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All Journal RESEARCH AND DEVELOPMENT JOURNAL OF EDUCATION (RDJE)
Ardiansyah, M.
Universitas Indraprasta PGRI
Education Mathematics Other

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PENERAPAN KONSEP TOTAL QUALITY MANAGEMENT (TQM) DALAM MENINGKATKAN MUTU PENDIDIKAN SD ISLAM PERTI JAKARTA BARAT Ardiansyah, M.
Research and Development Journal of Education Vol 10, No 1 (2024)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/rdje.v10i1.21090

Abstract

Pada era revolusi 4.0 ini, munculnya Total Quality Management (TQM) dianggap sebagai solusi terhadap permasalahan yang dihadapi lembaga pendidikan dalam meningkatkan kualitas pendidikan. Artikel ini bertujuan untuk menjelaskan beberapa konsep TQM yang diterapkan oleh SD Islam Perti Tomang, yaitu (1) fokus pada kepuasan konsumen, (2) komunikasi dan kerjasama antar karyawan, dan (3) upaya dalam peningkatan Sumber Daya Manusia (SDM). Metode yang digunakan merupakan pendekatan kualitatif dalam studi kasus, dalam hal ini analisis data dilakukan melalui empat langkah: pengumpulan data, klasifikasi, penyajian, dan kesimpulan. Hasil artikel menunjukkan bahwa SD Islam Perti Tomang mengimplementasikan konsep TQM untuk meningkatkan kualitas pendidikan yang diselenggarakan, terutama dalam menggabungkan ilmu umum dengan nilai keislaman. Dalam merumuskan masalah, ditemukan bahwa ketiga konsep TQM yang dibahas saling terkait dan diterapkan secara terpadu oleh SD Islam Perti Tomang.
IMPLEMENTASI DEEP LEARNING UNTUK MENINGKATKAN HASIL PEMBELAJARAN DI SEKOLAH MENENGAH KEJURUAN (SMK) SE-JAKARTA BARAT Ardiansyah, M.; Nugraha, Mohamad Lutfi
Research and Development Journal of Education Vol 11, No 1 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/rdje.v11i1.26453

Abstract

OPTIMIZING DEEP LEARNING MODELS FOR LIMITED DATA ENVIRONMENTS: A COMPARATIVE STUDY Ardiansyah, M.
Research and Development Journal of Education Vol 11, No 1 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/rdje.v11i1.26373

Abstract

The use of deep learning models in education is expanding, particularly in supporting student data analysis, personalized learning, and AI-based evaluation tools. However, most of these models require large amounts of data to perform optimally, which often poses a challenge in educational environments with limited data. This study aims to explore and optimize deep learning models under limited data conditions through a comparative analysis of several approaches designed to improve model efficiency in such settings. It examines techniques like transfer learning, data augmentation, and semi-supervised learning, and evaluates model performance on educational data such as attendance records, exam scores, and student survey results from vocational high school students across West Jakarta. The findings reveal that transfer learning and data augmentation significantly enhance model accuracy without needing to directly increase data volume, while semi-supervised learning provides stable performance on highly limited datasets. These findings contribute to the development of more efficient deep learning models suited for educational environments with restricted data access, supporting educators and edtech developers in making informed decisions on the application of machine learning in educational institutions.
Co-Authors Nugraha, Mohamad Lutfi