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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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OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE Ardiyansa, Safrizal Ardana; Maharani, Natasha Clarissa; Anam, Syaiful; Julianto, Eric
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1053-1066

Abstract

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.
APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM Syaripuddin, Syaripuddin; Amijaya, Fidia Deny Tisna; Wasono, Wasono; Tulzahrah, Shanaz; Suciati, Rara
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1067-1080

Abstract

Stock portfolios can be modeled into quadratic programming problems using the Markowitz mean-variance model. Quadratic programming problems can be solved using two methods, namely classical and heuristic methods. In this research, the classical method uses Wolfe’s method, while the heuristic method uses the particle swarm optimization (PSO) algorithm. This research aims to determine optimal results in portfolio problems using two methods, namely Wolfe’s method and the PSO algorithm. The data used in this research is data from 10 stock companies that distribute the highest dividends in the IDX High Dividend 20 category for the 2022 period. The research results discuss the portfolios of PT Astra International Tbk and PT. Indo Tambangraya Megah Tbk. Based on the result, using Wolfe’s method, the ASII and ITMG stock portfolios are obtained, namely the optimal proportion of ASII shares = 0.76401 or 76.401% and ITMG shares = 0.23598 or 23.598%, while the PSO algorithm obtains a portfolio of ASII and ITMG shares, namely ASII shares = 75.02% and ITMG shares = 24.98%. Compared to Wolfe’s method, the PSO algorithm has a smaller Z value 5.7.
OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION Sholawatunnisa, Dinda Pusparahmi; Suadaa, Lya Hulliyyatus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1081-1094

Abstract

Efficiently managing lengthy textual data, particularly in online news, is crucial for enhancing the performance of long text classification. This study delves into innovative approaches to streamline the Gross Domestic Product (GDP) computation process by harnessing modern data analytics, Natural Language Processing (NLP), and online news sources. Leveraging online news data introduces real-time information, promising to improve the accuracy and timeliness of economic indicators like GDP. However, handling the complexity of extensive textual data poses a challenge, demanding advanced NLP techniques. This research shifts from traditional word-weight-based methods to keyword-based extractive summarization techniques to address this. These tailored approaches ensure that selected sentences align precisely with specific keywords relevant to the research case, such as GDP growth rate detection. The study emphasizes the necessity of adapting summarization methods to capture information in unique research contexts effectively. According to classification results, the implementation of sentence selection successfully demonstrated improved performance in terms of classification accuracy. Specifically, there was an average accuracy increase of 0.0226 for machine learning and 0.0164 for transfer learning models. Additionally, in terms of computational efficiency, sentence selection also accelerates processing time during hyperparameter tuning and fine-tuning, as observed using the same computational resources.
CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES Nurdiansyah, Denny; Ma'ady, Mochamad Nizar Palefi; Sukmawaty, Yuana; Utomo, Muchammad Chandra Cahyo; Mutiani, Tia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1095-1104

Abstract

Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts. In this case, data mining techniques can identify patterns and relationships in population data. The K-Means algorithm is a clustering technique that divides data into groups or clusters based on similar characteristics. This research aims to apply the K-Means method with various approaches to clustering sub-districts in the Bojonegoro district according to population data. The research method used is a quantitative method with an exploratory study in the application of the K-Means method with a variety of approaches, namely the use of the Kernel K-Means method by utilizing the mapping function to map data to a higher dimension before the clustering process. In addition, the Fast K-Means method is used, which reduces the model training time to improve the cluster-centered recalibration problem as the amount of data increases. The data source used in this research is secondary population data in the form of birth, death, migrant, and moving variables obtained from the Satu Data Bojonegoro website developed by the Bojonegoro Regency Government. It is found that the best K-Means approach is the Kernel K-Means method with a number of clusters of 5. The performance of the cluster method is evaluated by measuring the average distance within the cluster. The data coordinate pattern in the Kernel K-means method clustering shows a smooth initial trend when the value of the number of clusters is 5 so that the clusters formed are obtained clearly. The conclusion from this study's results is that the K-Means method's best approach in grouping sub-districts in Bojonegoro district is the Kernel K-Means approach.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
FUZZY APPLICATION (MAMDANI METHOD) IN DECISION-MAKING ON LED TV SELECTION Ma'rif, Erni Fatun; Abadi, Agus Maman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1117-1128

Abstract

Science is now developing very quickly. Information technology has been used in various places. Using computers in business, government, and personal activities shows how important science and technology are in helping human activities. One method used to solve various problems is fuzzy logic. Several types of Fuzzy are classified as Fuzzy Inference Systems (FIS), namely Tsukamoto, Mamdani, and Sugeno. The application of vague logic in making decisions about choosing an LED TV is to make it easier to select electronic media. This research aims to help people who need clarification on the many LED TV choices currently available. So, we need a decision-making method to help people choose an LED TV that suits their needs and budget. One of the methods used in this research is the Mamdani method. There were 50 LED TV brands in this research, and the criteria used in selecting LED TVs were based on size, resolution, and price. An LED TV that meets the medium size, high resolution, and normal price criteria will be purchased. The LED TV data that meets the medium size, high resolution, and normal price criteria is the Samsung UA43AU7000KXXD brand LED TV. However, the actual decision remains based on the buyer's decision.
T-IDEAL AND α-IDEAL OF BP-ALGEBRAS Gemawati, Sri; M, Musraini; Putri, Ayunda; Marjulisa, Rike; Fitria, Elsi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1129-1134

