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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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IMPACT OF FEATURE SELECTION ON DECISION TREE AND RANDOM FOREST FOR CLASSIFYING STUDENT STUDY SUCCESS Satiranandi Wibowo, Firdaus Amruzain; Retnawati, Heri; Sakti, Muhammad Lintang Damar; Khoirunnisa, Asma; Batubara, Angella Ananta; Berlian, Miftah Okta; Ibrahim, Zulfa Safina; Jailani, Jailani; Sumaryanto, Sumaryanto; Prasojo, Lantip Diat
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/barekengvol19iss3pp2083-2096

Abstract

The advancement of technology has a profound impact on the field of education. Education plays a crucial role in enhancing quality of life, particularly in higher education, where one of the key parameters is student success. This study investigates the influence of feature selection on the performance of machine learning models, particularly Decision Tree and Random Forest, in classifying student academic success. Utilizing a dataset of 19,061 students, the research aims to identify significant variables impacting classification outcomes. Feature selection was conducted using LASSO regression, resulting in a refined dataset of critical predictors. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied, improving the representation of underrepresented classes. Both Decision Tree and Random Forest models were trained on balanced datasets, with performance evaluated using accuracy, precision, recall, and F1-score metrics. The Random Forest model demonstrated superior accuracy (96.41%) compared to the Decision Tree (67.15%), as well as higher AUC values. Model interpretability was enhanced using SHAP (SHapley Additive exPlanations). This study underscores the utility of advanced machine learning techniques in educational analytics, paving the way for data-driven decision-making to support student achievement.
PERFORMANCE EVALUATION OF THE INDF.JK STOCK PRICE MOVEMENT PREDICTION MODEL USING RANDOM FOREST METHOD WITH GRID SEARCH CROSS VALIDATION OPTIMIZATION Zaria, Della; Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda
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/barekengvol19iss3pp2155-2168

Abstract

Investment in financial instruments in Indonesia has shown significant growth over time, with stocks often being the first choice for investors to invest money. Unfortunately, deciding to buy and sell stocks is not easy. When determining the right time to buy or sell stocks, volatile stock price movements and losses caused by wrong decisions are investors' problems. Thus, it is essential to analyze stock price movement predictions. This study aims to evaluate the prediction model's performance for PT Indofood Sukses Makmur Tbk (INDF.JK) stock price movement in the next 30 days to reduce the risk of possible losses and help the decision-making process. We used the Random Forest method and Grid Search Cross Validation (CV) optimization to form the model. The data used is the closing price of INDF.JK stock for the period January 2, 2014, to December 29, 2023, from Yahoo Finance, which is processed into eight types of stock technical indicators, namely SMA_5, SMA_10, SMA_15, SMA_30, EMA_9, MACD, MACD_Signal, and RSI. The research pipeline includes descriptive statistics, preprocessing, feature and target variables determination, data split, model formation without and with optimization, testing accompanied by performance evaluation, and comparison of the formed model. The results show that the prediction model of INDF. JK's stock price movement in the next 30 days has excellent performance, proven accurate by 90.8% with the application of Random Forest and Grid Search CV. The Random Forest prediction model with Grid Search CV optimization has better performance indicators than the Random Forest model without Grid Search CV optimization, which is shown by the increase of all indicator values. The relative Strength Index is the variable with the best performance for the prediction model. It can be used as the primary consideration for investors when deciding on the buying and selling process of INDF.JK stock in the next 30 days.
ANALYSIS OF FOOD SECURITY FACTORS WITH PATH MODELING SEGMENTATION TREE (PATHMOX) METHOD IN PARTIAL LEAST SQUARES IN WEST KALIMANTAN Harnanta, Nabila Izza; Perdana, Hendra; Satyahadewi, Neva
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/barekengvol19iss3pp2229-2242

