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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Professional Teacher Education
Sugiyarto Surono
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences Other

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DynamicWeighted Particle Swarm Optimization - Support Vector Machine Optimization in Recursive Feature Elimination Feature Selection: Optimization in Recursive Feature Elimination Irma Binti Sya'idah; Sugiyarto Surono; Goh Khang Wen
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3963

Abstract

Feature Selection is a crucial step in data preprocessing to enhance machine learning efficiency, reduce computational complexity, and improve classification accuracy. The main challenge in feature selection for classification is identifying the most relevant and informative subset to enhance prediction accuracy. Previous studies often resulted in suboptimal subsets, leading to poor model performance and low accuracy. This research aims to enhance classification accuracy by utilizing Recursive Feature Elimination (RFE) combined with Dynamic Weighted Particle Swarm Optimization (DWPSO) and Support Vector Machine (SVM) algorithms. The research method involves the utilization of 12 datasets from the University of California, Irvine (UCI) repository, where features are selected via RFE and applied to the DWPSO-SVM algorithm. RFE iteratively removes the weakest features, constructing a model with the most relevant features to enhance accuracy. The research findings indicate that DWPSO-SVM with RFE significantly improves classification accuracy. For example, accuracy on the Breast Cancer dataset increased from 58% to 76%, and on the Heart dataset from 80% to 97%. The highest accuracy achieved was 100% on the Iris dataset. The conclusion of these findings that RFE in DWPSO-SVM offers consistent and balanced results in True Positive Rate (TPR) and True Negative Rate (TNR), providing reliable and accurate predictions for various applications.
Validity and reliability of a future-oriented critical thinking skills diagnostic test in elementary science education Efi Kurniasari; Sugiyarto Surono; Ishafit; Selviana Veronika Moruk
Journal of Professional Teacher Education Vol. 4 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jprotect.v4i1.2155

Abstract

Future-Oriented Critical Thinking Skills (FOCTS) extend conventional critical thinking by incorporating a future-oriented perspective that enables students to generate innovative solutions to emerging challenges. However, no validated diagnostic instrument currently exists to measure this construct among elementary school students. This study aimed to develop and evaluate the validity and reliability of a FOCTS diagnostic test for integrated science and social studies. The novelty of this study lies in operationalizing the future-oriented dimension of critical thinking into a measurable diagnostic instrument supported by both logical and face validity evidence.  The subjects used involved 30 students from one elementary school. Content validity was assessed through expert judgment covering logical validity (construct alignment) and face validity (clarity, readability, and presentation), using Aiken’s V coefficient. Empirical validity was examined using Pearson’s product-moment correlation, while reliability was evaluated using Cronbach’s Alpha. Due to scheduling constraints, empirical testing involved 30 sixth-grade students from one elementary school. The results showed an overall Aiken’s V value of 0.74, indicating adequate content validity. Empirical testing revealed that 25 of the 30 items were valid r = 0.367–0.713; r-critical = 0.361, α = 0.05, while five items were invalid. The 25 valid items demonstrated high reliability Cronbach’s Alpha = 0.871. These findings indicate that the FOCTS instrument has promising psychometric properties, although further validation with larger and more representative samples is recommended before wider implementation. The findings imply that the FOCTS instrument can serve as a promising diagnostic tool for identifying elementary students’ future-oriented critical thinking strengths and needs, pending further validation with larger samples.
Co-Authors Efi Kurniasari Goh Khang Wen Irma Binti Sya'idah Irma Binti Sya'idah Ishafit Selviana Veronika Moruk