Nonlinear Schr dinger Equations (NLSE) for 2-D.
ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD Pratama, Aditya Nugraha; Satyahadewi, Neva; Sulistianingsih, Evy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1753-1764

Abstract

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.
SPATIAL REGRESSION APPROACH TO MODELLING POVERTY IN JAVA ISLAND 2022 Siallagan, Maria A. Hasiholan; Pusponegoro, Novi Hidayat
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1765-1778

Abstract

Geographically Weighted Regression (GWR) model is a powerful tool for analyzing spatial patterns in data. However, the standard form of a spatial model that uses a single bandwidth calibration may be unrealistic because the response-predictor relationship may be either linear or nonlinear. To address this issue, the Multiscale GWR (MSGWR) model offers improved model performance by employing Generalized Additive Model (GAM) with varying bandwidth or smoothing function for each covariate in the model. This research aims to analyze the Percentage of Poor Population (PPP) on Java Island in 2022 using the geospatial models and related socioeconomic and demographic attributes, such as Open Unemployment Rate, Human Development Index, Labor Force Participation Rate, and GRDP Per capita to identify the best model in explaining the spatial pattern and to find out the determinant of PPP on Java Island in 2022. This study uses secondary data from Statistics Indonesia. The findings reveal that the MSGWR model provides the highest R2 and smallest AICc value compared to single bandwidth models, specifically the GWR and MXGWR models. Furthermore, the MSGWR model indicates that HDI has a significant negative effect on PPP, whereas LFPR has a significant positive effect on PPP across all districts in Java Island in 2022.
VALUE AT RISK ESTIMATION FOR STOCK PORTFOLIO USING THE ARCHIMEDEAN COPULA APPROACH Saifullah, Mohammad Dicky; Sa'adah, Umu; Andawaningtyas, Kwardiniya; Handamari, Endang Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1779-1790

Abstract

Investment is one of the many ways to achieve future profits. One form of investment that is widely made is stocks. The return obtained in investing in stocks is potentially higher than other investment alternatives, but the risks borne are amplified, so it is necessary to analyze these risks that may occur. In this study, the Archimedean copula method is used to estimate the Value at Risk on shares of PT Bank Rakyat Indonesia Tbk (BBRI) and PT Telekomunikasi Indonesia Tbk (TLKM) for the period September 1, 2021, to August 31, 2023. The stock data is used to determine the Archimedean copula model and calculate the estimated value of Value at Risk (VaR) on the stock return portfolio using the Archimedean copula approach. The Archimedean copula models used are the Clayton copula model, Gumbel copula, and Frank copula. Of the three Archimedean copula models, the best model was selected by looking at the largest Maximum Likelihood Estimation (MLE) value. In this study, the log-likelihood value of Clayton copula is 7.958, Gumbel copula is 6.663, and Frank copula is 8.398. Therefore, Frank copula is the best Archimedean copula model with the largest log-likelihood value of 8.398 for the said data. Then the VaR estimation is done with the Frank copula model. The Value at Risk estimation results based on the Frank copula model show maximum loss rates of -0.0277 at the 90% confidence level, -0.0363 at the 95% confidence level, and -0.0516 at the 99% confidence level.
COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA Adhel, Fadilah Amirul; Thamrin, Sri Astuti; Siswanto, Siswanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1791-1802

Abstract

Sentiment analysis is a process for analyzing opinions, sentiments, assessments, and emotions from someone's statements regarding a domain or is also a process for entering and processing data in the form of text. Support vector machine (SVM) is a supervised machine learning technique that functions as a separator of two classes of data. SVM aims to obtain numerical vectors using fasttext. SVM cannot choose appropriate parameters so the use of parameters is not optimal. To obtain optimal parameters with better classification results, firefly optimization was carried out. This research compares the fasttext-based SVM method without and with firefly optimization parameters using data from tweets with the keyword "Indonesian disaster" which was crawled using the Twitter application. The results of this research obtained 128 dimensions that form the weight of each word. This means that each word is represented in a 128-dimensional vector space. The evaluation of the SVM classification model with and without firefly optimization provides an accuracy of 89.1% and 61.3% respectively. This shows that the SVM classification method with firefly optimization provides quite good classification performance compared to the SVM model without optimization.
OPTIMIZING CARTON PRODUCT DELIVERY BY SOLVING TRAVELLING SALESMAN PROBLEM AT PACKAGING COMPANIES Aisyah, Fitri Sakinatul; Winarno, Winarno; Rinaldi, Dimas Nurwinata
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1803-1816

