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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Justek : Jurnal Sains Dan Teknologi Joutica : Journal of Informatic Unisla
Saba Mehmood
University of Management and Technology
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Mathematics

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Effectiveness of AI-Based Smart Agriculture Innovation Communication Through the Agrimind Application in Increasing Young Generation’s Interest in Farming Mohammad Rifky; Eli Purwati; Deny Wahyu Tricana; Saba Mehmood; Wasim Raza
Justek : Jurnal Sains dan Teknologi Vol 9, No 1 (2026): March
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v9i1.37787

Abstract

The low interest among the younger generation in entering the agricultural sector, as well as the challenges of farmer succession in Indonesia, are the primary issues underlying this study. This study aims to analyze the effectiveness of communicating smart agriculture innovations based on artificial intelligence (AI) through the Agrimind application in increasing young people’s interest in the agricultural sector. This study employs a quantitative descriptive approach, with data collected via a questionnaire administered to 140 respondents from the younger generation in Ponorogo Regency. The collected data was subsequently processed using descriptive statistical analysis methods and tested for validity and reliability. The findings indicate that 70% of respondents support the implementation of modern technology and AI-based training for young farmers. Respondents believe that the use of the Agrimind application can help improve efficiency, attract the interest of young farmers, and strengthen collaboration between senior farmers and the younger generation who are tech-savvy. These findings demonstrate that AI-based agricultural innovation communication holds significant potential for transforming the younger generation’s perception of the agricultural sector, improving farmers’ work efficiency, and strengthening farmer succession in the digital era.
Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation Syaharuddin Syaharuddin; Hendi Hidayah; Vera Mandailina; Saba Mehmood; Wasim Raza
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2603

Abstract

Multivariable nonlinear equation systems often appear in engineering, physics, economics, and artificial intelligence modeling, but often do not have closed analytical solutions. Therefore, accurate, efficient, and stable numerical methods are needed. This study aims to comparatively evaluate three iterative methods, namely Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt, in solving identical high-complexity multivariable nonlinear systems. Simulations were performed using MATLAB with an error tolerance of 0.001 and a maximum iteration limit of 100. The test system consisted of a combination of trigonometric, exponential, and polynomial functions, resulting in nonlinear interactions that were challenging for each method. The simulation results show that Levenberg–Marquardt excelled with only 6 iterations and a final error of 3.246 × 10⁻¹⁰, indicating high stability and efficiency, followed by Multivariate Newton-Raphson with 13 iterations and an error of 4.606 × 10⁻⁹, while Newton-Kantorovich requires 27 iterations with an error of 5.770 × 10⁻⁷, reflecting slower semi-local corrections. Three-dimensional visualization shows the intersection point of the surface as a solution, providing an intuitive understanding of the iteration trajectory characteristics of each method. The novelty of this research lies in the integrated numerical simulation framework that allows direct quantitative comparison of the three methods on identical systems with the same initial conditions, tolerance, and iteration limits. These findings provide important empirical references for selecting efficient and stable iterative methods for multivariable nonlinear systems, as well as practical guidance for numerical applications in engineering, physics, and scientific computing.
A Comparative Study of Hybrid GARCH–HOLT–BPNN Models for Rainfall Forecasting Using a MATLAB-Based Intelligent Computing System Supardi Supardi; Syaharuddin Syaharuddin; Vera Mandailina; Saba Mehmood
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2636

