Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Lentera Negeri
Ta Ali
Universitas Negeri padang
Humanities Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERSONALISASI PEMBELAJARAN BERBASIS AI UNTUK TRANSFORMASI MENUJU PENDIDIKAN ABAD 21 YANG BERPUSAT KEPADA SISWA Marina Elfera; Rahmawari Azril; Ta Ali; Titi Sriwahyuni
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 4 (2025): Volume 10. No4, Desember 2025.
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i4.36119

Abstract

The primary objective of this research is to analyze the transformative role of Artificial Intelligence (AI)-based personalized learning in shifting the traditional education model towards student-centered 21st-century learning. The core problem addressed in this study is the failure of traditional education models to accommodate the diverse learning styles, speeds, and needs of students. This failure hinders the crucial development of 21st-century skills such as critical thinking and students' personalized potential.The methodology employed is a systematic literature review, focusing on the principles and applications of AI in education, specifically on the implementation of adaptive learning systems, predictive diagnostics, and AI-based intelligent content curation.Key findings indicate that AI facilitates deep personalization, which was previously unattainable by human instructors alone. AI systems facilitate profound personalization through three main mechanisms: real-time diagnostics and targeted interventions, automatic curriculum adjustment to ensure competency mastery, and the fostering of student autonomy. The integration of AI effectively optimizes the role of teachers to focus on mentorship, while also fostering students' independence and ownership over their learning process.Thus, AI-based personalized learning is a crucial structural element for achieving modern educational goals, ensuring that every student can maximize their competencies. This study concludes that AI-powered personalized learning is not merely a technological enhancement but an essential structural component required to actualize the goals of modern, student-centered 21st-century education.
Dominasi India-Tiongkok dan emerging themes dalam riset emotion recognition: studi bibliometrik scopus Andri Dermawan; Ta Ali; Dedy Irfan; Hansi Effendi; Khayla Tsabita
Lentera Negeri Vol. 6 No. 2 (2025): Lentera Negeri
Publisher : Indonesian Institute For Counseling, Education and Therapy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/994110

Abstract

Pengenalan emosi berbasis kecerdasan buatan (artificial intelligence-based emotion recognition) berkembang pesat seiring meluasnya penerapan pembelajaran mendalam pada domain suara, wajah, teks, dan sinyal multimodal. Meskipun jumlah publikasi meningkat tajam, peta struktur intelektual bidang ini siapa yang mendominasi, negara mana yang menjadi pusat kolaborasi, tema mana yang matang dan mana yang baru tumbuh belum banyak disajikan secara sistematis. Penelitian ini bertujuan memetakan perkembangan riset emotion recognition berbasis AI secara global melalui pendekatan bibliometrik. Data diperoleh dari basis data Scopus menggunakan kombinasi boolean yang menautkan istilah pengenalan emosi, metode kecerdasan buatan, dan modalitas emosi (wajah, suara, teks, multimodal), kemudian disaring berdasarkan tahun terbit (2021–2025), jenis dokumen (artikel), bahasa (Inggris), jenis sumber (jurnal), dan tahap publikasi (final), sehingga diperoleh 1.168 artikel dari 415 jurnal. Analisis dilakukan menggunakan perangkat lunak Biblioshiny (Bibliometrix) dan VOSviewer, mencakup analisis tren produksi ilmiah, analisis sitasi, hukum Bradford, jejaring co-occurrence kata kunci, peta tematik, peta kolaborasi negara, serta model daur hidup dan pertumbuhan kumulatif publikasi. Hasil menunjukkan produksi ilmiah tahunan meningkat konsisten dari 102 artikel (2021) menjadi 368 artikel (2025), dengan proyeksi puncak siklus hidup topik pada pertengahan tahun 2026 (R² = 0,987). India (339 dokumen) dan Tiongkok (267 dokumen) menjadi kontributor paling produktif, sementara IEEE Access (86 dokumen), Multimedia Tools and Applications (55), dan Applied Sciences (38) menjadi jurnal inti sesuai hukum Bradford (13 jurnal inti, R² = 0,9843). Jejaring co-occurrence kata kunci mengidentifikasi tiga klaster utama speech emotion recognition/deep learning, convolutional neural network/face recognition, dan klaster deskriptor demografis (human, emotion) yang dikonfirmasi oleh peta tematik sebagai tema motor (motor themes) dengan sentralitas dan densitas tinggi. Studi ini memberikan peta terkini struktur intelektual bidang emotion recognition berbasis AI serta mengidentifikasi celah penelitian pada integrasi multimodal lintas budaya dan generalisasi lintas dataset sebagai agenda riset masa depan.
Co-Authors Andri Dermawan Dedy Irfan Hansi Effendi Khayla Tsabita Marina Elfera Rahmawari Azril Titin Sriwahyuni