Indonesian Journal of Artificial Intelligence and Multimodal Systems (IJAIMS) is a peer-reviewed, open-access journal that publishes high-quality research advancing the theory, methodology, and applications of Artificial Intelligence (AI) and multimodal systems. The journal welcomes works that integrate and reason across multiple data modalities—such as text, images, audio, video, sensor streams, and structured data—to build intelligent and reliable systems with scientific and societal impact.

Scope of Topics

IJAIMS considers (but is not limited to) the following topics:

• Multimodal Learning & Reasoning: multimodal fusion, alignment, grounding, cross-modal retrieval, multimodal agents
• Foundation Models: large-scale pretraining, self-supervised learning, transfer learning, scaling, efficient adaptation
• Natural Language Processing (NLP): language understanding and generation, information extraction, dialogue systems, summarization, machine translation
• Computer Vision: image/video understanding, detection, segmentation, recognition, visual question answering, medical imaging
• Speech & Audio AI: speech recognition, speaker modeling, audio event detection, multimodal speech-language systems
• Generative AI: diffusion models, GANs, multimodal generation, controllable generation, synthetic data
• Graph & Structured AI: graph neural networks, knowledge graphs, causal and relational learning
• Trustworthy & Responsible AI: explainability, robustness, fairness, privacy, safety, governance, evaluation and auditing
• AI Systems & Engineering: MLOps, model compression, efficient inference, edge AI, distributed training, benchmarking
• Applications: healthcare, education, smart city, agriculture, industry, finance, public services, and other real-world domains

Types of Manuscripts

• Research Articles (original contributions with rigorous methodology and evaluation)
• Review / Survey Articles (critical synthesis of recent progress, challenges, and research directions)

Submission Criteria

Submissions must demonstrate novelty, technical rigor, and clear contributions. Authors are encouraged to include reproducibility elements (e.g., data/code availability statements) when possible. All manuscripts undergo a double-blind peer-review process by at least two independent reviewers.