About the Journal
Jurnal Teknik Informatika C.I.T Medicom is a scientific journal that contains scientific writings on the results of pure research and applied research in the field of Informatics Engineering as well as general reviews of the development of related theories, methods, and applied sciences. can be seen more clearly below:
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Artificial Intelligence (AI)
- Machine Learning and Deep Learning
- Natural Language Processing (NLP)
- Computer Vision and Pattern Recognition
- Expert Systems and Decision Support Systems
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Big Data and Data Science
- Data Analysis and Visualization
- Large-Scale Data Processing
- Data Mining and Knowledge Discovery
- Cloud and Edge Computing Technologies
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Cybersecurity
- Cryptography and Information Security
- Threat Detection and Prevention Systems
- IoT Security and Digital Infrastructure
- Digital Forensics and Data Privacy
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Internet of Things (IoT)
- IoT Integration in Industry and Healthcare
- IoT Communication Protocols
- AI-Based IoT Systems Development
- Sustainability and Energy Efficiency in IoT
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Software Engineering
- Agile Development and DevOps
- Software Testing and Validation
- Formal Methods in System Development
- Component-Based System Integration
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Network and Communication Technologies
- 5G and 6G Network Architectures
- Wireless and Satellite Communication Systems
- Network Optimization and Communication Protocols
- Sensor Technology and Smart Grids
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Multimedia Processing
- Video Compression and Streaming
- Speech and Audio Recognition
- Virtual Reality (VR) and Augmented Reality (AR)
- Game Applications and Multimodal Interactions
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Cloud and Distributed Computing
- Cloud Virtualization and Infrastructure
- Grid and Fog Computing Systems
- Reliability and Scalability of Distributed Systems
- Blockchain and Distributed Ledger Technology
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Information Systems and Technology Management
- Digital Transformation in Public and Private Sectors
- Enterprise Resource Planning (ERP) Systems
- Information System Integration and Management
- Technology Strategy and Innovation
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Educational Technology
- AI-Based Adaptive Learning Systems
- Development of e-Learning and m-Learning Platforms
- Gamification in Education
- Virtual Labs and Simulation in Learning
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Theoretical Computing and Algorithms
- Algorithm Design and Analysis
- Parallel Programming and Quantum Computing
- Combinatorial Optimization and Heuristics
- Complexity Theory and Automata
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Technological Applications in Real Life
- Smart City and Transportation Systems
- Digital Health Applications (e-Health and m-Health)
- Smart Agriculture Applications
- Renewable Energy and Environmental Management
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Foundations of Computing
- Computational Complexity Theory: Classifications (P, NP, PSPACE, etc.), reductions, and completeness.
- Automata Theory: Finite automata, pushdown automata, Turing machines, and their applications.
- Formal Languages: Context-free grammars, regular languages, and parsing techniques.
- Lambda Calculus and Formal Methods: Applications in programming language design and verification.
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Algorithm Design and Optimization
- Classic Algorithm Design Paradigms: Divide and conquer, greedy algorithms, dynamic programming, and backtracking.
- Approximation Algorithms: Techniques for tackling NP-hard problems with provable performance bounds.
- Parallel and Distributed Algorithms: Strategies for shared and distributed memory models.
- Online Algorithms and Competitive Analysis: Real-time decision-making algorithms.
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Graph Theory and Network Algorithms
- Graph Algorithms: Shortest path, maximum flow, and graph coloring techniques.
- Network Optimization: Spanning trees, clustering, and routing protocols.
- Spectral Graph Theory: Eigenvalues of graphs and their applications in machine learning and network analysis.
- Social Network Analysis: Algorithms for community detection and influence maximization.
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Heuristics and Metaheuristics
- Evolutionary Algorithms: Genetic algorithms, particle swarm optimization, and ant colony optimization.
- Local Search and Simulated Annealing: Techniques for combinatorial optimization problems.
- Hybrid Metaheuristic Approaches: Combining multiple strategies for enhanced performance.
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Computational Geometry
- Convex Hulls, Voronoi Diagrams, and Delaunay Triangulations.
- Geometric Algorithms for Robotics, CAD, and GIS.
- Intersection, Clustering, and Shape Matching Problems.
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Randomized and Probabilistic Algorithms
- Monte Carlo and Las Vegas Algorithms: Trade-offs in accuracy and runtime.
- Probabilistic Analysis of Algorithms: Average-case performance evaluation.
- Markov Chains and Random Walks: Applications in optimization and machine learning.
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Quantum Computing and Algorithms
- Quantum Complexity Classes: BQP, QMA, and more.
- Quantum Algorithm Development: Shor's algorithm, Grover's search, and quantum simulation techniques.
- Quantum Error Correction and Cryptography.
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Bioinformatics and Computational Biology Algorithms
- Sequence Alignment and Phylogenetic Tree Construction.
- Protein Structure Prediction and Molecular Docking.
- Genome Assembly and Analysis.
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Emerging Areas in Algorithm Development
- Algorithms for Big Data: Streaming algorithms, sublinear algorithms, and MapReduce paradigms.
- Algorithms for AI and Machine Learning: Neural architecture search and gradient-free optimization.
- Energy-Efficient and Green Algorithms: Computational techniques for sustainability.
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Applications of Theoretical Computing in Interdisciplinary Domains
- Financial Computing: High-frequency trading algorithms, portfolio optimization, and risk analysis.
- Cryptography and Secure Computation: Homomorphic encryption and multi-party computation.
- Computational Social Science: Modeling human behavior and decision-making algorithms.
- Game Theory and Mechanism Design: Algorithms for competitive and cooperative scenarios.
This comprehensive scope allows for the exploration of both classical and cutting-edge developments in theoretical computing and algorithmic research, fostering innovation across scientific and industrial domains.
