Bulletin of Applied Mathematics and Mathematics Education
BAMME welcomes high-quality manuscripts resulted from a research project in the scope of applied mathematics and mathematics education, which includes, but is not limited to the following topics: Analysis and applied analysis, algebra and applied algebra, logic, geometry, differential equations, dynamical system, fuzzy system, etc. Graph theory, combinatorics, number theory, coding theory, cryptography, etc. Mathematical modeling in economics, physics, biology, medicine, engineering, control theory and automation, optimization, operational research, neural network, data science, machine learning, etc. Applied statistics and probability, finance mathematics, biostatistics, actuary, etc. RME-based mathematics education. Development studies in mathematics education. Mathematics Ability, includes the following abilities: reasoning, connection, communication, representation, and problem solving. Ethnomathematics, the results of research on the relationship between mathematics and culture practiced by members of cultural groups who share experiences and practices similar to mathematics that can be in a unique form. Application of ICT in mathematical learning and the design, development, and evaluation of the implementation or application of learning media.
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<![CDATA[Fuzzy regression model with Bayesian approach and its application to public health data ]]>
<![CDATA[Wan Ahmad, Wan Muhammad Amir bin]]>;
<![CDATA[Nizam Akbar, Nurul Asyikin]]>;
<![CDATA[Mohamad Ghazali, Farah Muna]]>;
<![CDATA[Mohd Noor, Nor Farid]]>;
<![CDATA[Aleng, Nor Azlida]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v2i1.3953
<![CDATA[The application of the Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression method through the SAS algorithm is the focus of this paper. As an alternative method of data analysis in biostatistics, this modified method can be used. This modified method includes a bootstrapping technique, residual normality checking and some Bayesian Linear Regression Modeling (BLR) enhancement through Fuzzy Bayesian Linear Regression. We illustrated the application of the algorithm for Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression in this paper.]]>
<![CDATA[The spectrum on prism graph using circulant matrix ]]>
<![CDATA[Triyani, Triyani]]>;
<![CDATA[Mashuri, Mashuri]]>;
<![CDATA[Anarkis, Bunga Tirai]]>;
<![CDATA[Riyadi, Slamet]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v2i1.5129
<![CDATA[Spectral graph theory discusses about the algebraic properties of graphs based on the spectrum of a graph. This article investigated the spectrum of prism graph. The method used in this research is the circulant matrix. The results showed that prism graph P2,s is a regular graph of degree 3, for s odd and s ≥ 3, P2,s is a circulantt graph with regular spectrum.]]>
<![CDATA[Face pattern recognition using Expectation-Maximization (EM) algorithm ]]>
<![CDATA[Purwadi, Joko]]>;
<![CDATA[Hernadi, Julan]]>;
<![CDATA[Suryantoro, M. Danang]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v2i1.5520
<![CDATA[This paper discuss about the use face patteren recognition which is now days become popular especialy on smartphone lock screen system. The method used in this research are the Expectation – Maximization (EM) Algorithm. EM Algorithm is an iterative optimization method for the estimation of Maximum Likelihood (ML) which is used in incomplete data problems. there are 2 stages, namely the Expectation stage E (E-step) and the Maximization stage M (M-step). These two stages will continue to be carried out until they reach a convergent value. The result of the research shows that EM Algorthm produce high accuracy, it’s about 95% on the data training and 83% accuracy on the data testing.]]>
<![CDATA[Effectiveness of Microsoft Kaizala and Google Classroom towards students’ mathematical communication skill and self-efficacy in learning statistics ]]>
<![CDATA[Andriyani, Andriyani]]>;
<![CDATA[Santosa, Achadi Budi]]>;
<![CDATA[Saryadi, Wahyu]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v2i1.5523
<![CDATA[This study aims to describe the effectiveness and differences in the effectiveness of the learning platform with Google Classroom and Microsoft Kaizala in terms of self-efficacy and students' mathematical communication skills. The population of this research is the XII grade students of SMK Negeri 1 Giritronto Wonogiri which consists of sevent classes. From the existing population, two classes were taken randomly, namely twelfth grade of TKJ-I and twelfth grade of TKJ-II as research samples. Twelfth grade of TKJ-I was given treatment by learning using the Microsoft Kaizala platform, while twelfth grade of TKJ-II was given treatment using the Google Classroom platform. The research data were analyzed by statistical one sample t-test, MANOVA test with Hotelling's T2 at a significant level of 0.05 and univariate test to determine which platform is more effective. The results showed that: (1) statistical learning using the Microsoft Kaizala platform was effective in terms of mathematical communication and self-efficacy, while the Google Classroom platform was effective in terms of mathematical communication but not effective in terms of self-efficacy; (2) there is a difference in effectiveness between the Microsoft Kaizala platform and Google Classroom. The Microsot Kaizala platform is more effective than Google Classroom in terms of the mathematical communication skills of class twelfth grade students of SMK Negeri 1 Giritronto Wonogiri.]]>
<![CDATA[Primary school teachers’ awareness on STEAM learning: A starting point to develop STEAM-CB textbook ]]>
<![CDATA[Khasanah, Uswatun]]>;
<![CDATA[Istiandaru, Afit]]>;
<![CDATA[Astuti, Dwi]]>;
<![CDATA[Adnan, Mazlini]]>;
<![CDATA[Prihaswati, Martyana]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v2i1.5565
<![CDATA[STEAM learning is an approach which integrates multidisciplinary knowledge while enriching students' skills to explore, try, ask, find, and build innovation, namely science, technology, engineering, art, and mathematics. We found that the learning resources of STEAM in Yogyakarta is very limited due to lack of knowledge about it. On the other hand, using local contexts as the starting point of learning is also important to foster character building. This preliminary research aims to provide insight on the teachers’ awareness on STEAM learning and textbook, as well as the design we figured out after discussing it with the teachers. It was a descriptive qualitative research involving 214 primary school teachers. We collected the data through questionnaire and focus group discussion. The results suggest that STEAM is quite a new concept for most of primary school teachers in Yogyakarta. Not many teachers used to or have developed the STEAM learning resources, especially the one integrated with the character building. Therefore, a STEAM textbook needs to be developed to fulfill the need. Furthermore, the design of the textbook could have the characteristics of integrating all components of STEAM (science, technology, engineering, art, mathematics), starting from local context to develop characters, improving creative and critical thinking, having knowledge and skill assessment, and using project-based learning model.]]>
