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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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VALUE AT RISK ANALYSIS ON BLUE CHIP STOCKS PORTFOLIO WITH GAUSSIAN COPULA Ardhitha, Tiffany; Sulistianingsih, Evy; Satyahadewi, Neva
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1739-1748

Abstract

Value at Risk (VaR) is a risk measurement tool to calculate the estimated maximum investment loss with a certain confidence level and period. VaR calculations using financial data are often not normally distributed, so the copula method is used, which is flexible on the assumption of normality on stock return data. Previous research discussed Gaussian copula using stocks from the telecommunications sector. In this research, using Gaussian copula on Blue Chip stocks. Blue Chip stocks have a good reputation and have a stable growth rate so they have a lower risk. Therefore, the research objective is to analyze the VaR portfolio of Blue Chip stock with Gaussian copula. This research uses the daily stock closing prices of BBNI and BBTN from November 2, 2020 to October 27, 2022. The analysis results suggested that a VaR portfolio using Gaussian copula with a confidence level of 90%, 95%, and 99%, respectively are 2.24%, 2.88%, and 4.02%. The value shows the percentage of investment risk that may be obtained in the next one-day period. This result also indicates that the higher the confidence level, the greater the VaR.
A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA Haq, Irvanal; Aidi, Muhammad Nur; Kurnia, Anang; Efriwati, Efriwati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1749-1760

Abstract

An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.
POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA Widyaningsih, Yekti; Budiawan, Zalfa Alifah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1761-1772

Abstract

Tuberculosis is an infectious disease and one of the world's top 10 highest causes of mortality in Indonesia. Based on this fact, it is necessary to study what factors affect number of tuberculosis cases. The number of tuberculosis cases as dependent variable is a count data that generally analyzed using Poisson regression. However, equidispersion assumption must be met, so Generalized Poisson Regression and Negative Binomial Regression are applied if the assumption is not met. Spatial aspects can be considered so Geographically Weighted Generalized Poisson Regression and Geographically Weighted Negative Binomial Regression were also conducted. Four models were built to evaluate relationship between number of tuberculosis cases and factors affecting it in Java in 2020. The explanatory variables are population density, percentage of children receiving BCG immunization, percentage of poor people, percentage of eligible drinking water facilities, percentage of family cards with access to proper sanitation, percentage of public places meet health requirements, and percentage of food management places meet hygienic requirements. This study shows that the best model for modeling the data is GWNBR with 2 groups of significant explanatory variables. Seven explanatory variables are statistically significant in 88 districts and six explanatory variables statistically significant in 12 districts.
COMPARISON OF SEASONAL TIME SERIES FORECASTING USING SARIMA AND HOLT WINTER’S EXPONENTIAL SMOOTHING (CASE STUDY: WEST SUMATRA EXPORT DATA) Hasibuan, Lilis Harianti; Musthofa, Syarto; Putri, Darvi Mailisa; Jannah, Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1773-1784

Abstract

Export is the activity of selling goods or services from one country to another. This activity usually occurs in a specific region or country. Export data is a type of data that has a seasonal pattern. This study aims to compare SARIMA and Holt Winter’s methods in forecasting export data. In this study, the SARIMA model ((1,1,1) (0,1,1))12 and Holt Winter's simulation were obtained. The data used is the export data of West Sumatra from 2016 to 2022. The best model is the one with the smallest MAPE or MAD. The SARIMA model yielded a MAPE of 0,437% and MAD of 78,821. Meanwhile, the Holt Winter's method yielded a MAPE of 0,894% and MAD of 163,320 with α=0,2, β=0,5, γ=0,1. Therefore, the SARIMA outperformed the Holt Winter’s method due to its higher accuracy. It can be concluded that the SARIMA is suitable to use as the forecasting model in this case. In this study, forecast have been made for the next 24 periods, from January 2023 to December 2024.
CLASSIFICATION OF STUDENT GRADUATION STATUS USING XGBOOST ALGORITHM Dwinanda, Maria Welita; Satyahadewi, Neva; Andani, Wirda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1785-1794

Abstract

College is an optional final stage in formal education. At this time, universities are required to have good quality by utilizing all the resources they have. Therefore, efforts are needed to help the faculty and study program make policies and decisions. One of the efforts that can be made is to classify student graduation status as early as possible to increase the number of students graduating on time. Thus, a classification algorithm is needed to avoid overfitting and produce good accuracy. The purpose of this study was to classified the student graduation status of the Statistics Untan Study Program using the XGBoost algorithm. XGBoost is an ensemble algorithm obtained through the development of gradient boosting. XGBoost has several features that can be used to prevent overfitting, but it can only process numerical data. Therefore, 140 numerical data were adjusted using the dummy technique in this study. The resulting XGBoost classification model is optimal at the number of rounds is 3 and the number of folds is 5. Based on the performance evaluation of the XGBoost algorithm, an accuracy of 75,00%, precision of 88,89%, sensitivity of 76,19% and specificity of 71,43% were obtained. Thus, the performance of the XGBoost algorithm is classified as good.
SURVIVAL FUNCTION AND HAZARD FUNCTION ANALYSIS OF EXPONENTIAL DISTRIBUTION IN TYPE I CENSORED SURVIVAL DATA: A CASE STUDY OF BREAST CANCER PATIENTS Kurniawan, Ardi; Previan, Anggara Teguh; Nurrohmah, Zidni Ilmatun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1795-1802

