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All Journal Dinamik Jurnal Pendidikan Vokasi Raflesia
Muhammad Rizki
Universitas Pendidikan Indonesia
Computer Science & IT Education

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Studi Komparatif Prestasi Belajar dan Manajemen Waktu Mahasiswa Pendidikan Teknik Otomotif Antara Aktivis Himpunan Jurusan dan Non-Aktivis Muhammad Rizki; Farraz Bahana Rahman; Wahid Munawar
Jurnal Pendidikan Vokasi Raflesia Vol 6 No 1 (2026): Jurnal Pendidikan Vokasi Raflesia
Publisher : LPPM Politeknik Raflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53494/jpvr.v6i1.1345

Abstract

Studi ini dimaksudkan untuk mengomparasikan capaian akademik (IPK) serta efektivitas pengelolaan waktu antara mahasiswa yang terlibat aktif dalam organisasi himpunan dengan mahasiswa non-aktivis di Program Studi Pendidikan Teknik Otomotif Universitas Pendidikan Indonesia. Menggunakan pendekatan kuantitatif komparatif, penelitian ini melibatkan 60 Responden yang dibagi secara proporsional . Instrumen penelitian berupa dokumen hasil belajar dan angket manajemen waktu. Uji prasyarat menunjukkan data hasil belajar (IPK) terdistribusi normal dan homogen, sementara data manajemen waktu tidak normal sehingga dianalisis menggunakan uji non-parametrik Mann-Whitney U Test. Hasil penelitian menunjukkan; (1) Tidak terdapat perbedaan signifikan pada prestasi hasil belajar (IPK) antara mahasiswa aktivis dan non-aktivis; (2) Terdapat perbedaan yang sangat signifikan pada kemampuan manajemen waktu (p < 0,001), dimana mahasiswa aktivis memiliki skor manajemen waktu yang lebih tinggi. Hal ini menunjukkan bahwa keterlibatan organisasi tidak menghambat pencapaian akademik dan justru menjadi ruang efektif dalam melatih keterampilan manajerial diri bagi calon tenaga pendidik dan professional di bidang Teknik Otomotif.
Pengembangan Prototipe Aplikasi Pembelajaran English Proficiency Test Berbasis Generative AI dengan Pendekatan Adaptif Berbasis Kerangka CEFR Ani Anisyah; Muhammad Rizki; Ihsan Ghozi Zulfikar; Muhammad Alam Basalamah; Ade Mulyana
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10614

Abstract

TOEFL and IELTS are among the most widely used assessments for evaluating English language proficiency. However, test-takers often face difficulties in the Structure and Written Expression section of TOEFL and the Writing section of IELTS, which frequently hinder them from reaching their target scores. Traditional learning methods, which lack personalization, further limit the provision of materials tailored to individual needs. To address these challenges, this study proposes the integration of intelligent and adaptive technologies into language learning. Machine learning provides a promising approach for classifying English proficiency levels in accordance with the Common European Framework of Reference for Languages (CEFR). Additionally, Generative Artificial Intelligence (AI) powered by Large Language Models (LLMs) enables the generation of personalized learning content suited to learners’ specific requirements. This study introduces the development of an adaptive learning application for TOEFL and IELTS preparation, built on Generative AI and guided by the CEFR framework. The application was developed using a prototyping approach with iterative refinement to ensure relevance to user needs. Logistic Regression was identified as the most effective model for CEFR-level prediction. Furthermore, usability testing with the System Usability Scale (SUS) yielded a score of 77.5, categorized as “good,” demonstrating the feasibility of the proposed solution.. Keywords—English Proficiency Test, CEFR, Generative AI, Natural Language Processing, Large Language Model (LLM
Co-Authors Ade Mulyana Ani Anisyah Farraz Bahana Rahman Ihsan Ghozi Zulfikar Muhammad Alam Basalamah Wahid Munawar