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Optimizing parameter selection in bidirectional encoder portrayal for transformers algorithm using particle swarm optimization for artificial intelligence generate essay detection Prasetyo, Tegar Arifin; Chandra, Rudy; Siagian, Wesly Mailander; Siregar, Horas Marolop Amsal; Siahaan, Samuel Jefri Saputra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5543-5554

Abstract

This research proposes a novel method for detecting artificial intelligence (AI)-generated essays by integrating the bidirectional encoder representations from transformers (BERT) model with particle swarm optimization (PSO). Unlike traditional approaches that rely on manual hyperparameter tuning, this study introduces a systematic optimization technique using PSO to improve BERT’s performance in identifying AI-generated content. The key problem addressed is the lack of effective, real-time detection systems that preserve academic integrity amidst rapid AI advancements. This optimization enhances the model’s detection accuracy and operational efficiency. The research dataset consisted of 46,246 essays, which, after data cleaning, were refined to 44,868. The model was then tested on 9,250 essays. Initial evaluations showed BERT's accuracy ranging from 83% to 94%. After being optimized with PSO, the model achieved an accuracy of 98%, an F1-score of 98.31%, precision of 97.75%, and recall of 98.87%. The model was deployed using a FastAPI-based web interface, enabling real-time detection and providing users with an efficient way to quickly verify text authenticity. This research contributes a scalable, automated solution for AI-generated text detection and offers promising implications for its application in various academic and digital content verification contexts.
Integration of Balanced Scorecard and Analytical Hierarchy Process as a Method of Performance Measurement and Target Strategy Selection Siagian, Wesly Mailander; Togatorop, Iqnatius Cahyo H.
Widya Cipta: Jurnal Sekretari dan Manajemen Vol. 9 No. 1 (2025): March
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/widyacipta.v9i1.12226

Abstract

This study evaluates the performance of PT. XYZ, a prominent ready-mix concrete company in Toba Regency, North Sumatra, Indonesia, from 2018 to 2022. Utilizing a descriptive and quantitative research approach, data were gathered through document analysis, questionnaires, and interviews. The findings reveal significant financial weaknesses, particularly in the total asset turnover, return on investment (ROI), and debt-to-asset ratios. However, the company demonstrates strong performance in customer satisfaction, internal business processes, and learning and growth dimensions. Employing the Balanced Scorecard (BSC) framework, this research assesses these four perspectives and highlights the critical importance of the learning and growth perspective for strategic development. Through the integration of the Analytical Hierarchy Process (AHP), the study identifies the enhancement of information systems as a key strategic goal. This approach not only provides a comprehensive evaluation of PT. XYZ performance over the five-year period but also offers targeted recommendations for future business development and sustainable growth. PT XYZ overall performance is rated as good based on the Balanced Scorecard, with most indicators showing positive results, and the development of an information system is identified as a top priority to support the company's learning and growth.