Abstract

Breast cancer is the most common cancer in women and the leading cause of cancer-related death in Indonesia. Analysis of survival data is important for improving the treatment and care of breast cancer patients. This study aims to estimate the parameters, find the survival function, and hazard function of breast cancer patients using a parametric method with an exponential distribution. Previous studies have shown that the Maximum Likelihood Estimation (MLE) method is suitable for estimating the survival function from exponential survival data by censoring. In this study, the exponential distribution was found to be the best for data on breast cancer patients from Surabaya Ontology Hospital. The estimated parameters are θ = 33.9157, and the survival function is calculated using The estimated hazard function for patient death or failure is 0.0295. The results of this study can contribute to the development of better treatment and care strategies for breast cancer patients. However, further research is needed because this study only used monthly time units.
DESIGN OF KIP KULIAH SELECTION SYSTEM AND RECIPIENT DETERMINATION USING SUPPORT VECTOR MACHINE (SVM) Talakua, Mozart Winston; Tomasouw, Berny Pebo; Ilwaru, Venn Yan Ishak
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1803-1814

Abstract

KIP Kuliah is tuition assistance from the government for high school graduates or equivalent with good academic potential but has economic limitations. In recent years it has been seen that the Indonesian government has always tried to increase the quota for KIP Kuliah recipients. In this study, the Support Vector Machine (SVM) method was applied to create a system for selecting and determining KIP Kuliah recipients. To obtain the best model to be used in the system, the training and testing data are divided into three data distribution schemes, namely 60/40, 70/30, and 80/20. After the training and testing process was carried out using the SVM method with various parameter variations, then the best accuracy rate of 94.59% is obtained in the 80/20 data sharing scheme for the nonlinear SVM model with the RBF kernel. With this system, it is hoped that the KIP Kuliah selection process at the tertiary level can run effectively, efficiently and the results of the determination are more targeted.
COMPARISON OF EDGE DETECTION METHODS USING ROBERTS AND LAPLACIAN OPERATORS ON MANGO LEAF OBJECTS Darwis, Dedi; Fernando, Yusra; Trisnawati, Fika; Marzuki, Dwiki Hafizh; Setiawansyah, Setiawansyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1815-1824

Abstract

Edge detection is a technique to find the outlines of an object in an image by detecting significant changes in brightness or discontinuities. This study discusses the comparison of edge detection using Roberts operators and Laplacian operators. The object used in edge detection is four types of mango leaves (Golek, Arum Manis, Madu, and Kuweni) with the *.jpeg format that has been pre-processed with 1000 x 278 pixels. The test used in this study compared the results of White Pixel values, MSE, and PSNR with test data as many as 24 data samples from four types of mango leaves. The results of the comparison of edge detection methods using the Laplacian operator get the lowest MSE value of 7.8577, the highest PSNR value of 39.2119, and the white pixel value of 164951, while the Roberts operator gets the lowest MSE value of 8.9723, the highest PSNR value of 38.6358, and the white pixel value of 155889.
LOGISTIC REGRESSION APPROACH TO STUDY PREGNANT WOMEN CASES AS A RISK FACTOR OF LBW AT THE BATAKTE PUBLIC HEALTH CENTER, KUPANG BARAT DISTRICT Ginting, Keristina Br; Putra, Ganesha Lapenangga
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/barekengvol17iss4pp1825-1834

Abstract

The research examines the influencing factors of low birth weight (LBW) in the work area of ​​the Batakte Public Health Center, Kupang Regency. The specific objective of this study is to identify the risk factors associated with the LBW. In parallel with the research objective, the hypothesis of this study is that there is a relationship among several maternal factors: age, education, number of children, birth spacing, chronic energy deficiency (CED), low hemoglobin in pregnant women, and the practice of antenatal care (ANC) during pregnancy. The sample in this study was some mothers who gave birth in 2020 and 2021, whose data were recorded. The results of the study show that the logistic models of the dominant parameters causing LBW cases are: 1. Pregnant women who experience CED have a risk of giving birth to babies with LBW: 2. The risk opportunities for pregnant women with low hemoglobin (below 11g/dl) is will give birth to a LBW baby: and 3. The risk of pregnant women who never or do not routinely perform ANC is about will give birth to a LBW baby. The results of parameter interpretation show that the dominant factor causing LBW cases in the work area of the Batakte Public Health Center is pregnant women who experience chronic energy deficiency conditions, low hemoglobin, and pregnant women who have never or only once done Antenatal care (ANC) in the work area of the Batakte Public Health Center.
ZERO-INFLATED NEGATIVE BINOMIAL MODELING IN INFANT DEATH CASE DUE TO PNEUMONIA IN EAST JAVA PROVINCE Astuti, Cindy Cahyaning; Puka, Agnes Ona Bliti; Wiguna, Akbar
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/barekengvol17iss4pp1835-1844

Abstract

Pneumonia is an acute infectious disease of the respiratory tract and an infection caused by a virus, bacteria or fungus that attacks the lung tissue. Several cases of pneumonia have resulted in deaths that occurred in toddlers aged 12-59 months. Based on official health in profile data, East Java's health in 2021 has a zero number of deaths under five aged 12-59 months due to pneumonia. Modeling data with many response variables is zero and there is overdispersion can be done using Zero Inflated Negative Binomial (ZINB) regression. This study aims to model the number of infant deaths aged 12-59 months due to pneumonia in East Java Province based on seven factors that are considered to influence the number of deaths in infants due to pneumonia. From this model, it can be seen that the factors that significantly influence the death of infants aged 12-59 months due to pneumonia in East Java Province using Zero Inflated Negative Binomial (ZINB) regression. The results of testing the parameters of the ZINB regression model show that the predictor variables that have a partial significant effect on the negative binomial model in East Java are the percentage of infants who received complete basic immunization, the percentage of coverage of under-five health services, the percentage of under-five children with malnutrition, the percentage of LBW (low birth weight babies). Selection of the best model is obtained by using the Bayesian Information Criterion (BIC) of 101,587.

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