Focus and Scope

JACKA journal published by the Informatics Engineering Program, Faculty of Engineering, Universitas Malikussaleh to accommodate the scientific writings of the ideas or studies related to informatics science.

JACKA journal published many related subjects on informatics science such as (but not limited to):

  • Adversarial Machine Learning: Addressing security concerns and developing algorithms robust to adversarial attacks.
  • Anomaly Detection Algorithms: Identifying unusual patterns or outliers in data.
  • Automated Machine Learning (AutoML): Developing algorithms that automate the machine learning model selection and hyperparameter tuning.
  • Automated Planning and Scheduling: Developing algorithms for autonomous decision-making and task scheduling.
  • Bayesian Networks: Utilizing probability theory to model and analyze uncertain systems.
  • Computer Vision: Developing algorithms for image and video analysis, enabling machines to interpret visual information.
  • Constraint Satisfaction Problems (CSP): Designing algorithms to solve problems subject to constraints.
  • Deep Learning: Developing algorithms for neural networks with multiple layers to model complex patterns.
  • Distributed AI Algorithms: Implementing AI algorithms that can work across multiple interconnected devices or nodes.
  • Ensemble Learning: Combining multiple models to improve overall system performance.
  • Evolutionary Algorithms: Utilizing principles of natural selection for optimization and problem-solving.
  • Experiential Learning Algorithms: Designing algorithms that improve performance through experience and learning.
  • Expert Systems: Creating rule-based systems that emulate human expertise in specific domains.
  • Explainable AI (XAI): Developing algorithms that provide transparency and explanations for AI decisions.
  • Fuzzy Logic: Implementing logic that deals with uncertainty and imprecision in decision-making.
  • Genetic Algorithms: Implementing algorithms inspired by genetic evolution for optimization tasks.
  • Knowledge Representation and Reasoning: Creating structures and algorithms to represent and manipulate knowledge.
  • Machine Learning Algorithms: Designing algorithms that enable systems to learn from data and make predictions.
  • Multi-agent Systems: Designing algorithms for systems with multiple interacting agents.
  • Natural Language Processing (NLP): Creating algorithms that understand and process human language.
  • Neuroevolution: Combining evolutionary algorithms with neural networks for optimization.
  • Optimization Algorithms: Developing algorithms focused on improving the performance, efficiency, or decision-making of systems by finding optimal solutions to problems.
  • Pattern Recognition: Developing algorithms to identify patterns within data.
  • Reinforcement Learning: Designing algorithms that learn through trial and error, often applied in decision-making systems.
  • Robotics Algorithms: Designing algorithms for autonomous navigation, manipulation, and decision-making in robots.
  • Semantic Web Technologies: Implementing algorithms for structuring and retrieving information on the web.
  • Sentiment Analysis Algorithms: Analyzing text data to determine sentiment or emotion.
  • Speech Recognition: Developing algorithms to convert spoken language into text.
  • Swarm Intelligence: Developing algorithms based on collective behavior, as seen in swarms or colonies.
  • Time Series Forecasting Algorithms: Predicting future values based on historical data patterns.