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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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PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA Syahzaqi, Idrus; Sediono, Sediono; Dyaksa, Mega Kurnia; Vionita, Anggi Triya; Ghasani, Anisah Nabilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2207-2218

Abstract

Climate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people.
MATHEMATICAL MODEL OF DENGUE HEMORRHAGIC FEVER SPREAD WITH DIFFERENT LEVELS OF TRANSMISSION RISK Herdicho, Faishal Farrel; Hakim, Nabil Azizul; Fatmawati, Fatmawati; Alfiniyah, Cicik; Akanni, John Olajide
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1649-1666

Abstract

Dengue Haemorrhagic Fever (DHF) is a vector-borne disease caused by the dengue virus, transmitted to humans through the bite of an infected female Aedes aegypti mosquito. DHF is prevalent in tropical regions, necessitating mathematical modeling to better understand its dynamics and predict its spread. This study develops and analyzes a mathematical model for DHF transmission that incorporates seven compartments to reflect different transmission risk levels. Stability analysis of the disease-free and endemic equilibria was conducted, with the basic reproduction number used to classify the conditions under which DHF transmission is controlled or endemic . Key model parameters were estimated using DHF case data from East Java in 2018, employing a genetic algorithm (GA) to optimize the estimation process. The GA approach achieved a mean absolute percentage error (MAPE) of , ensuring high accuracy in parameter values. Furthermore, the basic reproduction number was determined to be , which is greater than one, confirming that DHF remains endemic in East Java. Sensitivity analysis identified the mosquito biting rate , mosquito mortality rate , and transmission rates as the most critical factors influencing . Numerical simulations demonstrated the effects of these key parameters on both and the symptomatic human population . An increase in , , or significantly amplified and , while a rise in had the opposite effect, reducing both transmission and infections. These results underscore the critical role of vector control strategies, such as increasing mosquito mortality and reducing breeding sites, in mitigating DHF outbreaks. This study highlights the utility of combining mathematical modeling with genetic algorithm-based parameter estimation to provide accurate insights into disease dynamics and inform effective control measures.
A COMPARATIVE ANALYSIS OF DBSCAN AND GAUSSIAN MIXTURE MODEL FOR CLUSTERING INDONESIAN PROVINCES BASED ON SOCIOECONOMIC WELFARE INDICATORS Andayani, Sri; Retnani, Namita; Yusri, Thesa Adi Saputra; Marwoto, Bambang Sumarno Hadi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2039-2056

Abstract

Public welfare refers to a condition in which people experience happiness, comfort, prosperity, and can adequately fulfill their basic needs. Indonesia consists of several provinces, each with varying levels of welfare. One crucial aspect in promoting equitable development is ensuring that all regions in Indonesia achieve similar welfare standards. This study aims to classify Indonesian provinces based on socioeconomic welfare indicators, with the results serving as a basis for policy-making that considers regional potential and challenges. The data used in this study are secondary data obtained from the official website of BPS-Statistics Indonesia on provincial welfare indicators from 2020 to 2023. The research methodology includes data collection, descriptive statistical analysis, determining the optimal number of clusters, and comparing the clustering performance of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Gaussian Mixture Model (GMM) using Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index as evaluation metrics. The DBSCAN-based clustering resulted in two clusters: high-welfare and low-welfare regions. Meanwhile, GMM clustering produced five clusters: moderate, fairly low, low, high, and fairly high welfare regions. Based on cluster validity measures, GMM outperformed DBSCAN, achieving a Silhouette score of 0.28, a Davies-Bouldin Index of 1.12, and a Calinski-Harabasz Index of 10.9.
OPTIMAL PORTFOLIO FORMATION USING MEAN VARIANCE EFFICIENT PORTFOLIO AND CAPITAL ASSET PRICING MODEL WITH ARTIFICIAL NEURAL NETWORK AS STOCK SELECTION METHOD Siswanah, Emy; Maslihah, Siti; Anggraini, Agustina; Hakim, Muhammad Malik
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2097-2110

