A Fuzzy C-Means–Based Clustering Model for Analyzing TOEFL Prediction Scores in Higher Education

In the era of digital transformation, the application of data mining in academic data management has become an important requirement for improving the quality of education. One crucial aspect is English proficiency. One of the tools for measuring English proficiency is the Test of English as a Foreign Language (TOEFL) Prediction test, which is administered at every university, including the State Polytechnic of Lhokseumawe. The management of TOEFL Prediction scores can utilize data mining as a basis for more in-depth learning analysis, as well as evaluation material. This study aims to design and develop a model for grouping the TOEFL scores of students at State Polytechnic of Lhokseumawe by applying the Fuzzy C-Means (FCM) algorithm. The research methods included observation and interviews, data collection and pre-processing, cluster model design, web-based system development, and system testing. Evaluation was conducted through Black Box and White Box testing for the system, as well as cluster quality validation using the Xie-Beni Index (XB) and Partition Coefficient. The results showed that the pre-test dataset of first-year students (651 data) produced three clusters with an XB value of 0.623, while the dataset of final-year students (826 data) produced six clusters with an XB value of 0.181. The developed model proved to be able to map students' English language abilities in a more structured manner and could be used as a basis for academic planning and skill improvement.