Teknik Data Mining Dengan Menggunakan Algoritma Decision Tree Untuk Mengetahui Pola Pemahaman Mahasiswa Pada Matakuliah Pemrograman

Sri Novida Sari; Putri Annisa; An Nisa Dian Rahma; Rama Prameswara Ritonga; Dito Putro Utomo

doi:10.47065/bit.v6i4.2339

Sri Novida Sari * Politeknik Negeri Medan, Medan, Indonesia
Putri Annisa Politeknik Negeri Medan, Medan, Indonesia
An Nisa Dian Rahma Politeknik Negeri Medan, Medan, Indonesia
Rama Prameswara Ritonga Politeknik Negeri Medan, Medan, Indonesia
Dito Putro Utomo Politeknik Negeri Medan, Medan, Indonesia

DOI: https://doi.org/10.47065/bit.v6i4.2339

Keywords: Data Mining; Patterns; Student Understanding; Decision Tree Algorithm; Programming Course

Abstract

Medan State Polytechnic, as one of the leading vocational universities in Medan City, plays a crucial role in producing graduates who are ready to work and possess applied competencies according to industry needs. One of the strategic departments is the Computer Engineering and Informatics Department, which focuses on developing students' abilities in technology and programming. Programming courses are an important foundation in developing students' analytical and logical skills. However, many students still experience difficulties in understanding basic programming concepts, which results in low academic achievement and learning motivation. This study aims to identify patterns of student understanding in programming courses using the Decision Tree algorithm as a classification method. Through a data mining approach, this study attempts to extract hidden patterns from students' academic data to identify factors that influence their level of understanding. The Decision Tree algorithm was chosen because it is able to produce classification models that are easy to understand and interpret, and is effective in handling both categorical and numerical data. The research data was processed using Google Collaboratory with the help of the scikit-learn library. The testing process was carried out through the formation of a classification model, decision tree visualization, and confusion matrix analysis to measure model performance. Based on the test results, an accuracy value of 50% and an F1-score of 51.68% were obtained, indicating that the Decision Tree model has a good ability to predict and classify students' level of understanding of programming courses. Overall, this research provides an important contribution to the development of data-based learning strategies in vocational education environments. Through the results obtained, lecturers are expected to be able to adjust teaching methods according to student characteristics and abilities, so that the learning process becomes more adaptive, effective, and has a positive impact on improving student understanding of programming courses.

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