Abstract

Rainfall forecasting is essential for water resource management, hydrometeorological disaster mitigation, and agricultural planning. This study addressed the limitations of previous research that focused on single models or hybrid approaches combining only two methods, which often failed to capture the simultaneous volatility, trend, and nonlinear characteristics of rainfall data. Monthly rainfall data from 2015 to 2024 were analyzed using three individual models: Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Holt’s Exponential Smoothing, and Backpropagation Neural Network (BPNN). Two hybrid models, GARCH–Holt and GARCH–Holt–BPNN, were also developed to integrate the advantages of statistical and artificial intelligence methods. Hyperparameter tuning was performed to optimize model performance, and forecasting accuracy was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results showed that GARCH effectively captured short-term volatility, Holt followed trends and seasonal patterns, and BPNN modeled nonlinear relationships despite sensitivity to data variations. The GARCH–Holt hybrid improved stability and accuracy compared to individual models, while the GARCH–Holt–BPNN hybrid achieved the highest predictive performance with a MAPE of 1.13%, indicating strong generalization capability. Forecasted rainfall for 2025 revealed seasonal patterns characterized by periods of heavy, moderate, and light rainfall. A MATLAB-based Graphical User Interface (GUI) was developed to facilitate interactive modeling and visualization. Overall, the proposed hybrid GARCH–Holt–BPNN model provides a more robust and reliable forecasting framework by effectively integrating volatility, trend, and nonlinear components, thereby enhancing predictive accuracy and supporting data-driven decision-making in hydrometeorological applications.
ETHNOMATHEMATICS: AN EXPLORATION OF GEOMETRIC SHAPES AND STRUCTURES IN THE “UMA LENGGE” TRADITIONAL HOUSE Ririn Fitriatunnisa; Intan Dwi Hastuti; Yuni Mariyati; Saba Mehmood
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol. 14 No. 4 (2025)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ajpm.v14i4.12409

Abstract

This research aims to explore the geometric shapes and structures found in the Uma Lengge traditional house with a qualitative approach based on the ethnographic model. This research was conducted in Maria Village, Wawo Sub-district, which was chosen because of the existence of the Uma Lengge traditional house and its role in cultural preservation and the development of the tourism sector. The problem raised in this research is the lack of studies linking the architectural design of Uma Lengge with mathematical concepts, especially within the framework of ethnomathematics, which has the potential to provide new insights into the relationship between culture and science. Respondents were selected using a purposive sampling technique, including custodians, cultural experts, and conservationists who have in-depth knowledge of the history and construction of Uma Lengge. Data collection was conducted through direct observation, literature study, and in-depth interviews. Data analysis techniques include data reduction, data presentation, and verification to ensure the validity and reliability of research findings. The results show that the architectural design of Uma Lengge combines geometric concepts in flat and spatial shapes, including patterns of triangles, squares, rectangles, triangular prisms, cubes, blocks, and pyramid frustums. These geometric structures not only reflect beauty and function, but also reflect local wisdom in the use of traditional mathematical concepts. This research makes an important contribution in expanding the understanding of ethnomathematics as part of cultural heritage that can be integrated into the mathematics learning process. It is recommended to conduct more comprehensive follow-up research on the application of other mathematical concepts, as well as develop ethnomathematics-based learning modules to increase students' appreciation of mathematics in a cultural context.
Optimization of Flood Detection Systems Using Hybrid BP-TRQN Neural Network Algorithms: An In-Depth Review Syaharuddin; Saba Mehmood; Wasim Raza; Sunday Emmanuel Fadugba
Joutica Vol 11 No 02 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i02.1648

Abstract

accurate, stable, and efficient real-time detection system. The purpose of this study is to conduct a Systematic Literature Review of the performance of the Hybrid Back Propagation–Time Recurrent Quantum Networks (BP-TRQN) algorithm in optimizing accuracy, learning stability, and data processing efficiency in flood detection systems. This study employed the PRISMA-based Systematic Literature Review (SLR) method to identify, screen, and analyze relevant studies. A total of 44 articles published between 2015 and 2025 were selected from the Scopus, DOAJ, and Google Scholar databases based on predefined inclusion and exclusion criteria. The results show that Hybrid BP-TRQN consistently provides significant improvements over single models, particularly in reducing Root Mean Squared Error (RMSE), increasing the coefficient of determination (R²), and accelerating convergence. The integration of temporal and spatial components and optimization has been proven to strengthen the model's ability to process large-scale hydrometeorological data and satellite images more adaptively. In addition, the application of hybrid algorithms also improves computational efficiency, making it potentially applicable in real-time early warning systems. Thus, BP-TRQN can be considered a promising approach in supporting flood disaster mitigation through a more reliable and responsive detection system.
Co-Authors Deny Wahyu Tricana Eli Purwati Hendi Hidayah Intan Dwi Hastuti Mariyati, Yuni Mohammad Rifky Ririn Fitriatunnisa Sunday Emmanuel Fadugba Supardi Supardi Syaharuddin Syaharuddin Syaharuddin Vera Mandailina, Vera Wasim Raza