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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 60 Documents
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Search results for , issue "Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
ENSEMBLE BAGGING WITH ORDINAL LOGISTIC REGRESSION TO CLASSIFY TODDLER NUTRITIONAL STATUS Arini, Luthfia Hanun Yuli; Solimun, Solimun; Efendi, Achmad; Fernandes, Adji Achmad Rinaldo
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/barekengvol19iss1pp1-12

Abstract

One problem in classifying stunting data is that the data used does not have a balanced proportion. This study aims to apply the logistic regression classification method with ordinal scale response variables to overcome class imbalance through the ensemble bagging approach. The data used is secondary data in the form of final research reports that have been tested for validity and reliability. The predictor variables used are economic conditions, health services and the environment with categorical response variables, namely the nutritional status of toddlers in the categories of stunting, normal and high. The methods used are ordinal logistic regression and ensemble bagging on ordinal logistic regression with bootstraps of 100, 500, and 1000. The variables that influence the nutritional status of toddlers are Economic Conditions, Health Services, and the Environment. The results of the study showed that the accuracy, sensitivity, specificity, and F1-Score for ordinal logistic regression were smaller than ensemble bagging in ordinal logistic regression. The best classification method obtained was bagging logistic regression with a bootstrap number of 500 and obtained an accuracy value of 85%, sensitivity of 87.2%, specificity of 72.6%, and F1-Score of 79.3%.
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:
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.
MULTINOMIAL LOGISTIC REGRESSION MODEL USING MAXIMUM LIKELIHOOD APPROACH AND BAYES METHOD ON INDONESIA'S ECONOMIC GROWTH PRE TO POST COVID-19 PANDEMIC Purwanto, Arie; Suprayogi, Muhammad Aziz; Setiawan, Erwan; Loly, Joao Ferreira Rendes Bean; Rahman, Gusti Arviana; Kurnia, Anang
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/barekengvol19iss1pp51-62

Abstract

Economic growth in Indonesia has become a major concern in the global context, especially before and after the Covid-19 pandemic. Key sectors such as tourism, manufacturing, trade and transportation have been seriously affected by restrictions on travel and economic activity imposed to control the spread of the virus. Therefore, it is considered necessary to carry out modeling to describe existing conditions. In this research, two approaches were used, namely the Maximum Likelihood approach and the Bayes approach. The use of methods in general as research material for researchers to study these two methods further. So far the algorithm used for the Bayes concept method is Markov Chain Monte Carlo with Hasting's Metropolis method. The parameter estimation results obtained from both methods are considered quite identical. However, it is necessary to pay attention to the iteration procedure that will be carried out. The selection of factors used in the iteration process is very determining in obtaining estimated parameter values. Furthermore, the results obtained so far do not contain any fundamental differences regarding economic growth in Indonesia. In general, Indonesia can be said to be stable in terms of economic growth.
CLUSTERING DISTRICTS/CITIES IN EAST JAVA PROVINCE BASED ON HIV CASES USING K-MEANS, AGNES, AND ENSEMBLE Lusia, Dwi Ayu; Salsabila, Imelda; Kusdarwati, Heni; Astutik, Suci
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/barekengvol19iss1pp63-72

Abstract

Cluster analysis is a method of grouping data into certain groups based on similar characteristics. This research aims to group districts/cities in East Java Province in 2021 based on HIV cases using hierarchical cluster analysis (AGNES), non-hierarchical cluster analysis (K-means), and ensemble clustering. The study found that the ensemble clustering solution forms four clusters, consistent with the results of AGNES clustering. This suggests that ensemble clustering improves the quality of cluster solutions by leveraging both hierarchical and non-hierarchical methods. The grouping of districts/cities based on HIV cases provides a clear distribution pattern for more targeted interventions. The study is limited to HIV cases in East Java Province and may not be generalizable to other regions with different epidemic characteristics. Additionally, the study focuses on clustering methods without investigating temporal changes in HIV case distribution. This research is one of the few studies that applies ensemble clustering to HIV cases in East Java Province. It combines hierarchical and non-hierarchical methods to improve the clustering process and provides a practical approach for regional HIV control planning.
THE IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) METHOD ON OPEN UNEMPLOYMENT RATE IN REGENCY/CITY OF SUMATRA ISLAND Yuni, Syarifah Meurah; Saputra, T. Murdani; Fadhilah, Nadya Nur
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/barekengvol19iss1pp73-86

Abstract

Unemployment is a condition where a person who is included in the labor force but does not have a job and is not actively looking for work. The number of unemployed is measured using the Open Unemployment Rate (OUR) indicator. OUR is obtained by comparing the number of job seekers and the number of labor force. This study aims to obtain a model of OUR in each district / city of Sumatra Island and what factors influence it using the Geographically Weighted Regression (GWR) method and Fixed Gaussian Kernel Function weighting, and describe predictor variables on thematic maps. The GWR method is one of the statistical methods that can prevent the presence of spatial aspects in the data. The parameters estimated by the local regression model vary at each location point and are estimated using the Weighted Least Square (WLS) method. Based on the research results obtained from this study, the GWR models obtained amounted to 154 different local models in each district / city on the island of Sumatra. Variables Labor Force Participation Rate, Population Growth Rate, Population Density and Average Years of Schooling have a significant influence on each location, meanwhile variable Percentage of Poor Population and variable Poverty Line have no influence on any location. These variables are able to explain the OUR by 57.2%, where the remaining 42.8% is explained by other factors that are not explained in the model.
APPLIED MODIFIED EXPONENTIAL APPROACH METHOD TO DETERMINE THE OPTIMAL SOLUTION Pasaribu, Meliana; Helmi, Helmi; Pajriah, Dwi; Lestari, Devi Indah
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/barekengvol19iss1pp87-96

Abstract

PT. IGM distributes vaccines to several cities within and outside West Kalimantan. Distribution can be carried out directly or through CV. XYZ. To maintain vaccine quality, an effective and efficient vaccine management plan is required, especially for storage and distribution, to prevent any deviations in these processes This is done to ensure the vaccine’s potency remains intact until it is ready for use. Distribution routes are chosen to be as efficient as possible. Therefore, this article discusses the application of the transportation method to manage vaccine distribution and minimize distribution costs. The distribution problem is formulated into a mathematical model and solved using the modified exponential approach method. This method is improvement on the improved Exponential Approach, focusing on the determination of initial solution and table revisions. Allocation is based on selecting cells with the smallest reduced cost entries. Based on research findings, PT IGM distributes vaccines to CV. XYZ, Pontianak and Kuburaya in amounts of 209.000 units, 151.000 units and 310.000 units, respectively. CV. XYZ distributes vaccines to Ketapang, Singkawang, Sintang and Bengkayang in amount of 40.000 units, 55.000 units, 45.000 units, and 9.000 units, respectively.
STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS Maulana, Dimas Avian; Sofro, A'yunin; Ariyanto, Danang; Romadhonia, Riska Wahyu; Oktaviarina, Affiati; Purnama, Mohammad Dian
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/barekengvol19iss1pp97-106

Abstract

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.
MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS Maula, Sugha Faiz Al; Setiawan, Nicoletta Almira Dyah; Pusporani, Elly; Jannah, Sa'idah Zahrotul
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/barekengvol19iss1pp107-118

Abstract

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.

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