APPLICATION OF DATA MINING TECHNIQUES TO ANALYZE ATTENDANCE AND IMPROVE THE QUALITY OF CHINESE LEARNING - Grace Limiko; Pupista, Orinda; Surahmat, Asep; Umbu Zogara, Lukas (Universitas Utpadaka Swastika, 2025)
In the era of globalization, learning Chinese is increasingly important, but challenges such as low student attendance and learning quality are still significant problems. This article discusses the application of data mining techniques as a solution to analyze student attendance and improve the quality of Chinese learning. By collecting and analyzing attendance data from 200 students for one semester, through classification and visualization methods, this article identifies patterns that affect student attendance. The analysis results show that 65% of students who followed the interactive teaching method attended more than 80% of the total meetings, compared to only 40% of students who followed the traditional teaching method. In addition, it was found that 75% of students who received additional material for difficult topics experienced a 20% increase in average test scores compared to pre-intervention scores. Recommendations for improvement were made based on these findings, including adaptation of teaching methods and provision of supplementary materials. Through a case study of an educational institution that has successfully implemented this technique, this article shows that data mining can not only improve student attendance, but also significantly improve the quality of learning. This research is expected to encourage educational institutions to adopt data mining technology in an effort to improve students' learning experience.