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All Journal ILKOM Jurnal Ilmiah Journal of Applied Data Sciences
Che Pee, Ahmad Naim
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Abstract

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.
An Enhanced Mean Value Theorem with Bisection Technique to Elevate User Focus Metrics in Talent Finder Applications Arifin, M Zainal; Wibawa, Aji Prasetya; Safii, Moh; Noertjahyana, Agustinus; Che Pee, Ahmad Naim
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2822.140-149

Abstract

Contemporary digital workplaces face pervasive distractions (e.g., notifications, multitasking), yet talent-assessment systems rarely quantify their impact on attention. To address this gap, we integrate the classical Mean Value Theorem (MVT) with an adaptive bisection algorithm to model user-focus dynamics in talent-matching applications. MVT’s limit-based formulation captures continuous attentional shifts, while the iterative bisection method focus metrics by capturing dynamic attentional shifts through the mean toward optimal focus equilibrium, ensuring temporal continuity and rapid convergence. A controlled experiment involving Universitas Negeri Malang undergraduate students tested the Enhanced Mean Value Theorem–Bisection (EMVT-B) method in four simulated workplace scenarios. Participants selected Focus-oriented options over alternative strengths (Communication, Input, Relator, Adaptability) in approximately 65% of decisions, highlighting moderate yet improvable attentional commitment. Sensitivity analysis indicated that increasing the mean-shift threshold by 0.05 could raise Focus-oriented selections to 72%, emphasizing the method's practical impact. These findings establish EMVT-B as both a diagnostic and prescriptive tool, quantifying attentional stability while providing personalized strategies to enhance user focus. Future research should examine longitudinal applications and broader talent portfolios.
Co-Authors Agustinus Noertjahyana Aji Prasetya Wibawa Bayu Surarso Imam Tahyudin Irfan Santiko M Zainal Arifin, M Zainal Moh. Safii Tri Retnaningsih Soeprobowati Zainal Arifin Hasibuan