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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Triyanna Widyaningtyas
Universitas Negeri Malang, Malang, Indonesia
Computer Science & IT

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Comparison of Text Representation for Clustering Student Concept Maps Reni Fatrisna Salsabila; Didik Dwi Prasetya; Triyanna Widyaningtyas; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4598

Abstract

This research aims to address the critical challenge of selecting a text representation method that effectively captures students’ conceptual understanding for clustering purposes. Traditional methods, such as Term Frequency-Inverse Document Frequency (TF-IDF), often fail to capture semantic relationships, limiting their effectiveness in clustering complex datasets. This study compares TF-IDF with the advanced Bidirectional Encoder Representations from Transformers (BERT) to determine their suitability in clustering student concept maps for two learning topics: Databases and Cyber Security. The method used applies two clustering algorithms: K-Means and its improved variant, K-Means++, which enhances centroid initialization for better stability and clustering quality. The datasets consist of concept maps from 27 students for each topic, including 1,206 concepts and 616 propositions for Databases, as well as 2,564 concepts and 1,282 propositions for Cyber Security. Evaluation is conducted using two metrics Davies-Bouldin Index (DBI) and Silhouette Score, to assess the compactness and separability of the clusters. The result of this study is that BERT consistently outperforms TF-IDF, producing lower DBI values and higher Silhouette Scores across all clusters (k= 2 - k=10). Combining BERT with K-Means++ yields the most compact and well-separated clusters, while TF-IDF results in overlapping and less-defined clusters. The research concludes that BERT is a superior text representation method for clustering, offering significant advantages in capturing semantic context and enabling educators to identify student misconceptions and improve learning strategies.
Performance Evaluation of Artificial Intelligence Models for Classification in Concept Map Quality Assessment Wahyu Styo Pratama; Didik Dwi Prasetya; Triyanna Widyaningtyas; Muhammad Zaki Wiryawan; Lalu Ganda Rady Putra; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4729

Abstract

Open-ended concept maps generated by students give better flexibility and present a complex analysis process for teachers. We investigate the application of classification algorithms in assessing openended concept maps, with the purpose of providing assistance for teachers in evaluating student comprehension. The method used in this study is experimental methods, which consists of data collection, preprocessing, representation generation, and modelling with Feedforward Neural Network, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Our dataset, derived from concept maps, consists of 3,759 words forming 690 propositions, scored carefully by experts to ensure high accuracy in the evaluation process. Results of this study indicate that K-NN outperformed all other models, achieving the highest accuracy and Receiver Operating Characteristic-Area Under the Curve scores, demonstrating its robustness in distinguishing between classes. Support Vector Machine excelled in precision, effectively minimizing false positives, while Random Forest showcased a balanced performance through its ensemble learning approach. Decision Tree and Linear Regression showed limitations in handling complex data patterns. FeedforwardNeural Network can model intricate relationships, but needs further optimization. This research concluded that Artificial Intelligence classification enables a better assessment for teachers, enables the path for personalized learning strategies in learning.
Co-Authors Didik Dwi Prasetya Lalu Ganda Rady Putra Muhammad Zaki Wiryawan Reni Fatrisna Salsabila Tsukasa Hirashima Wahyu Styo Pratama