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.
Articles
50 Documents
<![CDATA[Identifying malaria disease through red-blood microscopic image with XGBoost and random forest methods ]]>
<![CDATA[Fajriyah, Rohmatul]]>;
<![CDATA[Muhajir, Muhammad]]>;
<![CDATA[Abdullah, Ahmad Hussain]]>;
<![CDATA[Ayu, Devina Gilar]]>;
<![CDATA[Rahman, Iqbal Fathur]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 4 No. 2 (2024) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v4i2.11740
<![CDATA[Blood cells that flow in the human body provide information to diagnose a disease. The information provided can be obtained through images of these blood cells using image processing techniques. Malaria is a very deadly disease and can affect everyone. Patients with malaria will experience anaemia because the red blood cells or erythrocytes are contaminated with plasmodium. This study offers an alternative solution to malaria disease identification through the image classification of red blood cells, by applying image processing and image classification methods with XGBoost and random forest. The research was conducted using the R programming language in RStudio and Python. The accuracy of XGBoost and random forest methods were 71.26% and 77.58%, respectively. Therefore, the random forest provided a better optimal classification model with higher accuracy. The model is used to build an application which is R web-based, RShiny. In practice, this application can be used by health workers in classifying patients based on red blood cell images such that the health centre would be easier to manage the existing patients.]]>
<![CDATA[Developing an RME-based 3D storybook with AR technology to enhance spatial ability ]]>
<![CDATA[Rochmat, Syaiful]]>;
<![CDATA[Andriyani, Andriyani]]>;
<![CDATA[Siswanto, Deny Hadi]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.10880
<![CDATA[This research aims to develop a 3D storybook using realistic mathematics education (RME) approach and augmented reality (AR) technology to enhance students' spatial ability. We used the ‘Analysis, Design, Development, Implementation, and Evaluation’ (ADDIE) development model and involved 34 eighth-grade students of SMP N 15 Yogyakarta – a public junior high school in Indonesia. During the development, we underwent several interviews, observations, asking experts’ validation to the content and the media, asking students’ response to the product, and tests related to the students' spatial ability. The results suggest that the content validation got an average score of 109 (good criteria), the media validation got an average score of 48.16 (good criteria), and the students response got an average score of 78.36 (very good criteria). At the end of the stage, there are still found an obstacle during the testing of the 3D storybook, namely the time required to load the 3D content. It makes some students encounter difficulty to access it, indicating the need for further development. However, the final product could facilitate the students to exercise their spatial ability using the 3D storybook while learning geometry.]]>
<![CDATA[TGT vs STAD: Comparing vocational students’ performance between two cooperative learning models ]]>
<![CDATA[Pratiwi, Aulia Cahya]]>;
<![CDATA[Khasanah, Uswatun]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.11012
<![CDATA[Various factors – such as low interest, motivation, and poor problem-solving skills – contribute to vocational high school students' low mathematics learning outcomes. The cooperative learning models of Teams Games Tournament (TGT) and Student Team Achievement Division (STAD) are the alternative teaching strategies proven to improve student learning outcomes. This study aims to compare the effects of the two models on the mathematics learning outcomes of eleventh-grade students at SMK Negeri 1 Salam – a vocational high school in Indonesia. The research uses a quantitative approach with a true experimental design of the posttest-only control group design type. We randomly selected two classes from a homogeneous population, which applied the TGT model to one class and the STAD model to the other. They used a learning outcome test that experts had validated and tested for reliability. Data were analyzed using the independent sample t-test and one-tailed test. The research results show a significant difference in the influence between the TGT and STAD models on mathematics learning outcomes, with the average score of the TGT group being higher (78.57) compared to the STAD group (74.11). One-tailed tests show that the TGT model has a significantly greater impact than STAD. Thus, the TGT model is considered more effective in improving the mathematics learning outcomes of vocational school students because it can create a competitive, enjoyable learning atmosphere and encourage active student participation.]]>
<![CDATA[Multicollinearity problem-solving with Jackknife Ridge Regression: A case study on slum conditions in Bone Bolango ]]>
<![CDATA[Adam, Dwi Putri Juniar]]>;
<![CDATA[Nasib, Salmun K.]]>;
