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
61 Documents
<![CDATA[Application of Multiple Linear Regression Models for prediction of rice production yields in Central Lampung ]]>
<![CDATA[Yani, Nadia Fitri]]>;
<![CDATA[Muthoharoh, Luluk]]>;
<![CDATA[Winardi, Abdy]]>
<![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.v5i2.14559
<![CDATA[Rice production is a crucial component of agricultural sustainability and food security in Indonesia, particularly in Central Lampung. This study aims to analyze the influence of planting area and harvested area on rice production using a multiple linear regression approach. The analysis employs secondary time-series data and applies an ordinary least squares (OLS) method with a logarithmic transformation of the dependent variable to address heteroskedasticity issues. Descriptive statistics and classical assumption tests, including normality, multicollinearity, heteroskedasticity, and autocorrelation tests, were conducted to ensure model validity. The results indicate that harvested area has a statistically significant positive effect on rice production, while planting areas shows a negative but statistically insignificant effect. The regression model demonstrates strong explanatory capability with an R-squared value of 81.27% and is statistically significant based on the F-test. Model evaluation using in-sample error metrics yields a Mean Absolute Error (MAE) of 19,344.89, a Root Mean Squared Error (RMSE) of 46,738.41, and a Mean Absolute Percentage Error (MAPE) of 48.20%, indicating that the model effectively captures general production trends but has limited accuracy for precise quantitative forecasting. These findings suggest that harvested area plays a dominant role in determining rice output, while further improvements in predictive performance may be achieved by incorporating additional explanatory variables and exploring alternative modeling techniques.]]>
<![CDATA[Classification of weather events in Lahat regency using the K-Nearest Neighbor method ]]>
<![CDATA[Kresnawati, Endang Sri]]>;
<![CDATA[Resti, Yulia]]>;
<![CDATA[Eliyati, Ning]]>;
<![CDATA[Zayanti, Des Alwine]]>;
<![CDATA[Dewi, Novi Rustiana]]>;
<![CDATA[Yani, Irsyadi]]>
<![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.v5i2.14613
<![CDATA[Weather event classification in a region is very important for various purposes, such as in the fields of transportation, health, agriculture, and others. Lahat has varying land elevations ranging from 26-106 meters above sea level in the East Merapi sub-district to 341-3032 meters above sea level in the Tanjung Sakti Pumi sub-district. It greatly affects local temperature, rainfall, and atmospheric pressure, which in turn affects the distribution of weather patterns and disasters such as floods. KNN is a prediction method that uses the concept of distance for a number of k nearest observations in determining the similarity between observations. Several metrics can be used for this prediction purpose. This study aims to predict weather events in Lahat Regency using the KNN method with several different distance metrics and then compare them to obtain the performance of the KNN prediction method. The results show that the Euclidean distance metric used in the KNN method has a better performance measurement, followed by the Manhattan and Minkowski metrics. In the Euclidean metric, the accuracy, precision, recall, f1-score, AUC, and MC value are 92.69%, 88.21%, 85.81%, 86.99%, 88.99%, and 76.37%, respectively.]]>
<![CDATA[Optimizing stock allocation and profit in MSMEs: Multiple constraints bounded Knapsack model solved using Grey Wolf Optimizer algorithm ]]>
<![CDATA[Dalilah, Mufarrida]]>;
<![CDATA[Cipta, Hendra]]>
<![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.v5i2.14728
<![CDATA[Effective inventory management is a determining factor in the probability and sustainability of micro, small, and medium enterprises (MSMEs). Adjusting the ideal stock of each product type that has to be distributed while taking perishable items, storage capacity constraints, and client demand unpredictability into account is a difficulty. Stock allocation must maximize profit while adhering to intricate constraints and particular item number limitations in the multiple-constraints Knapsack problem. This research aims to apply the Grey Wolf Optimizer (GWO) algorithm to the multiple constraints bounded Knapsack problem for optimal stock allocation while increasing profitability for MSMEs by comparing the ideal value of the simplex technique. The population parameter (Npop) and the maximum iteration (Max Iter) were the two parameters used to test the GWO method. According to sensitivity analysis, the GWO algorithm optimization study was less successful in producing the best outcomes. This resulted from a discrepancy between the simplex method's IDR 9,508,000 profit optimization and GWO's IDR 9,440,000. Nonetheless, the GWO method was almost ideal, as indicated by the deviation percentage of 0.7152%. The study highlights the applicability of metaheuristic optimization for MSME management inventory, offering a near-optimal solution with minimal deviation from analytical results. Limitations include the single-case scope and parameter sensitivity of the GWO algorithm.]]>
<![CDATA[Mathematical Model of Social Media Addiction: An Optimal Control Approach ]]>
<![CDATA[Ratna Widayati]]>;
<![CDATA[Intrada Reviladi]]>;
<![CDATA[Nur Afandi]]>;
<![CDATA[Ramya Rachmawati]]>
<![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.v5i2.14795
