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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 2 (2025): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
COMPARISON FORECASTING BETWEEN SINGULAR SPECTRUM ANALYSIS AND LOCAL LINEAR METHOD FOR SHIP ACCIDENT SEARCH AND RESCUE OPERATIONS IN INDONESIA Recylia, Rien; Saifudin, Toha; Chamidah, Nur; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1329-1340

Abstract

As a maritime country strategically located along the world's leading transportation routes, Indonesia often faces increased ship accidents. Based on the Basarnas Statistics Book, ship accidents handled by Basarnas from 2021 to 2023 increased by 3%. This condition requires an effective forecasting method to carry out SAR operations to predict ship accidents in the Indonesian region in the future and assess the readiness and needs of Basarnas resources. This study compares the forecasting results obtained using the Singular Spectrum Analysis (SSA) and the Local Linear methods. Both methods do not require parametric assumptions. The data used in this study are divided into training data and test data. This data is secondary data obtained from the Basarnas Statistics Book. The training data in this study is the number of SAR operations from January 2021 to December 2022, while the testing data is from January 2023 to December 2023. From the analysis results, it is known that the method with the smallest MAPE is the Local Linear method with a MAPE of test data of 18.67% (good forecasting category), optimal bandwidth (h) = 4.299, and CV (h) = 231.39 where bandwidth is used to determine the level of smoothness of the estimate, while the CV (h) value is used to select the optimal bandwidth that minimizes the estimation error. At the same time, the SSA method has a MAPE of 40.27% (fair forecasting category). This shows that the Local Linear method provides a more accurate forecast of the number of SAR operations related to ship accidents in Indonesia. This research contributes to the SDGs to make Basarnas an effective and accountable institution and improve the planning and decision-making process in SAR operations through accurate forecasting research is relevant to accurate forecasting.
ANALYSIS OF RON 92 OIL BASED ON MORPHOLOGY AND HISTOGRAM TECHNIQUES Sari, Indah Purnama; Azis, Zainal; Hasibuan, Ahmad Riady
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1341-1352

Abstract

In oil quality monitoring, digital imagery has become an essential tool. With the advancement of technology, digital imagery can be used to visualize oil samples and perform analysis quickly and accurately. However, effective image processing techniques are needed to generate useful information from digital images. The main problem faced in fuel quality evaluation is the inaccuracy and inconsistency of manual methods. Manual methods often require expensive equipment and trained workers and are prone to human error. Therefore, a more efficient and accurate method is needed. Morphological and histogram techniques on digital images offer a potential solution to this problem. One of the common techniques used in image processing is morphological techniques, which involve mathematical operations on images to change or describe certain image features. This technique can help identify important structures and patterns in Ron 92 oil images, such as quality and cleanliness. In addition, histograms are helpful statistical tools in image analysis, which represent the distribution of pixel intensities in an image. Histogram analysis can provide insight into the distribution of pixel intensity values ​​in an oil image, which is relevant to the quality and homogeneity of the oil.
SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA Fernanda Rifki, Kevin Agung; Ahsan, Muhammad; Mashuri, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1353-1364

Abstract

Quality control has an important role in the manufacturing process. One of the statistical tools used in quality control is Statistical Process Control (SPC). The SPC product is a control chart. A control chart is a graphical tool used to determine if a process is under statistical quality control, helping to identify issues and drive quality improvements. Control charts are usually used to control variables or attribute data quality. Commonly used variable data is data with mean and variability characteristics. Various types of control charts are control charts for mean, control charts for variability, and simultaneous control charts designed to control mean and variability simultaneously. In real-field practice, manufacturing requires multivariate process control because many variables must be controlled. This research proposes a multivariate simultaneous control chart, the Exponentially Weighted Moving Average Max Multivariate (EWMA Max-Mchart). This control chart can handle multivariate process control simultaneously, both process mean and process variability. This research tests the performance of control charts with a simulation study using synthetic data with several process mean conditions and a covariance matrix. As a comparison, the development of the previous Max-M control chart was also tested. Based on the synthetic data generated, a performance comparison was made by looking at the suitability of in-control and out-of-control. The comparison results show that the EWMA Max-Mchart has better quality control performance if there is a shift than the Max-Mchart.
PMC-LABELING OF SOME CLASSES OF GRAPHS CONTAINING CYCLES Ponraj, R; Prabhu, S; Sivakumar, M
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1445-1456

