Artikel Ilmiah

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Application for Measuring Learning Outcome at Politeknik Negeri Batam
(Politeknik Negeri Batam, 2025-01-02) Azlan Rafar Azlan Rafar; Thohari, Ahmad Hamim
The assessment of Learning Outcomes (LO) is a critical component of educational processes, aimed at evaluating the extent to which students meet predefined educational objectives. An outcome-based approach to LO assessment emphasizes quantifiable results over the process itself, underscoring its role in enhancing instructional quality and helping students recognize their accomplishments. This study focuses on the development of a web-based application designed to assess Learning Outcomes (LO) at Politeknik Negeri Batam. The outcome-based assessment approach implemented by the application aims to enhance objectivity and accuracy, providing clearer insights into students’ progress relative to predefined learning objectives. By emphasizing measurable results, the application supports the institution’s commitment to fostering an educational environment grounded in continuous improvement. Using the Rapid Application Development (RAD) methodology, the application was created through iterative cycles that allowed for frequent user feedback and quick adjustments. This approach ensured a user-centered design while streamlining development phases, thereby meeting functional requirements efficiently. Key phases of RAD, including Requirements Planning, User Design, and Construction, were instrumental in aligning the application’s features with the actual needs of instructors and students. By providing faculty with the tools to evaluate and track students' academic performance effectively, this application contributes significantly to enhancing the quality of education. Its integration is anticipated to become a valuable component of the institution's educational assessment framework. The implementation of this outcome-based application marks a step forward in modernizing assessment methods at Politeknik Negeri Batam. By digitizing and centralizing the evaluation of Learning Outcomes, the system reduces manual assessment time, allowing faculty to focus more on instructional strategies and student support. Future enhancements to the application could include expanding its analytics capabilities and integrating it with other institutional systems to foster a more comprehensive educational assessment framework.
Hybrid Simulated Annealing and Random Forest for Traffic Density Prediction in VANETs
(IEEE, 2025-02-06) Fajri, Wahidil; Wijanarko, Heru
The study addresses the issue of predicting traffic density in Vehicular Ad-hoc Networks (VANETs), where dynamic and unexpected traffic patterns limit accurate forecasting. Recent models frequently encounter challenges with accuracy caused by overfitting or complications in handling real-time data. The study introduces a hybrid model that combines Random Forest with Simulated Annealing, optimising the model’s parameters to mitigate overfitting and improve reliability. The research follows several steps: first, data from a VANETs dataset was collected and preprocessed, and then several standard machine learning models, like Linear Regression, Decision Trees, Random Forest, Support Vector Regression, and K-Nearest Neighbors, were tested. The Random Forest model showed the best performance metrics and was optimized using Simulated Annealing. The hybrid Simulated Annealing-Random Forest model significantly improved accuracy, outperforming traditional models.