Abstract

Food security is essential for ensuring community well-being by guaranteeing sufficient, safe, and nutritious food, particularly in regions with complex socio-economic conditions. This study analyzes food security in West Kalimantan Province by identifying key influencing factors, constructing a structural equation model, and segmenting regions based on their food security characteristics. Utilizing secondary data from the 2023 Food Security and Vulnerability Atlas (FSVA), the research employs the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the Path Modeling Segmentation Tree (PATHMOX) approach. The study incorporates ten indicators across four latent variables: food availability, food access, food absorption, and overall food security. The results reveal that regional segmentation using the PATHMOX approach effectively identifies data heterogeneity, categorizing West Kalimantan’s 14 districts/cities into two distinct groups based on the Human Development Index (HDI). The first group (10 regions) exhibits higher food consumption despite socio-economic challenges, whereas the second group (4 areas) demonstrates better food security yet lower intake levels. These findings highlight that food security is influenced by access, distribution, and policy implementation rather than solely by the Normative Consumption Production Ratio (NCPR). The insights from this study provide a foundation for developing targeted policies to enhance food security strategies in West Kalimantan Province, ensuring a more sustainable and equitable food system. By applying PATHMOX segmentation, policymakers can address regional disparities more effectively, fostering strategic interventions that improve food availability, accessibility, and utilization across different population groups.
USING A MONOTONE SEQUENCE OF FUNCTIONS TO DETERMINE THE SHORTEST ARC LENGTH OF CIRCLES CONNECTED ANY TWO POINTS ON SPHERE Djafar, Muhammad Kabil; Safiuddin, La Ode; Laome, Lilis; Muhtar, Norma; Budiman, Herdi; Cahyono, Edi; Gubu, La.; Alfian, Alfian; Alamsyah, Indra; Kohalsum, Askar
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/barekengvol19iss3pp1923-1932

Abstract

This paper discusses about arc length of circles that connected any two points on a sphere. On a sphere, there are infinitely many circles that connect any two points. Using a monotone sequence of functions, we can show that the shortest arc length of circle that connect any two points on sphere is the circle with its center at the origin.
IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS Sumargo, Bagus; Kadir, Kadir; Safariza, Dena; Asikin, Munawar; Siregar, Dania; Sari, Nilam Novita; Umbara, Danu; Hilmianto, Rizky; Kurniawan, Robert; Firmansyah, Irman
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/barekengvol19iss3pp1779-1790

Abstract

Stunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation.
MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL Pasaribu, Asysta Amalia; Kurnia, Anang
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/barekengvol19iss3pp2069-2082

Abstract

Aggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distribution properties. However, not all distributions have joint distribution properties; the joint distributions may be unknown, so motivating the use of the Copulas in this study is needed. Sometimes, the Copula model is introduced to construct joint distribution properties. The Copula model in this research is used in financial policies such as investment. In the investment sector, the aggregate risk comes from the sum of the single risks and returns. The model used in aggregate return is the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. The data used in this study is the closing price data for Apple and Microsoft stocks from January 01, 2010, to January 01, 2024. The best model selection is the model with the GARCH-Bivariate Normal approach with the smallest MSE value. Model GARCH(1,1)-Bivariate Normal is the best model for the volatility model of aggregate return.
MODELING TOTAL FERTILITY RATE IN INDONESIA: A COMPARISON OF FOURIER SERIES REGRESSION AND ELASTIC NET REGRESSION Fitri, Fadhilah; Ketrin, Melin Wanike; Almuhayar, Mawanda
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/barekengvol19iss3pp2017-2028

Abstract

The Total Fertility Rate (TFR) describes population growth and socioeconomic development of a country. This statistic plays an important role in predicting future social and economic conditions. Indonesia has experienced a steady decline in TFR over the past few decades, which can be a serious problem if this trend continues. Therefore, the factor influencing the decline must be found. The independent variables include the percentage of women graduating high school, percentage of the poor population, poverty gap index, poverty severity index, prevalence of inadequate food consumption, proportion of people living below 50 percent of median income, unemployment rate, infant mortality rate, child mortality rate, and percentage of ever-married women aged 15–49 years using contraception methods. The aim of this study is to compare both Fourier Series Regression and Elastic Net Regression models to see which approximation can capture the TRF phenomenon that occurs in Indonesia and identify the causes of its decline. Fourier Regression is chosen because there is a repetition of patterns in several variables. Moreover, this data is experiencing multicollinearity; hence, Elastic-net Regression is the best way because this method overcomes the limitations of each Ridge and Lasso approach. These models are compared to see which is more suitable to capture the relationships between these factors and TFR. The best model obtained will provide a clearer understanding of Indonesia's underlying drivers of fertility decline. The result is that the Fourier Series Regression can model all variables better than the Elastic-net Regression, and the independent variables can explain the proportion of variance in the dependent variables by 97.91%, with all the independent variables significantly affecting the Total Fertility Rate.
PERFORMANCE COMPARISON OF SOME TYPES OF WAVELET TRANSFORMS FOR TOURISM DATA PATTERN APPROXIMATION Bahri, Syamsul; Awalushaumi, Lailia; Syechah, Bulqis Nebulla; Pradana, Satriawan
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/barekengvol19iss3pp1905-1922