Abstract

Let be a graph with p vertices and q edges. We have introduced a new graph labeling method using integers and cordial-related works and investigated some graphs for this labeling technique. Using this labeling concept, we have examined the graphs like path, cycle, star, complete graph, comb, and wheel graph. The first research paper on graph theory was published by Leonhard Euler. However, he did not use the word ‘graph’ in his work. In the early stages of the development of the subject, the vertices of a graph were specified as , and the edges were denoted by, . In recent times, several researchers have attempted to provide different types of labeling to the vertices and edges of a graph by identifying the relevant mathematical properties. The present paper provides a novel method of labeling by employing integers, which may form a foundation for future research work. In this paper, we investigate the pair mean cordial labeling behavior of some cycle-related graphs like the ice cream graph, closed web graph, circulant graph, zig-zag chord graph, pentagonal circular ladder, djembe graph, quadrilateral friendship graph, and origami graph.
STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES Satriani, Rineka Brylian Akbar; Farikhin, Farikhin; Surarso, Bayu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1365-1378

Abstract

Stock prices have unstable movements, so forecasting is needed to decide to invest appropriately according to the strategy. Fuzzy Time Series (FTS) uses fuzzy sets to forecast future time series values using historical data. However, interval partitioning in FTS needs to be considered as it can affect the forecasting results. FCM is applied to solve the problem of interval assignment in the universe of discourse. It allows the evaluation of the distribution of historical data and forming intervals of different sizes. Type 2 Fuzzy Time Series (T2FTS) is an extension of FTS to improve forecasting performance and refine fuzzy relationships. This research aims to improve forecasting accuracy using the Fuzzy C-Means (FCM)-T2FTS combination. This research uses daily data on BBRI stock prices from January 2023 to May 2024, with the variables used being close, high, and low prices. The results showed that determining the interval length using unequal length is more efficient than fixed interval length and can improve model performance, demonstrated from the MAPE values of T2FTS and FCM-T2FTS, which are 2.09% and 1.97%, respectively, the difference between the two MAPEs, is 0.12%. Hence, FCM-T2FTS is 12% more efficient than T2FTS. Therefore, FCM-T2FTS can improve forecasting accuracy.
FOREST FIRE ANALYSIS FROM PERSPECTIVE OF SPATIAL-TEMPORAL USING GSTAR (p;λ_1,λ_2,…,λ_p) MODEL Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1379-1392

Abstract

West Kalimantan is particularly susceptible to the devastating effects of forest fires, among the natural disasters that have a significant impact. One of the indicators that can be used to identify forest fires is the presence of hotspots. The term "hotspot" refers to data that has both spatial and temporal characteristics. Using the Generalized Space-Time Autoregressive (GSTAR) model combined with the Queen Contiguity weight matrix, this research aims to model and forecast the confidence level of hotspots in Kubu Raya Regency and its surrounding areas. We chose the GSTAR model because of its ability to model spatial interactions between locations and temporal change patterns over time. According to NASA FIRMS, the data used in this study were confidence level hotspot data, covering the period from January 2014 to August 2024. To define locations for modeling, the study area was divided into grids measuring degrees. The maximum confidence level value in each grid was used to represent the highest potential fire risk. The research process consists of the following stages: data preparation, stationarity testing, calculation of the Queen Contiguity spatial weight matrix, identification of model orders based on STACF and STPACF plots, and estimation of model parameters to predict hotspot confidence levels. The GSTAR (3;1) model was selected as the best model because it satisfies the white-noise assumption and has a MAPE value of 14.78%. Based on the MAPE, the GSTAR (3;1) model can provide reasonably accurate predictions for the confidence level of fire points over the following three periods. The prediction results indicate a decline in the fire point confidence level across all locations during the following three periods. The findings of this study can support the optimization of resource allocation in the prevention of forest fires.
PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE Kho, Delvian Christoper; Purnomo, Hindriyanto Dwi; Hendry, Hendry
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1393-1408

Abstract

Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trends such as sideways movement, uptrends, downtrends, and volatility. Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively.
A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM Berliana, Sarni Maniar; Samosir, Omas Bulan; Karim, Rafidah Abd; Valenzuela, Victoria Pena; Wahyuni, Krismanti Tri; Alfian, Andi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1409-1420