<![CDATA[In this study, we developed a deterministic mathematical model to analyze social media addiction, incorporating an optimal control strategy. The basic model captures the dynamics through which individuals become exposed to and eventually addicted to social media platforms. To enhance the model, we introduced two time-dependent control variables: one representing awareness campaigns through advertising and education, and the other representing treatment interventions for individuals suffering from addiction. An optimal control framework was then formulated based on these interventions. By applying Pontryagin’s Minimum Principle, we derived the necessary conditions for optimality and constructed the corresponding optimality system. Numerical simulations of the optimal control problem were conducted using the forward-backward sweep method to assess the effectiveness of the proposed strategies. The results demonstrate that the integrated control strategy—combining public awareness efforts with treatment interventions— substantially reduces the number of individuals exposed to and addicted to social media. Compared to scenarios without intervention, the number of affected individuals was significantly lower. These findings underscore the importance of implementing combined strategies rather than isolated measures. Therefore, this integrated approach is strongly recommended for policymakers and stakeholders as a practical and effective means to mitigate the adverse effects of social media addiction on public health and societal well-being.. Keywords: Social Media Addiction, Mathematical Model, Pontryagin Minimum Principle, Optimal Control.]]>
<![CDATA[Data assimilation for predicting the dynamics of acute respiratory infections using the ensemble Kalman filter ]]>
<![CDATA[Yolanda Norasia]]>;
<![CDATA[Dinni Rahma Oktaviani]]>;
<![CDATA[Aini Fitriyah]]>;
<![CDATA[Devi Marita Putri]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.14695
<![CDATA[Acute respiratory infection (ARI) is one of the most pressing public health problems due to its high transmission rate and the potential to cause significant pressure on health services. This study applies the Ensemble Kalman Filter (EnKF) method to predict the spread of ARI with a three-compartment population model, namely Susceptible (S), Exposed (E), and Infected (I). This study shows that the EnKF method can predict the spread of ARI well. The number of ensembles used affects the level of accuracy. The EnKF provides accurate predictions of the dynamics of ARI spread, making it relevant as a scientific basis in the formulation of data-based mitigation strategies. It can provide a scientific basis for policymakers to formulate accurate and measurable preventive measures.]]>
<![CDATA[Spatial clustering analysis of DKI Jakarta criminal cases in 2024 using DBSCAN and comparison with K-Means ]]>
<![CDATA[SINTIA AFRIYANI]]>;
<![CDATA[WINARSI J. BIDUL]]>;
<![CDATA[IMAM AL MAKSUR]]>;
<![CDATA[ACHMAD FAUZAN]]>;
<![CDATA[ROZA AZIZAH PRIMATIKA]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.14910
<![CDATA[The high crime rate in DKI Jakarta requires spatial analysis to accurately identify vulnerable zones. Such information is essential for developing data-driven crime prevention strategies. Therefore, this study aims to map the spatial distribution of criminal cases in DKI Jakarta in 2024 and to evaluate two clustering methods Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-Means in order to determine the most effective approach for identifying crime-prone areas. Data came from the Jakarta Open Data portal, containing coordinates (latitude, longitude), crime types, and supporting details. Pre-processing involved removing duplicates, filtering 2024 records, and eliminating invalid coordinates. Spatial features were normalized using Standard Scaler. DBSCAN parameters (eps, min_samples) were tuned via grid search, while K-Means used the Elbow method to determine optimal clusters. Performance was evaluated using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. K-Means achieved a higher Silhouette Score (0.483), lower Davies-Bouldin Index (0.725), and higher Calinski-Harabasz Index (145.897), indicating more compact, well-separated clusters. DBSCAN formed more clusters (12) and identified noise points, showing its ability to capture spatial density variations and detect small-scale hotspots. In conclusion, K-Means is more suitable for macro-level mapping of security areas, supporting the allocation of police resources and administrative planning, while DBSCAN is more effective in identifying localized hotspots that require targeted surveillance and rapid response. These findings provide practical insights for policymakers and law enforcement agencies in developing data-driven strategies for urban crime prevention and security management.]]>
<![CDATA[Clustering of productivity in the food, plantation, and farm sectors in Mamasa Regency using K-Means clustering analysis ]]>
<![CDATA[Muhammad Zulfadhli]]>;
<![CDATA[Kesumaning Dyah Larasati]]>;
<![CDATA[Rizky Fitria Ramadhanni]]>;
<![CDATA[Raynaldi Anggiat Samuel Siahaan]]>;
<![CDATA[Mochamad Fatih Romadlon]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.15064