<![CDATA[Adityaningrum, Amanda]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.12759
<![CDATA[Slum conditions in Indonesia, particularly in Gorontalo Province's Bone Bolango District, are a significant challenge to sustainable development. This research aims to identify the key factors contributing to slum conditions in the strategic economic areas of Kabila, Suwawa, and Tilongkabila using Jackknife Ridge Regression (JRR) analysis to address multicollinearity and overfitting issues. Data from the Regional Development Planning Board (BAPPEDA) Bone Bolango District's 2023 document was used, with a sample of 40 urban villages and villages. The result showed that there is a high collinearity between two independent variables, necessitating the use of JRR. The JRR model identified seven independent variables significantly related to slum value. The regression model explained 83% of the variability in slum conditions. This study provides methodological depth through the JRR framework, which enables accurate slum analysis where traditional models (like OLS) tend to fall short. It emphasizes the need for Bone Bolango to prioritize its policy initiatives by focusing on the seven independent variables. Additionally, the framework demonstrates scalability, making it adaptable to other Indonesian provinces that face similar challenges with slum data.]]>
<![CDATA[Simulating Bitcoin price movements with the Bates model and Monte Carlo methods ]]>
<![CDATA[Staenly, Staenly]]>;
<![CDATA[Irsan, Maria Yus Trinity]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.12815
<![CDATA[This study investigates the price dynamics of Bitcoin, a highly volatile and speculative digital asset. Using daily closing price data from January 2023 to January 2024, we apply the Bates model, which combines stochastic volatility with jump-diffusion processes, to better capture both continuous fluctuations and sudden, large price changes in the market. The model parameters are calibrated using historical data and evaluated through Monte Carlo simulation with 10,000 generated price paths over a 31-day forecast horizon. The results demonstrate a strong short-term predictive performance, with a Mean Absolute Percentage Error (MAPE) of 4.32%. This indicates that the Bates model can capture both volatility clustering and abrupt shifts, which are characteristic of Bitcoin. The findings suggest that this approach provides a valuable tool for risk management and investment decision-making in highly uncertain and dynamic markets.]]>
<![CDATA[Relationship between opening and closing of stock prices for IHSG and issuers: A case study in the Indonesia Stock Exchange ]]>
<![CDATA[Manik, Efron]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.12975
<![CDATA[Identifying the most influential variables in stock price movements is a crucial aspect of developing an accurate mathematical model for predicting market trends. This study analyzes two main variables: the composite stock price index (IHSG) and the closing price of company shares, to determine the extent of their influence on stock prices on the observation day. The findings indicate that the IHSG from one day prior to the observation day does not have a significant impact on the closing price of a particular stock. This means that changes in the IHSG on the previous day cannot be used as the main indicator to predict a company's stock price on the following day. On the other hand, the closing price of a company's stock on the previous day has a strong correlation with the company's closing stock price on the observation day, which is 70%. Besides historical stock price factors, irrational investor behavior can cause volatility that does not fully reflect a stock’s fundamental value. Therefore, it is essential to consider investors' psychological aspects in stock market analysis.]]>
<![CDATA[Predicting financial distress in Indonesian life insurance companies with classification methods and synthetic features generation ]]>
<![CDATA[Purwanto, Dwi]]>;
<![CDATA[Prastyo, Dedy Dwi]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.13114
<![CDATA[Financial problems in life insurance companies can become serious if not addressed immediately. Companies experiencing financial distress, for instance, are unable to meet their obligations to pay their liabilities. A company can be categorized as experiencing financial distress when it has an RBC ratio of less than 120%—based on regulation by the Indonesian Finance Service Authority—or ROA < 0 (suffering loss). Therefore, financial distress prediction is carried out to assess the company's current financial condition so that it can be handled early. In this study, we aimed to predict financial distress of Indonesian life insurance companies. We utilized the Support Vector Machine (SVM) classification method, Generalized Extreme Value Regression (GEVR), and Extreme Gradient Boosting (XGB) and by incorporating synthetic feature generation in variable selection. The results of financial distress prediction obtained the best model that can predict the financial condition of life insurance companies in Indonesia at each size, where for sizes 0 and 1, the XGB model with variable selection produces accuracy values of 98.00% and 94.10%, respectively, and AUC values of 100% and 87.40%. Then, at size 2, we can use Stepwise Generalized Extreme Value Regression with accuracy and AUC results of 90.20% and 82.60%, respectively. Each addition of size to the time window classification results tends to reduce the model's performance in predicting the financial condition of life insurance companies in Indonesia.]]>