Abstract

Suicide, a global health challenge identified in Goal 3 of the global agenda for enhancing worldwide well-being, demands urgent attention. This study focused on predicting suicidal thoughts using machine learning, leveraging the 2021 National Women's Life Experience Survey (SPHPN) involving women aged 15 to 64. Analyzing 11,305 ever-married women, 504 (4.5%) reported experiencing suicidal thoughts. The outcome variable was binary (1 for suicidal thoughts, 0 for none). The study used seven predictors: age, education level, residence type, physical and sexual violence, smoking frequency, alcohol consumption, and depression. Ordinary logistic regression and SMOTE-based logistic regression were applied. The former identified physical violence, depression, and sexual violence as crucial factors, while the latter emphasized physical violence, sexual violence, and age. In cases of class imbalance, the SMOTE-enhanced model exhibited improved performance in terms of sensitivity, false positive rate, balanced accuracy, and Kappa statistic, with lower standard errors of parameter estimates. The findings highlight the importance of addressing violence and mental health in policies aimed at reducing suicidal thoughts among women. Policymakers can use these insights to develop targeted interventions, and healthcare providers can identify high-risk individuals for timely interventions. Community programs and public health campaigns should promote mental well-being and prevent suicidal behaviors using these findings. Future research should include more predictors, diverse populations, and longitudinal data to better understand causal relationships and timing. Interdisciplinary collaboration and advanced machine learning techniques can enhance predictive accuracy and model interpretability.
APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS Joan, Karin; Irawan, Robyn; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1421-1432

Abstract

Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. Multi-Output Machine Learning is essential as a decision support tool because decision-making in many problems generally considers multiple factors. The use of Multi-Output Machine Learning is more advantageous than conventional machine learning in terms of time efficiency, addressing data limitations, and ease of maintenance. These benefits will significantly impact cost savings for industries utilizing Big Data. The models used in this research include Multivariate Regression Tree, Multivariate Random Forest, and Multi-Output Neural Network. The Multivariate Regression Tree and Multivariate Random Forest are developed by modifying the splitting function using Mahalanobis distance. The topological changes introducing shared and private hidden layers are the key development of the Multi-Output Neural Network. The prediction results indicated a trade-off in error between two output variables when comparing the Multivariate Regression Tree and Multivariate Random Forest with their single output counterparts. Meanwhile, the Multi-Output Neural Network model successfully improved the prediction results for both output variables. This research also introduces Mixed Multi-Output Machine Learning, which can predict numerical and categorical output variables. The Mixed Multi-Output Machine Learning model utilizes the logit values from the Logistic Regression model to extend the range of prediction results beyond the 0 to 1 interval. Multi-Output Neural Network is the sole model that produces predictions with relatively small errors and high accuracy values.
ASSESSING UNEMPLOYMENT RATES IN TANAH DATAR REGENCY: INSIGHTS FROM SMALL AREA ESTIMATION Winanda, Rara Sandhy; Khairani, Yasyfin Ikrima; Permana, Fajar Wisga
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1433-1444

Abstract

Unemployment is a significant issue in Indonesia's labor market. The unemployment rate is measured by the Open Unemployment Rate (OUR) through the National Labor Force Survey (SAKERNAS) conducted by BPS. In 2022, the OUR in Tanah Datar District reached its highest level in the past fifteen years. This rise in unemployment contrasts with the declining poverty rate, unlike other districts/cities in West Sumatra. To address the increasing unemployment, detailed information at the smallest administrative level is necessary. However, because the limited sample size in SAKERNAS does not allow for direct estimation of the OUR with sufficient accuracy, this study aims to overcome this limitation by estimating the OUR at the subdistrict level using indirect estimation through Small Area Estimation (SAE). The SAE method applied is Empirical Best Linear Unbiased Prediction (EBLUP), using the Restricted Maximum Likelihood (REML) estimation model. This research uses secondary data obtained from the National Labor Force Survey (SAKERNAS) of Tanah Datar Regency for the August 2022 period and the Village Potential data (PODES) of Tanah Datar Regency in 2021. The findings indicate that three subdistricts—Pariangan, Lintau Buo Utara, and Padang Ganting—have higher OUR values than Tanah Datar Regency in 2022, with rates of 6.00%, 6.01%, and 11.03%, respectively. The factor that influences the high OUR in these sub-districts is the variable percentage of the male population, which in this model has a large contribution to the calculation of OUR. The indirect estimations using EBLUP are deemed reliable, as the RSE value is below 25%. Therefore, the EBLUP indirect estimation results for OUR at the subdistrict level in Tanah Datar Regency can guide local government efforts to take targeted actions to reduce unemployment, especially in areas with high OUR.

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