About the Journal

Jurnal Ilmu Komputer dan Informatika (JIKI) is a scientific journal that publishes research articles in the field of Computer Science and Informatics. The journal particularly focuses on specific topics related to machine learning, data mining, and artificial intelligence. Jurnal Ilmu Komputer dan Informatika (JIKI) is registered with the Indonesian Institute of Sciences (LIPI) under P-ISSN: 2807-6664 and E-ISSN: 2807-6591. In addition, JIKI is registered with Crossref and provides a Digital Object Identifier (DOI) for each published article: https://doi.org/10.54082/jiki.IDPaper. 

Jurnal Ilmu Komputer dan Informatika (JIKI) has been accredited with SINTA 5 based on the Decree of the Director General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, Number Nomor 177/E/KPT/2024 (Download Accreditation Decree).

Jurnal Ilmu Komputer dan Informatika (JIKI) is published twice a year, in June and December. All submitted manuscripts undergo a double-blind peer review process by qualified reviewers. Manuscripts may be submitted in Indonesian or English. 

Focus and Scope

Jurnal Ilmu Komputer dan Informatika (JIKI) receives the submission of original research articles and literature review in computer science and informatics, specific in machine learning, data mining, and artificial intelligence, in the following areas:

• Machine Learning: Supervised learning; unsupervised learning; reinforcement learning; Semi-supervised and self-supervised learning; Online learning and incremental models; Ensemble methods and model aggregation; Deep learning (neural networks, convolutional networks, recurrent networks, transformers); Optimization methods for machine learning; Feature engineering and representation learning; Model interpretability and explainable machine learning; Transfer learning, domain adaptation, and multi-task learning; Probabilistic models and Bayesian learning; Generative models (GANs, VAEs, diffusion models); Federated learning and distributed machine learning; Applications in healthcare, finance, education, robotics, natural language processing, and computer vision.

• Data Mining: Data preprocessing and transformation; Pattern discovery and knowledge extraction; Classification regression, and clustering methods; Association rule mining and frequent pattern analysis; Anomaly and outlier detection; Feature selection and dimensionality reduction; Text mining and natural language data processing; Web mining and social network analysis; Sequential, temporal, and spatial data mining; Stream data mining and real-time analytics; Big data and scalable algorithms; Privacy-preserving data mining; Interpretability and explainable data mining; Applications of data mining in healthcare, finance, education, cybersecurity, and e-commerce.

• Artificial Intelligence: Knowledge representation and reasoning; Automated planning and scheduling; Search algorithms and heuristic methods; Constraint satisfaction and optimization; Natural language processing and understanding; Computer vision and image understanding; Speech recognition and synthesis; Intelligent agents and multi-agent systems; Expert systems and decision support systems; Robotics and autonomous systems; Cognitive architectures and human-like intelligence; Philosophical foundations of artificial intelligence; Distributed and collaborative AI; Hybrid intelligent systems; Applications of AI in healthcare, education, transportation, finance, and cybersecurity.