Abstract

This paper explores the characteristics of two distinct ideal types within BP-algebra, specifically T-ideal and -ideal. Initially, we elucidate the characteristics of the T-ideal in BP-algebra, establishing its connections with the perfect, normal, and normal ideal in BP-algebra. Subsequently, we demonstrate that the kernel of a homomorphism in BP-algebra constitutes a T-ideal. Moving forward, we delineate the properties of -ideal in BP-algebra, highlighting its relationships with ideal and filter in the context of BP-algebra. Additionally, we explore the characteristics of -ideal and subalgebra in 0-commutative BP-algebra. Finally, it is proven that the kernel of a homomorphism in 0-commutative BP-algebra can be identified as an -ideal.
FUZZY LOGIC APPLICATION FOR DETERMINING THE FEASIBILITY OF NICKEL MINING IN SOUTHEAST SULAWESI PROVINCE Agustina, Ni Luh Ika Tri; Abadi, Agus Maman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1135-1146

Abstract

This study uses the Mamdani method to assess the feasibility of nickel mining locations in Southeast Sulawesi Province. Despite the crucial role of mining in the Indonesian economy, research on the site feasibility decisions in mining using the Mamdani method still needs to be completed. Therefore, this study addresses this knowledge gap by providing new contributions and effective solutions. The Mamdani method is employed in the various stages of mining activities, particularly in feasibility studies, which are the main focus. Mining feasibility studies involve both technical and non-technical analyses, encompassing aspects such as nickel reserves and environmental impacts. This research seeks to expand the use of the Mamdani method in mining site feasibility decisions, offering sustainable and environmentally responsible solutions. The research results show that North Konawe Regency has very large estimated nickel reserves but has a relatively low environmental impact and is quite far from the port, thus achieving a high location suitability score for mining. On the other hand, Konawe Regency has lower nickel reserves but has quite a large environmental impact, and the distance to the port is quite far, so the location feasibility score is lower. The outcomes of this research are expected to provide new insights, fill knowledge gaps, and serve as a valuable reference for future mining site feasibility decision-making. The translation is accurate, well-structured, and free from plagiarism.
CRAMER’S RULE IN MIN-PLUS ALGEBRA Putri, Zakia Nur Ramadhani; Siswanto, Siswanto; Kurniawan, Vika Yugi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1147-1154

Abstract

Cramer’s rule is one of a method for solving a system of linear equations in conventional algebra. The system of linear equation can be solved using Cramer’s rule if . Max-plus algebra is a set where is a set of real numbers, equipped with biner operations and where and . Min-plus Algebra is a set where is a set of real numbers, equipped with biner operations and where and . In max-plus algebra has been formulated Cramer’s rule to solve a system of linear equations. Because max-plus algebra is isomorphic to min-plus algebra, Cramer’s rule can be formulated into min-plus algebra. The purpose of this research is to determine the sufficient conditions for a system of linear equations can be solved using Cramer’s rule. The method used in this research is a literature study that reviews previous research related to min-plus algebra, max-plus algebra, and Cramer’s rule in max-plus algebra. By using the appropriate analogy in max-plus algebra, we can determine the sufficient conditions so that a system of linear equations in min-plus algebra can be solved using Cramer’s rule. Based on the research, the sufficient conditions for a system of linear equations can be solved using Cramer’s rule are for and with the Cramer’s rule is . For an invertible matrix A, Cramer’s rule can be written as .
COMBINATION OF ETHNOMATHEMATICS AND THE MOZART EFFECT TO IMPROVE PROBLEM-SOLVING SKILLS AND MATHEMATICAL DISPOSITION Kusuma, Dianne Amor; Ruchjana, Budi Nurani; Widodo, Sri Adi; Dwipriyoko, Estiyan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1155-1166

Abstract

The background of this research is that student learning outcomes in analytical geometry lecture during the transition from pandemic to Covid-19 endemic are still low, which is due to a lack of student interest in learning, and they are still accustomed to online learning, thus having an impact on their low problem-solving skills and mathematical disposition. This research aims to determine to what extent the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition from pandemic to endemic COVID-19, so the research is important to do. The implementation of ethnomathematics and the Mozart effect in mathematics learning is unique because it is a combination of learning approaches that have never been used before in Indonesia and other countries. The research method used was a quasi-experimental non-equivalent control group design because this research was experimental and sample determination was not carried out randomly, but using purposive sampling technique on the second-semester students of the mathematics undergraduate program, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran. The instruments used in this study were problem-solving skills test, mathematical disposition scale, and students’ attitude questionnaire toward learning with the implementation of ethnomathematics and the Mozart effect. The results showed that: (1) problem-solving skills of students who received learning by implementing ethnomathematics and the Mozart effect are better than students who achieved direct instruction; (2) mathematical disposition of students who received learning by implementing ethnomathematics and the Mozart effect is better than students who achieved direct instruction; and (3) students are interested and motivated to learn mathematics by implementing ethnomathematics and the Mozart effect. This research concludes that the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition period from the pandemic to endemic COVID-19. It can be seen from good average post test scores achieved by students.

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