Development of Smart Study Web Application for Classifying Student Material Understanding Levels Using Naive Bayes Classifier

Purnomo Hadi Susilo; Vita Ihwatin Mujtahidah; Nur Qomariyah Nawafilah; Azizul Azhar Ramli

doi:10.52436/1.jutif.2026.7.3.5507

Authors

Purnomo Hadi Susilo Informatics Engineering, Faculty of Science and Technology, Universitas Islam Lamongan, Indonesia
Vita Ihwatin Mujtahidah Informatics Engineering, Faculty of Science and Technology, Universitas Islam Lamongan, Indonesia
Nur Qomariyah Nawafilah Informatics Engineering, Faculty of Science and Technology, Universitas Islam Lamongan, Indonesia
Azizul Azhar Ramli Software Engineering Department, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.3.5507

Keywords:

Data Mining, E-Learning, Naive Bayes, Smart Study application, Student Comprehension Assessment

Abstract

The rapid development of information and communication technology requires adaptive digital learning systems that are able to evaluate students’ learning outcomes objectively. However, the Smart Study application previously functioned only as a quiz delivery platform and lacked analytical capabilities to assess students’ levels of material understanding, particularly in practical courses such as Computer Networks. This study aims to design and develop a web-based Smart Study application integrated with the Naive Bayes classification algorithm to determine students’ understanding levels based on quiz performance data. The research methodology includes data collection from Informatics Engineering students at Universitas Islam Lamongan, followed by data preprocessing through cleaning and categorical conversion of features, including final score, average response time, response time variability, and correct incorrect response time ratio. The dataset was divided into 80% training data and 20% testing data. The Naive Bayes model was trained and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results show that the proposed model achieved an accuracy of 75%, correctly classifying 15 out of 20 testing samples. The model demonstrated strong performance in identifying the Comprehended class with an F1-score of 0.83, while performance for the Not Comprehended class was lower with an F1-score of 0.55 due to class imbalance. This study contributes to the fields of learning analytics and educational data mining by demonstrating the integration of a simple machine learning method into an e-learning application to support early detection of learning difficulties and data-driven evaluation of digital learning processes in higher education.

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