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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) POLYGLOT Jurnal Pendidikan Dasar JOHME: Journal of Holistic Mathematics Education JURNAL PENDIDIKAN TAMBUSAI IJoASER (International Journal on Advanced Science, Education, and Religion) Jurnal Studi Guru dan Pembelajaran KOLOKIUM: Jurnal Pendidikan Luar Sekolah Diligentia: Journal of Theology and Christian Education JIIP (Jurnal Ilmiah Ilmu Pendidikan) Jurnal Amanat Agung DEDIKASI PKM Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Prosiding Konferensi Nasional PKM-CSR Jurnal Al-Qiyam Jurnal Tana Mana Aletheia Christian Educators Journal ELIGIBLE : Journal of Social Sciences EduInovasi: Journal of Basic Educational Studies Journal Of Economic Sciences (Ekuisci) Journal of Education Sciences (Edusci)
Budi Wibawanta, Budi
Universitas Pelita Harapan
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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An AI-Based Personalized Learning Framework for Corporate Employee Development: An Integrative Literature Synthesis Dzaky Mubarok; Khoe Yao Tung; Budi Wibawanta
Jurnal Ekuisci Vol 3 No 6 (2026): Vol 3 No 6 July 2026
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/ekuisci.v3i6.1232

Abstract

Background: Accelerating digital transformation, encompassing widespread business process automation and the adoption of artificial intelligence, has widened the competency gap between current workforce capabilities and future organizational demands. This condition positions the corporate Learning and Development (L&D) function as a strategic pillar for sustaining competitive advantage and simultaneously heightens the urgency of integrating AI into corporate learning systems. Purpose: Aims. This study synthesized, through an integrative approach, empirical and conceptual literature on AI-based personalized learning frameworks for corporate employee development to produce a coherent conceptual framework. Method: A Systematic Literature Review (SLR) design was employed, utilizing qualitative meta-synthesis guided by the PRISMA 2020 protocol. Research questions were formulated using the SPIDER framework. Systematic searches were conducted across five major academic databases, Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar, covering publications from 2020 to 2025. Results: From 1,847 initially identified articles, 1,203 unique records remained after deduplication. Title and abstract screening yielded 312 articles, and full-text screening produced a final synthesis corpus of 47 articles. Findings confirm that AI-driven personalized learning systems have a significant capacity to address workforce competency gaps arising from digital transformation. Conclusion: This study produced a comprehensive AI-based personalized learning framework by integrating perspectives from educational technology, human resource management, and artificial intelligence. Implementation. Organizations are advised to adopt this framework as a strategic response to the imperatives of reskilling and upskilling in the digital transformation era.
Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning Evander Banjarnahor; Budi Wibawanta; Ronald Belferik; Rijanto Purbojo
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1322-1330

Abstract

Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Co-Authors Agnes Jumarta Gulo Ariani Padang Asih Enggar Susanti, Asih Enggar Banjarnahor, Evander Bernard Napitupulu Billy Mumu Cendana, Wiputra Christina Dwi Putri Debora Stefany Sitepu Dwioktorianto Effendy, Samuel Dzaky Mubarok Ester Abigail Tanio Fanggidae, Aldy M Hendra Tan Imanuel Adhitya Wulanata, Imanuel Adhitya Januari, Vivian Khoe Yao Tung Lindawati Manalu, Mei Lora Tri Oktavia Mangunsong, Febri M. Marianto Sarassang Marisa Milenia Milenia, Marisa Mumu, Billy Neneng Andriani Novemelia Purba Oce Datu Appulembang, Oce Datu Purba, Carry Budi Dharma Purba, Enjel Natanael Rijanto Purbojo Ronald Belferik Sagala, Prima V Samuel Dwioktorianto Effendy Sarassang, Marianto Sarwa, Edy Prasetyo Sitepu, Debora Stefany Suryaatmadja, Caroline Tony Santoso Wijaya, Yeny