<![CDATA[The productivity of the food, plantation, and farm sectors is the main driver of the economy in Mamasa Regency. However, there are significant disparities between subdistricts, requiring targeted development strategies. This study aims to group 17 subdistricts based on the productivity characteristics of the three main sectors using the K-Means Clustering method. The secondary data analyzed includes productivity, production, planted area, and the number of farmers/ranchers for each sector. The research stages include descriptive analysis, identification of leading commodities, and classification of subdistricts based on the productivity of each sector. The analysis results divide the subdistricts into three main clusters with different characteristics. Cluster 1 (High Productivity) is dominated by leading subdistricts, such as Pana for cattle farming, Nosu for Arabica coffee, and Tabulahan for patchouli. Cluster 2 (Medium Productivity) includes subdistricts with balanced performance in several commodities, while Cluster 3 (Low Productivity) consists of subdistricts that still face challenges in land optimization, production, and cultivation efficiency. The implication of this study is cluster-based policy recommendations. Local governments are advised to implement specific strategies, such as developing cattle breeding centers in Pana, processing Arabica coffee in Nosu, and patchouli industry in Tabulahan. For low-productivity clusters, interventions are directed at improving infrastructure, access to inputs, and technological assistance. With this evidence-based strategy, local potential can be optimized, regional disparities reduced, and economic growth in Mamasa Regency can be more inclusive and sustainable.]]>
<![CDATA[Clustering Indonesian provinces based on welfare level using several validity indices ]]>
<![CDATA[Yudi Setyawan]]>;
<![CDATA[Maria Kristina Yolanda Hawa]]>;
<![CDATA[Kris Suryowati]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.15817
<![CDATA[One of the national development goals is to increase the level of community welfare. There are several aspects that influence the level of welfare, namely population, health, education, housing, social, employment, consumption, and poverty. This research aims to group provinces in Indonesia based on their level of welfare so that the government can determine appropriate policies in the context of economic recovery and improving the welfare of the Indonesian people. The data used are indicators of provincial welfare levels in Indonesia in 2022 from the Central Statistics Agency. Data is grouped into 3 clusters based on welfare level, namely high (C1), medium (C2), and low (C3) using the K-Means and Fuzzy C-Means methods. Based on the results of the validity test, it is known that ththe best method is the K-Means method with Euclidean distance using the parameter k = 3, the resulting DBI value is 0.989 and the C-Index is 0.076, where this value is better than those of the Fuzzy C-Means method. It is hoped that the results can provide information regarding the characteristics of provinces in Indonesia based on welfare level indicators and become a reference for the government in improving welfare in Indonesia.]]>
<![CDATA[Geographically weighted panel regression using Haversine distance for mapping sustainable development goals ]]>
<![CDATA[Mohamad Hidayat Hala]]>;
<![CDATA[Hasan S. Panigoro]]>;
<![CDATA[Amanda Adityaningrum]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.16002
<![CDATA[The Sustainable Development Goals (SDGs) vary widely across Asian countries, indicating that the factors driving SDGs achievement may vary by location. Global models may miss these local variations, so this study used Geographically Weighted Panel Regression (GWR Panel), a method that estimates separate regression coefficients for each geographical location. The GWR Panel in this study was used to capture spatially varying SDGs determinants across 46 Asian countries from 2015 to 2024. This study also compares four Adaptive Kernel functions (Gaussian, Exponential, Bisquare, Tricube) with Haversine distances, as kernel choice directly affects which neighboring countries influence each local coefficient estimate, where applying the incorrect kernel to spatially heterogeneous data can lead to biased local estimates. The best kernel was selected using Cross-Validation (CV). The Adaptive Exponential Kernel produced the lowest CV value (81.686), compared to Adaptive Gaussian (83.128), Bisquare (84.485), and Tricube (85.095), confirming it as the most accurate kernel for this data. The results identified 16 distinct country groups, demonstrating that SDGs determinants vary across Asia. Education, gender, economic growth, infrastructure, environment, institutions, and partnerships are universally important. Meanwhile, water, health, hunger, and climate show the greatest regional variation. SDGs policies should be adjusted to local contexts.]]>
<![CDATA[A mathematical model of malaria transmission dynamics with multi-stage infection and dual treatment pathways ]]>
<![CDATA[Panca Dewi Fitriyana]]>;
<![CDATA[Budi Priyo Prawoto]]>
<![CDATA[ Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/bamme.v6i1.16070
<![CDATA[Malaria remained a complex global public health challenge due to the interplay between biological transmission and human treatment-seeking behavior. This study developed a deterministic mathematical model incorporating two levels of infection severity (mild and severe) and dual treatment pathways, namely herbal and medical treatment. The model was formulated as a system of nonlinear ordinary differential equations and analyzed using the Next Generation Matrix method to derive the basic reproduction number , , while local stability was examined using the Routh–Hurwitz criterion. The results showed that the disease-free equilibrium was locally asymptotically stable when , indicating the eventual elimination of the disease. Sensitivity analysis revealed that mosquito mortality and transmission rates were the most influential parameters affecting disease spread. Numerical simulations further demonstrated that increasing early-stage treatment, particularly herbal treatment for mild infections, significantly reduced and limited progression to severe cases. These findings highlighted the critical role of early treatment-seeking behavior combined with effective vector control in reducing malaria transmission and supporting long-term elimination strategies.]]>
