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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Angriani Nur
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Computer Science & IT

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The Effect of K-NN Algorithm Practice Chatbot Tutors on the Speed and Accuracy of Solving AI Problems in Vocational Schools Andi Asfar; Andi Winda Purnamasari; Angriani Nur; Aprilia; Arnis; Ashabul Kahfi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/wrd2bn63

Abstract

Learning Artificial Intelligence (AI) concepts at the vocational high school level often presents challenges, particularly in understanding procedural algorithms such as K-Nearest Neighbor (K-NN). Students frequently experience difficulties in calculating distances, selecting the optimal value of k, and determining classification results accurately and efficiently. This study investigates the effect of a chatbot tutor designed for K-NN practice on students’ speed and accuracy in solving AI problems. A quasi-experimental design with a pretest–posttest control group model was employed. Sixty students from a vocational high school majoring in Software Engineering were divided into an experimental group receiving chatbot-assisted practice and a control group receiving conventional instruction. Data were collected through validated K-NN problem-solving tests and task completion time measurements. Statistical analysis using paired and independent sample t-tests revealed that the experimental group demonstrated significantly faster completion times and higher accuracy scores compared to the control group (p < 0.05). The effect size indicated a moderate to high practical impact. The findings suggest that chatbot-based tutoring can enhance both efficiency and precision in learning K-NN algorithms. This study contributes empirical evidence supporting the integration of chatbot tutors into Artificial Intelligence instruction in vocational education to improve problem-solving performance.
Co-Authors Andi Asfar Andi Winda Purnamasari Aprilia Arnis Ashabul Kahfi