Klasterisasi Jawaban Uraian Mahasiswa Menggunakan TF-IDF dan K-Means untuk Membantu Koreksi Ujian

 (*)Irsyad Arif Mashudi Mail (Politeknik Negeri Malang Malang, Indonesia)
 Sofyan Noor Arief (Politeknik Negeri Malang Malang, Indonesia)
 Deasy Sandhya E.I. (Politeknik Negeri Malang Malang, Indonesia)
 Triana Fatmawati (Politeknik Negeri Malang Malang, Indonesia)
 Mamluatul Hani’ah (Politeknik Negeri Malang Malang, Indonesia)
 Irfan Thalib Alfarid (Politeknik Negeri Malang Malang, Indonesia)

(*) Corresponding Author

Submitted: August 21, 2023; Published: October 31, 2023

Abstract

One way to ensure students understand a topic is by giving them essay questions. Essay questions provide a more accurate evaluation compared to other types of questions. However, this raises new problems where lecturers often have not found an effective way to assess answers to essay questions. The large number of students makes the assessment process take a long time. However, in reality, there are many similarities in the answers between students. These similar answers can be grouped and given the same grade. Unfortunately, if done manually, this grouping takes a very long time. Clustering is one way that can be used to determine variations in student answers as a whole. TF-IDF and K-Means are the clustering algorithms that are considered the strongest and most popular. By using TF-IDF and K-Means to help lecturers group students' descriptive answers, it turns out to be quite effective because with a percentage of conformity to the grouping results of 65%, lecturers can group descriptive answers in a much faster time than manually grouping descriptive answers.

Keywords


Essay Correction System; Clustering; TF-IDF; K-Means; Decision Support System

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