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Novitasari Novitasari
Universitas Prima Indonesia, Indonesia
Computer Science & IT

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Comparison of Offline-Online-Blended Learning Models Using Agglomerative Hierarchical Clustering N. Priya Dharshinni; Darwis Darwis; Toni Toni; Ricki Ricki; Novitasari Novitasari; Putri Putri
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

The pandemic has resulted in changes learning models over the last 3 years. Offline learning models in schools have changed to online learning due to the covid-19 pandemic and after the end of the covid-19 pandemic, many schools have implemented the blended learning model. The problem faced by the school is that it is unable to determine which learning model is the most effective to develop better learning methods for students. The purpose of this study was to compare offline, online, and blended learning models to determine effective learning methods based on student subject grades using agglomerative hierarchical clustering algorithms. The results of the study found that blended learning was the most understood learning by students compared to offline and online learning models which were seen from cluster results based on the good grade predicate category show that in offline learning the average value of students in the good grade predicate is 290 students and decrease to 219 students while using online learning and an increased drastically to 362 students after implementing blended learning. While the subjects that can be understood by students using offline, online and blended learning models are the subjects of Religion, Mathematics, and Natural Science.
Co-Authors Darwis Darwis N. PRIYA DHARSHINNI Putri Putri Ricki Ricki Toni Toni