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All Journal SIGMA: Jurnal Pendidikan Matematika Journal of Computer Science Advancements
Muchamad Sobri Sungkar
Universitas Harkat Negeri
Computer Science & IT Mathematics Other

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METODE PEMBELAJARAN BRAIN-BASED LEARNING DENGAN STRATEGI KONSTRUKTIVISME MENINGKATKAN PENALARAN MATEMATIS SISWA Muh Sahidun; Muchamad Sobri Sungkar
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 18 No. 1: Juni 2026
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/38g4w022

Abstract

Penelitian ini dilatarbelakangi rendahnya penalaran matematis siswa akibat metode konvensional yang kurang aktif serta belum mengoptimalkan potensi kerja otak dan pembentukan pengetahuan secara mandiri. Tujuan: Tujuan penelitian ini adalah menganalisis peningkatan kemampuan penalaran matematis siswa melalui penerapan metode brain-based learning berbasis konstruktivisme. Metode: Menggunakan desain eksperimen semu (quasi-experiment), melibatkan 58 siswa. Instrumen penelitian menggunakan tes penalaran matematis mencakup indikator pengenalan pola dan manipulasi matematika. Teknik analisis data menggunakan uji Independent Sample T-test, uji Paired Sample T-test, dan uji N-gain guna mengukur tingkat kemampuan penalaran matematis siswa.  Hasil: Hasil penelitian menunjukkan kelas eksperimen mengalami peningkatan rata-rata signifikan 15,65 (Sig. 0,000), melampaui kelas kontrol (6,65). Uji N-gain membuktikan efektivitas brain-based learning mencapai 44,11%, jauh mengungguli kelas kontrol yang hanya 18,17%. Simpulan:  Penelitian ini membuktikan bahwa sinergi lingkungan berbasis kerja otak dan konstruksi pengetahuan mandiri signifikan meningkatkan penalaran matematis. Implikasinya, Brain-Based Learning menjadi solusi praktis bagi pendidik untuk mentransformasi pembelajaran konvensional menjadi lebih dinamis, sekaligus melatih siswa memecahkan masalah secara sistematis, aktif, dan mandiri.
NATURAL LANGUAGE PROCESSING FOR AUTOMATED REQUIREMENT ENGINEERING IN AGILE SOFTWARE DEVELOPMENT Muchamad Sobri Sungkar; Serikbek Baibek; Salma Hamdan
Journal of Computer Science Advancements Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.2646

Abstract

Manual Requirement Engineering (RE) in Agile software development creates a significant bottleneck. The reliance on natural language user stories at scale results in high-volume backlogs prone to ambiguity, duplication, and incompleteness, leading to costly, downstream development defects. This research aims to design, develop, and empirically validate a novel, hybrid Natural Language Processing (NLP) framework, termed the Agile Requirement Quality (ARQ) framework, to automate the detection of these common requirement defects. The goal is to reduce cognitive load and improve defect detection velocity during backlog refinement. A mixed-methods Design Science Research (DSR) methodology was employed. We developed the ARQ artifact (a hybrid BERT and heuristic model) and validated it both in-vitro against a 5,000-story “gold standard” annotated corpus (Fleiss’ Kappa 0.86) and in-situ through a quasi-experiment with professional Agile teams. The findings demonstrate high efficacy. In-vitro validation achieved high accuracy (overall 95.2%, with F1-scores of 0.87 for ambiguity and 0.94 for duplication). The in-situ experiment was conclusive: the ARQ-assisted team achieved a 73% increase in defect detection and an 87.5% reduction in “defect leakage” compared to the control team, registering high usability (88.5 SUS). This study provides robust empirical evidence that NLP-driven automation is a viable, high-impact strategy for mitigating risk in Agile RE. The framework functions as a practical “augmented intelligence” tool, significantly reducing defect leakage and improving quality assurance velocity.
Co-Authors Muh Sahidun Salma Hamdan Serikbek Baibek