Abstract

There are two main things in forming an optimal stock portfolio: stock selection and stock weight determination. This study aims to determine the performance of an optimal portfolio formed using ANN as a stock selection method and MVEP (Mean-Variance Efficient Portfolio) and CAPM (Capital Asset Pricing Model) to determine stock weights. In addition, it is also necessary to determine the characteristics of the stocks formed in the portfolio. The criteria for stock selection are choosing stocks predicted to have maximum mean returns with minimal risk. This research uses data from 10 stocks listed on the Indonesian Stock Exchange. The forecasting results state that ANN can be used to predict stock prices to get a picture of stock prices in the future. Based on the calculation results, BMRI, TLKM, ASII, TPIA, and BBNI stocks were selected to form a stock portfolio. The MVEP and CAPM methods produce stock weights with different characteristics. The MVEP method gives the most significant weight to stocks that have the largest predicted mean return but experience changes in accuracy categories. The CAPM method gives the most significant weight to stocks with less risk than other stocks and has the smallest MAPE value. Empirically, ANN can be used to select stocks to form a portfolio. Stock price predictions with the most significant mean return and small risk can be used as a reference when forming a portfolio using the MVEP and CAPM methods.
PREDICTION OF ECONOMIC GROWTH RATE OF TUBAN REGENCY WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ALGORITHM Muaziza, Maya; Arifin, Ahmad Zaenal; Putro, Suzatmo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1699-1710

Abstract

This research aims to implement and evaluate the accuracy of the Adaptive Neuro Fuzzy Inference System (ANFIS) forward stage method to predict the economic growth rate of the Tuban Regency. In the application of ANFIS, two types of variables are required, namely, input variables which include road length, the number of electricity customers, the number of health workers, the number of high schools, and the number of cases of ordinary theft. Meanwhile, the predicted output variable is the economic growth rate. The fuzzification process uses a triangular membership function to map the input values. The data used in this study were obtained from the Central Bureau of Statistics (BPS) of Tuban Regency for 2014-2024. The prediction results show a very low Mean Absolute Percentage Error (MAPE) value of 0.14%, which reflects a very high level of accuracy. With MAPE < 10%, the accuracy of this model reaches 99.86% based on calculations made through the Matlab GUI. This research shows that the Adaptive Neuro Fuzzy Inference System (ANFIS) method can be used effectively and accurately to predict the economic growth rate of the Tuban Regency.
BEEF PRICE FORECASTING BASED ON TEMPORAL, SPATIAL AND SPACE-TIME PARAMETER INDICES Fatimah, Syifa Nurul; Zainnuddin, Ahmad Fuad; Mardiana, Novi; Mukhaiyar, Utriweni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1805-1824

Abstract

Beef is among the most sought-after commodities in Indonesia, resulting in significant price fluctuations, particularly during religious holidays. These price variations affect inflation and necessitate adjustments in government policies concerning beef distribution and imports. Therefore, it is essential to analyze and predict beef prices using empirical data from regions with the highest beef production and consumption levels. This study aims to examine beef price data through the lenses of temporal, spatial, and space-time dependencies within Java. The methodologies employed in this research include ARIMA, Semivariogram, Kriging, and GSTAR models applied to weekly beef price data from Java. ARIMA is used to analyze and forecast time series data based on past values and past forecast errors. The Semivariogram measures spatial dependence by quantifying how price similarities change with distance. Kriging is a geostatistical interpolation method that predicts price values at unobserved locations based on spatial correlation. GSTAR extends ARIMA by incorporating spatial and temporal dependencies to model interactions across different locations over time. The data used in this study consists of weekly beef price records from major markets across Java, obtained from National Food Agency of Indonesia, from August 2022 to May 2024. The findings of this study reveal that beef price fluctuations in Java are primarily influenced by temporal factors, particularly major religious holidays, rather than by location or a combination of location and time. However, there are spatial variations in beef prices across different observation locations. The best predictive model for forecasting beef prices is the ARIMA model. These results provide valuable insights into the patterns of beef prices based on temporal, spatial, and space-time parameters, offering a robust framework for understanding and anticipating price dynamics in the region.
SURVIVAL ANALYSIS ON DATA OF STUDENTS NOT GRADUATING ON TIME USING WEIBULL REGRESSION, COX PROPORTIONAL HAZARDS REGRESSION, AND RANDOM SURVIVAL FOREST METHODS Rachmawati, Ramya; Afandi, Nur; Alwansyah, Muhammad Arib
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2111-2126

Abstract

This article presents a comprehensive study of the factors that influence the length of study data of undergraduate students at FMIPA UNIB class 2018 and 2019. This study is essential because observations show that many students study for more than 8 semesters. The purpose of this study is to determine the factors that significantly influence the length of study of undergraduate students. These factors can be internal and external. Survival analysis is the right method to identify these factors because ordinary regression analysis is unable to estimate survival data. Therefore, methods such as Weibull regression, Cox Proportional Hazards regression, and Random Survival Forest are used. This study does not compare the methods used because these methods are independent of each other, but have the same goal, namely, to determine the factors that influence the length of study of students. The data used in this study are data on the length of study of students from the 2018 and 2019 cohorts sourced from the academic subsection of FMIPA UNIB, with variables of GPA, gender, region of origin, university entry route, parents' occupation, type of study program, and length of study. The results showed that GPA and the type of study program significantly influenced the length of study in Weibull regression analysis. In Cox proportional hazard regression, the GPA variable is an influential factor, while using the Random Survival Forest method, all factors significantly influenced the length of study, with their respective levels of importance.
A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS Omesa, Umar A; Waziri, Muhammad Y.; Moghrabi, Issam A. R.; Ibrahim, Sulaiman M.; E B, Gudu; S L, Fakai; Yunus, Rabiu Bashir; Madi, Elissa Nadia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1973-1988

