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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Community Empowerment
Diba, Naila Farah
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Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Digital innovation : Implementation of interactive teaching materials for vocational school teachers to support Merdeka Belajar and SDGs 4 Azizah, Nuril Lutvi; Cornelius, Cornelius; Eviyanti, Ade; Liansari, Vevy; Wardani, Gita; Diba, Naila Farah
Community Empowerment Vol 10 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.12263

Abstract

The problem in vocational school (SMK) learning is the lack of interactive teaching media used by educators. However, under the Merdeka Belajar curriculum, vocational school students are expected to develop their achievements and create self-employment opportunities based on their acquired skills. This activity aims to enhance the skills of vocational school teachers in digitizing teaching materials through 2D animated videos, 3D animated videos with Artificial Intelligence (AI), and digital flip books. The method used in this community service includes socialization, training, mentoring, and evaluation. The results of the activity indicate an improvement in vocational school teachers' skills in creating 2D and 3D animated instructional videos. Additionally, some teachers have successfully developed interactive digital-based teaching materials.
ANALYSIS OF APRIORI AND K-NEAREST NEIGHBOR (KNN) ALGORITHM IN RECOMMENDING APPROPRIATE LEARNING METHOD Azizah, Nuril Lutvi; Eviyanti, Ade; Ariyanti, Novia; Wardani, Gita; Diba, Naila Farah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0557-0572

Abstract

The study investigates the utilization of data mining techniques, especially the Apriori algorithm and K-Nearest Neighbor (KNN) classification, in recommending appropriate learning methods based on student data. The purpose of this research is to analyze patterns and groupings in students’ behavior, preferences, and academic performance to support more informed and personalized educational strategies. The Apriori algorithm is used to identify frequent associations among learning related attributes, while KNN classification helps group students with similar learning characteristics. The analysis revealed that the digital learning method is the most preferred by students, with a percentage of 84.29%, followed by the traditional lecture method at 15.70%. These results reflect a notable trend toward technology-driven, flexible learning environments, although conventional approaches continue to hold relevance for a portion of learners. The research concludes that the integration of the Apriori algorithm and KNN clustering proves to be an effective analytical framework for facilitating adaptive learning. This approach allows educators and institutions to make data-driven decisions in tailoring instructional methods that align with the diverse needs and preferences of students.
Co-Authors Ade Eviyanti Cornelius, Cornelius Novia Ariyanti Nuril Lutvi Azizah Vevy Liansari Wardani, Gita