Abstract

Tourism is an economic sector that significantly supports the country's foreign exchange, including in West Nusa Tenggara Province (NTB). Data on tourist visits to an area, including to NTB, is a representation of time series data. The wavelet method is one of the tools that is quite reliable for modeling time series data. This study aims to model the number of tourist visits to NTB Province using discrete wavelet transformation decomposition to estimate data. Several Wavelet functions such as Haar wavelet, Symlet, Coiflet, Daubechies, Best-localized Daubechies, Fejér-Korovkin, and Bi-orthogonal Splines at various orders and levels of decomposition became the basis for simulation in modeling data. Based on the Root of Mean of Square Error (RMSE) indicator, this study compares the performance of each wavelet function against the modeling performance at various orders and levels of decomposition. Numerically, for the data on the total number of tourists visiting NTB Province, the best approximation was given by the Fejér-Korovkin wavelet order 4-th (fk4) and the best-localized Daubechies wavelet order 7-th (bl7) at the 2-nd level with an RMSE value of 2.2993 × 10-11. Partially, the best approximation of the data on the number of foreign tourist visits was given by the Bi-orthogonal Splines wavelet type order 2.6 (bior2.6) at the 2nd decomposition level with an RMSE value of 1.1718 × 10-11 and for the data on domestic tourist visits was given by the Fejér-Korovkin wavelet type order 4-th (fk4) and the best-localized Daubechies wavelet order 7-th (bl7) at the 2nd level with an RMSE value of 1.3352 × 10-11.
MULTIOBJECTIVE MODEL PREDICTIVE CONTROL IN STOCK PORTFOLIO OPTIMIZATION Y, Marlina; Solikhatun, Solikhatun
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/barekengvol19iss3pp2191-2206

Abstract

This paper proposes a Multi-Objective Model Predictive Control (MO-MPC) framework for stock portfolio optimization, designed to achieve an optimal balance between return maximization and risk minimization in volatile financial markets. This approach integrates Stochastic Model Predictive Control (SMPC) to predict asset returns and dynamically adjust portfolio allocation based on a discrete-time state-space model. The optimization problem is formulated as a multi-objective optimization and is solved using Multi-Objective Particle Swarm Optimization (MOPSO). Simulation results show that the MO-MPC approach significantly outperforms conventional methods regarding wealth maximization and risk minimization. Moreover, SMPC performs better than MOPSO in maximizing portfolio value and reducing risk. These findings confirm the potential of SMPC as an adaptive and reliable strategy for financial decision-making under uncertainty.
MODELING THE IDX30 STOCK INDEX USING STEP FUNCTION INTERVENTION ANALYSIS Rais, Rais; Afriza, Dini Aprilia; Setiawan, Iman; Sain, Hartayuni; Fadjryani, Fadjryani; Junaidi, Junaidi
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/barekengvol19iss3pp2057-2068

Abstract

The significant decline in the IDX30 stock index occurred due to an intervention, namely the COVID-19 pandemic, which affected market stability and investment decisions. This study aims to model and forecast the IDX30 stock index using intervention analysis with a step function, which is very suitable for capturing long-term external shocks. The methodology used includes the ARIMA (AutoRegressive Integrated Moving Average) model combined with step function intervention analysis to account for structural changes due to external disturbances. The data used is sourced from investing.com, consisting of weekly IDX30 stock index prices from January 2019 to December 2023. The results show that the COVID-19 pandemic significantly impacted the IDX30 index, causing a drastic decline. The best model identified is ARIMA (1,2,1) with intervention parameters b = 0, s = 0, and r = 1. The forecasting results range from Rp. 488 to Rp. 505, with a Mean Absolute Percentage Error (MAPE) of 1.9404%, which shows the forecasting results are very good, indicating high forecasting accuracy. These findings highlight the effectiveness of intervention analysis in modeling financial time series data affected by external disturbances.

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