Abstract

Optimization of delivery routes is one form of increasing business productivity to achieve the company's main goal of distributing each product to customers. The Traveling Salesman Problem (TSP) is a combinatorial optimization problem where a salesman goes on a distribution journey that starts from the depot, then visits all customers exactly once, and ends with returning to the depot. This study aims to optimize the distribution route of carton products for packaging companies using the TSP model. The research methodology includes observation, interviews, and document studies to understand the distribution process of carton products at packaging companies. To complete the TSP model, Branch and Bound (BB) and Nearest Neighbor (NN) methods are applied to find the best solution for determining the distribution route of carton products. The way the BB method works is by utilizing branch cuts and boundaries to reduce search space and speed up the resolution process. In the NN method, the nearest point is chosen to get the shortest route distance. Research findings show that the use of the BB method results in a mileage difference from the initial route of 125 km (53% more efficient) and a fuel cost difference of 121,231 IDR (45% cheaper). Meanwhile, the NN method results in a mileage difference from the initial route of 115 km (48.94%) and a fuel cost difference of 109,901 IDR (41.27%). So, the method that produces the best solution is the BB method. The limitations of this study lie in the scale of the model used and the assumptions underlying the analysis. Future research can broaden the scope of the model and consider other factors that may affect the distribution of carton products. The results of this study contribute to improving the efficiency of carton product distribution.
COMPARISON OF DOUBLE EXPONENTIAL SMOOTHING AND FUZZY TIME SERIES MARKOV CHAIN IN FORECASTING FOREIGN TOURIST ARRIVALS Putri, Darvi Mailisa; Afrimayani, Afrimayani; Hasibuan, Lilis Harianti; Ul Hasanah, Fitri Rahmah; Jannah, Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1817-1828

Abstract

Foreign tourist arrivals are one of the factors that make a positive contribution to a country's economy, especially the addition of foreign exchange. This activity is important for the tourism industry and the government to make policies for progress in the tourism sector. This research aims to forecast data on foreign tourist arrivals, especially land routes. This data set, which is a monthly time series covering the period from January 2018 to October 2023, is sourced from the Central Statistics Agency (BPS). The DES technique is a method that quickly adapts to changes in data patterns and can lessen the impacts of random fluctuations, resulting in more stable estimates. Meanwhile, the FTS-MC approach can handle large data variations by utilizing fuzzy sets. Furthermore, combining fuzzy time series with Markov Chains increases forecast accuracy by taking into account state transitions and probability. The research demonstrates that the DES method produces the MAPE value of 0.108530 or 10% which is obtained from the alpha value of 0.9 and beta 0.2. The MAPE 0.108530 means that the ability of the forecasting model is classified as a good category. In the FTS-MC method, the forecast data is close to the actual data. This is indicated by the MAPE value obtained of 0.086850 or 8%, which means that the ability of the forecasting model is very good. Based on the analysis of the two methods, it is concluded that the FTS-MC method is better applied to data on land-based foreign tourist arrivals.
APPLICATION OF ADASYN OVERSAMPLING TECHNIQUE ON K-NEAREST NEIGHBOR ALGORITHM Marlisa, Herina; Satyahadewi, Neva; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1829-1838

Abstract

The K-Nearest Neighbor Algorithm is a commonly used data mining algorithm for classification due to its effectiveness with large datasets and noise. However, class imbalance may impact classification results, where data with unbalanced classes may classify new data based on the majority class and ignore minority class data. The research analyzed whether applying the Adaptive Synthetic (ADASYN) oversampling technique in the K-Nearest Neighbor Algorithm can handle data imbalance problems. The study looks at the resulting accuracy, specificity, and sensitivity values. ADASYN oversamples the minority class data based on the model's difficulty level of data learning using distribution weights. This research uses the Pima Indian Diabetes Dataset from the Kaggle website. The dependent variable was diabetes mellitus status, while the independent variables were number of pregnancies, glucose levels, diastolic blood pressure, insulin levels, Body Mass Index (BMI), and age. The study found that the accuracy, specificity, and sensitivity values were 72.88%, 73.42%, and 71.79%, respectively. Based on the results of the analysis, it can be concluded that using ADASYN in the K-Nearest Neighbor Algorithm to classify diabetes mellitus in Pima Indian women is good enough to address imbalanced data. It is shown that the ADASYN oversampling technique can help the K-Nearest Neighbor Algorithm to classify new data without ignoring the data of the minority class.

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