<![CDATA[In the rapidly growing digital era, a major challenge faced by the education system is adapting the curriculum to the fast-paced technological advancements. This study aims to analyze the effectiveness of the curriculum through the application of data mining, focusing on the use of clustering algorithms to identify patterns in student data and curriculum. The data used in this study were collected from 200 students and 50 teachers using a digital-based curriculum, which included information on student satisfaction, exam performance, and feedback on teaching materials. Through the application of clustering algorithms, this study identified three main clusters, each of which showed different curriculum needs. The first cluster indicates that students with low satisfaction and low exam performance require a more adaptive and technology-based curriculum. The second cluster, which shows high satisfaction and high exam performance, requires the maintenance and development of the existing curriculum. Meanwhile, the third cluster, which had high satisfaction but low exam performance, showed the need for a more personalized teaching approach and more effective exam evaluation techniques. The results of this study provide valuable insights for designing a curriculum that is more responsive to educational needs in the digital era. By utilizing data mining, particularly clustering, education systems can better understand and tailor to the diverse needs of students, creating more effective and relevant learning experiences in line with technological advancements. This research also contributes significantly to the design of data-driven curricula that can improve the quality of education in the digital age.]]>
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