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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Wicaksono, Reo
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Computer Science & IT

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Machine Learning for Open-ended Concept Map Proposition Assessment: Impact of Length on Accuracy Wicaksono, Reo; Dwi Prasetya, Didik; Elbaith Zaeni, Ilham Ari; Ariyanta, Nadindra Dwi; Hirashima, Tsukasa
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5044

Abstract

Open-ended concept maps allow learners to freely connect concepts, enriching understanding by linking new and prior knowledge. However, manually assessing proposition quality is time-consuming and subjective. This study proposes an automatic classification model for proposition quality assessment using term frequency–inverse document frequency (TF-IDF), a text representation method based on word frequency, and several machine learning algorithms. Two datasets were used are Relational Database with an average 5 words per proposition and Cybersecurity Authentication with an average 10 words per proposition. Comparative experiments with Support Vector Machine (SVM), a supervised classification algorithm, K-Nearest Neighbor, Random Forest, and Long Short-Term Memory (LSTM), a recurrent neural network for sequence data, revealed that SVM with RBF kernel achieved the highest performance on shorter propositions 87% accuracy, Cohen’s Kappa 0.76, while LSTM showed greater strength in handling longer propositions 85% accuracy, Cohen’s Kappa 0.69. These findings suggest that proposition length influences model effectiveness. The proposed approach can reduce the burden of manual assessment, increase the objectivity of evaluation, and support more efficient implementation of concept maps in education.
Assessing the Semantic Alignment in Multilingual Student-Teacher Concept Maps Using mBERT Ariyanta, Nadindra Dwi; Prasetya, Didik Dwi; Elbaith Zaeni, Ilham Ari; Hirashima, Tsukasa; Wicaksono, Reo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5046

Abstract

This study examines the effectiveness of mBERT (Multilingual Bidirectional Encoder Representations from Transformers) in assessing semantic alignment between student and teacher concept maps in multilingual educational contexts, comparing its performance with TF-IDF. Using datasets in both Indonesian and English, the study demonstrates that mBERT outperforms TF-IDF in capturing complexsemantic relationships, achieving 96% accuracy, 96% precision, 100% recall, and a 98% F1 score in the Indonesian dataset. In contrast, TF-IDF achieved higher precision (73%) and accuracy (79%) in the English dataset, where mBERT recorded 54% accuracy, 47% precision, but 90% recall. Semantic alignment was measured using cosine similarity to calculate the cosine of the angle between vectorsrepresenting textual embeddings generated by both models. This method facilitates cross-linguistic semantic comparison, overcoming challenges related to word frequency and syntactic variations. While mBERT’s computational demands and the study’s limited linguistic scope suggest room for improvement, the findings highlight the potential for hybrid models and emphasize the transformative impact of AI-driven tools, such as mBERT, in fostering inclusive and effective multilingual education.
Co-Authors Ariyanta, Nadindra Dwi Didik Dwi Prasetya Hirashima, Tsukasa Zaeni, Ilham Ari Elbaith