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Menampilkan 1–2 dari 2 artikel
An Adaptive Computational Model for Detecting Concept Drift in Long Term Data Streams Using Incremental Learning Approaches
Rinna Rachmatika
; Kecitaan Harefa
Indonesian Journal of Infomatics
Vol 1
, No 1
(2026)
Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using increme...
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Socio Technical Perspectives on Implementing Artificial Intelligence for Formative Assessment in Culturally Diverse Educational Institutions
Rinna Rachmatika
; Kecitaan Harefa
International Journal of Educational Technology and Society
Vol 2
, No 4
(2025)
The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in...
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