The International Journal of Machine Learning (IJOML) provides a global forum for disseminating high-quality, peer-reviewed research on theoretical foundations, methodological innovations, and applied advancements in machine learning. The journal emphasizes transparency, reproducibility, and accessibility of data, algorithms, and processes to foster accountable and impactful scientific progress.

IJOML welcomes original contributions, surveys, and case studies that enhance the understanding and application of machine learning in both academic and industrial contexts. The journal is published twice a year, in June and December.

Journal Information Details
Original Title International Journal of Machine Learning
Short Title IJOML
Abbreviation International Journal of Machine Learning 
Frequency 2 Issues per year (June and December)
Publisher APJIKOM
DOI 10.52436/1.ijoml.year.vol.no.IDPaper
P-ISSN xxxx-xxxx
e-ISSN 3124-6362
Indexing -
Discipline Machine Learning

 

International Journal of Machine Learning (IJOML) has published papers from authors with different country. Diversity of author's in IJOML:

Vol. 1 No. 1, June 2026 : Indonesia, Malaysia, Poland

 

 

 

Current Issue

Vol. 1 No. 1 (2026): IJOML Volume 1, Number 1, June 2026

International Journal of Machine Learning (IJOML) Volume 1, Number 1, June 2026 was published on January 28, 2026. IJOML in this edition has received quite a lot of article submissions, but in the process some of the best articles have been selected according to the results of the review. This edition published article from several affiliations, including : Universitas Jenderal Soedirman (Indonesia), Universitas Siliwangi (Indonesia), Universiti Teknikal Malaysia Melaka (Malaysia), HallymUniversity (Republic of Korea), Universitas Amikom Purwokerto (Indonesia), Wroclaw University of Science and Technology (Poland), Universitas Nahdlatul Ulama Kalimantan Timur (Indonesia).

 

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Published: 2026-01-28

Full Issue

IJOML Vol. 1, No. 1, June 2026

Articles

Evaluation of Undersampling and Oversampling Techniques in Term Deposit Prediction: A Gradient Boosting Approach

Lasmedi Afuan, Abdul Karim, Ipung Permadi

1-11

PDF

Benchmarking Modern Optimizers for IndoBERT-Based Sentiment Analysis on Indonesian Gojek Reviews

Randi Rizal, Hidayatulah Himawan

12-22

PDF

RoBERTa with Sample Reweighting and Temperature Scaling for Imbalanced Toxicity Detection: A Performance–Fairness–Calibration Study

Lasmedi Afuan, Nurul Hidayat, Abdul Karim

23-37

PDF

Cold-Start Generalization in Educational Interaction Data: Comparing Student-Wise and Question-Wise Splits with Probabilistic Calibration

Purwadi Purwadi, Othman Bin Mohd, Nor Azman Bin Abu

38-50

PDF

Stability-Aware Hierarchical Forecasting: Synergizing Conformal Prediction with Decomposition Ensembles

Damar Nurcahyono, Rajiansyah Rajiansyah, Hamdani Hamdani

51-63

PDF
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The International Journal of Machine Learning (IJOML) provides a global forum for disseminating high-quality, peer-reviewed research on theoretical foundations, methodological innovations, and applied advancements in machine learning. The journal emphasizes transparency, reproducibility, and accessibility of data, algorithms, and processes to foster accountable and impactful scientific progress.

IJOML welcomes original contributions, surveys, and case studies that enhance the understanding and application of machine learning in both academic and industrial contexts. The journal is published twice a year, in June and December. IJOML published by APJIKOM.