Abstract

Conjugate Gradient (CG) methods are widely used for solving unconstrained optimization problems due to their efficiency and low memory requirements. However, standard CG methods may not always guarantee sufficient descent condition, which can impact their robustness and convergence behavior. Additionally, their effectiveness in training artificial neural networks (ANNs) remains an area of interest. In response, this paper presents a three-term conjugate gradient (CG) method for unconstrained optimization problems. The new parameter is formulated so that the search direction satisfies the sufficient descent condition. The global convergence result of the new algorithm is discussed under suitable assumptions. To evaluate the performance of the new method we considered some standard test problems for unconstrained optimization and applied the proposed method to train different ANNs on some benchmark data sets contained in the NN toolbox. The experimental results show that performance is encouraging for both unconstrained minimization test problems and in training neural networks.
META-REGRESSION OF SOCIOECONOMIC FACTORS AND THE PREVALENCE OF PHYSICAL DISORDERS IN HYPERTENSIVE PATIENTS Rahmi, Nur Silviyah; Astutik, Suci; Surya Wardhani, Ni Wayan; Maharani, Adinda Gita; Fakhrunnisa, Atmadani Rahayu; Khatimah, Husnul; Aulia, Silvia Intan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2275-2286

Abstract

Hypertension is a common degenerative disease with a high mortality rate and a significant impact on quality of life and productivity. Education level plays a crucial role in understanding and managing hypertension, where higher education levels can contribute to reducing the risk of hypertension. This study utilized meta-analysis and meta-regression to explore the relationship between education level and hypertension prevalence. Secondary data from eight previous studies conducted between 2015 and 2023 were analyzed. Heterogeneity analysis was performed to determine the appropriate meta-analysis model, with a random-effect model selected based on the test results. Of the eight studies analyzed, five showed a negative odds ratio, indicating that individuals with higher education levels have a lower likelihood of developing hypertension compared to those with lower education levels. The heterogeneity test showed significant variability among the studies (I2 = 91.38%). The random-effect model estimated a combined effect size with an ln odds ratio of -0.1777 and a 95% confidence interval of -0.3228 to -0.0326. These findings suggest that higher education levels are associated with a lower risk of hypertension. This underscores the importance of improving access to quality education as part of public health strategies to reduce the incidence of hypertension and enhance overall community well-being.
PREDICTION OF THE ELECTRIC POWER BY OSCILLATING WATER COLUMN WAVE POWER PLANTS ON BAWEAN ISLAND USING LSTM Putri, Risma Madurahma; Hakim, Lutfi; Novitasari, Dian C Rini; Asyhar, Ahmad Hanif; Setiawan, Fajar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2287-2300

Abstract

The demand for electricity in Indonesia continues to increase in line with population growth and the expansion of economic development. This increase is not matched by the diminishing electricity resources, as fossil fuels, which are non-renewable, are being used. Therefore, there is a need for renewable energy sources that can be utilized as long-term electricity resources. The abundant marine areas in Indonesia make it a potential source of alternative energy, one form of its utilization is the Ocean Wave Power Plant using the Oscillating Water Column (OWC) method. Bawean Island in Gresik is one of the regions that has this potential, while also facing long-standing electricity supply limitations that have resulted in uneven electricity distribution among the community. The problem does not stop at power generation but also extends to the transmission system between supply and demand. This research is conducted to predict the electricity generated by the ocean wave power plant to help avoid mismatches when supplying electricity. This study uses time series data from January 1st, 2021, to May 5th, 2024, which includes wave height, length, period, and amplitude. Electricity prediction based on these parameters can be performed using deep learning-based methods that can effectively process sequential time series data, such as the Long Short Term Memory (LSTM) method, by experimenting with the number of neurons, epochs, and batch sizes. The best prediction results for the variables of height, length, period, and amplitude of the waves obtained MAPE values of 0.3657%, 0.1637%, 0.0888%, and 0.3480%, respectively. The electricity prediction results from the best parameters obtained a MAPE of 0.3549%.

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