<![CDATA[Comparing the performance of DTID3 and DTID3-Smote methods in predicting the rain events with unbalanced classes ]]>
<![CDATA[Zayanti, Des A.]]>;
<![CDATA[Eliyati, Ning]]>;
<![CDATA[Resti, Yulia]]>;
<![CDATA[Hoiri, Sajiril]]>;
<![CDATA[Kresnawati, Endang S.]]>;
<![CDATA[Dewi, Novi R.]]>;
<![CDATA[Amran, Ali]]>;
<![CDATA[Yani, Irsyadi]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.13122
<![CDATA[Prediction of rainfall events in a region is important for many aspects of life. However, the majority of datasets that predict rainfall events have an unbalanced distribution of observations in their classes, including the Prabumulih city dataset, South Sumatra. DTID3 provides very satisfactory performance in many cases of prediction, while the Smote technique is useful for balancing the distribution of data classes. This study aims to compare the performance of the DTID3 and DTID3-Smote methods in predicting rainfall events in Prabumulih City. The main contribution of this study compared to previous studies is that the DTID3 and Smote methods are used together to predict rainfall events, especially in Prabumulih City. Using training data from 2017-2022 and test data from 2023, the results show that the DTID3-Smote method has a better performance measure than the decision tree method in predicting rainfall events in Prabumulih City. In the decision tree method, the accuracy, precision, recall, specificity, and f1-score metrics are 73.56%, 81.91%, 50.94%, 91.22%, and 62.81%, respectively. In the decision tree-SMOTE method, the values are respectively 74.66%, 82.61%, 53.44%, 91.22%, and 64.9%.]]>
<![CDATA[Pipeline on microarray data analysis: Pre-processing ]]>
<![CDATA[Fajriyah, Rohmatul]]>;
<![CDATA[Kongchouy, Noodchanath]]>;
<![CDATA[Ayudhaya, Wanvisa Saisanan Na]]>;
<![CDATA[Yotenka, Rahmadi]]>;
<![CDATA[Danarwindu, Ghiffari Ahnaf]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i1.12539
<![CDATA[Bioinformatics is blooming and its data are store in some repository offline and or online. Yet some basic concepts are not fully disseminated. The paper intends to provide the reader with a review of one important concept in the pipeline bioinformatics data analysis of microarray, pre-processing. In pre-processing, there are four steps, background correction, normalization, probe correction and summarization. Each step consists of several methods, and we describe each method to give a better understanding on how it works theoretically. We focused on microarray data from Affymetrix platform with single-color chip.]]>
<![CDATA[Shopping pattern segmentation: HAC versus K-Means performance analysis ]]>
<![CDATA[Hidayati, Nur Arina]]>;
<![CDATA[Khasanah, Uswatun]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 2 (2025) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v5i2.14502
<![CDATA[Despite widespread use in consumer analytics, clustering techniques remain underutilized for analyzing household basic food commodity consumption patterns, particularly for developing localized retail strategies and targeted food security policies in resource-constrained contexts. This study addresses this practical gap by systematically comparing Hierarchical Agglomerative Clustering (HAC) and K-Means performance on essential consumption patterns across seven commodities: bread, vegetables, fruit, meat, poultry, milk, and wine. Using dual validation metrics, Silhouette Coefficient and Davies-Bouldin Index, we evaluate clustering effectiveness specifically for small-scale household datasets typical of regional food policy environments. HAC demonstrated superior cluster stability (Silhouette score = 0.2936, DBI = 0.8977) compared to K-Means (0.2912, 0.9871), enabling identification of three actionable consumption segments, namely budget-conscious households with economical protein consumption, high spender households with premium patterns across categories, and balanced/selective households preferring bread and wine. These empirically-derived segments provide implementable frameworks for food subsidy targeting, inventory optimization in local retail contexts, and nutrition intervention program design. The findings demonstrate that methodologically rigorous clustering analysis yields policy-relevant household segmentation even with constrained data, offering practical guidance for evidence-based food security interventions where basic commodity consumption directly informs resource allocation decisions.]]>
