About the Journal
The Journal of Data Intelligence and Applied Machine Learning (JDIAML) is an open-access, peer-reviewed publication of the Makwa Foundation. The journal disseminates high-quality research on data intelligence, machine learning, and text analytics, with a strong emphasis on unsupervised and self-supervised learning, including clustering, topic discovery, representation learning, and segmentation applied to real-world data. The JDIAML invites methodological, empirical, and applied studies that demonstrate clear novelty, rigorous evaluation, and reproducibility.
The Journal of Data Intelligence and Applied Machine Learning (JDIAML) serves as an international forum for researchers and practitioners working on intelligent methods for extracting value from data, with a particular focus on textual, behavioural and multimodal datasets. The journal places a premium on research that contributes to the development of robust algorithms, the generation of sound evaluations, and the attainment of reproducible results.
The journal publishes original research articles, review articles, and short communications in the following areas: data mining, machine learning, natural language processing, and applied AI. The journal pays particular attention to unsupervised learning and weak-label settings.
The publication of this journal is undertaken by Yayasan Lembaga Studi Makwa (Makwa Foundation) in collaboration with the Program Studi Informatika, Faculty of Science and Technology, UIN Sjech M. Djamil Djambek Bukittinggi.
