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All Journal SPIRIT JOINCS (Journal of Informatics, Network, and Computer Science)
Hari Moerti
Institut Teknologi dan Bisnis Yadika Pasuruan
Computer Science & IT

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Comparison of Naive Bayes and KNN for Honey-Mumford Learning Style Classification in Interpersonal Skill: Komparasi Naive Bayes dan KNN untuk Klasifikasi Gaya Belajar Honey-Mumford pada Interpersonal Skill Hari Moerti; Hamzah Setiawan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

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Abstract

Developing soft skills competence, particularly interpersonal abilities, often presents a challenge for Informatics students accustomed to technical and structured thinking patterns. The mismatch between teaching methods and student learning preferences can hinder the absorption of non-technical material. This study aims to classify student learning style profiles in the Interpersonal Skill course using a Machine Learning approach based on the Honey-Mumford model (Activist, Reflector, Theorist, Pragmatist). The research methodology employs Educational Data Mining techniques by comparing the performance of Naive Bayes and K-Nearest Neighbor (KNN) algorithms in predicting learning styles based on academic history data and behavioral questionnaires. Experimental results indicate that the Naive Bayes algorithm outperforms KNN in recognizing student characteristic patterns, achieving an accuracy rate of 93.33%. These findings suggest that engineering students possess heterogeneous learning styles; therefore, adaptive and varied teaching strategies are essential to optimize the comprehension of soft skills materia.
SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMA PROGRAM INDONESIA PINTAR SISWA MADRASAH DENGAN METODE SMART Hari Moerti; Dedy Ardiansyah
SPIRIT Vol 18, No 1 (2026): SPIRIT
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v18i1.416

Abstract

The Indonesia Pintar Program (PIP) for Madrasah is a government program for underprivileged students enabling them to continue their education without economic barriers. However, the current selection process at Madrasah Aliyah is manual, making it prone to subjectivity and inaccurate targeting. This study aims to develop a Decision Support System (DSS), implement the SMART method for criteria assessment, and generate objective and accurate recipient recommendations. Research data were sourced from students at MA Al-Arif, Gempol, Pasuruan, East Java. The Simple Multi Attribute Rating Technique (SMART) was utilized for attribute weighting in multi-attribute decision-making. Test results comparing manual and system calculations showed an accuracy rate of 99.3%, which indicates a very high level of computational consistency in processing the selection criteria. The study concludes that the implementation of a SMART-based DSS provides more objective, accurate, and targeted recommendations for PIP recipients at Madrasah Aliyah.
Co-Authors Dedy Ardiansyah Hamzah Setiawan