Focusand Scope

Focus

Jurnal Computech & Bisnis (JCB) is a peer-reviewed scientific journal published by STMIK Mardira Indonesia. The journal focuses on the advancement of computer science and informatics through the development, analysis, implementation, and evaluation of computational methods, software engineering approaches, information security techniques, computational intelligence, and intelligent information systems.

JCB provides a scholarly platform for researchers, academics, and practitioners to disseminate original research that contributes to the development of computational theories, methods, algorithms, architectures, and intelligent technological solutions.

The journal prioritizes research that demonstrates clear scientific novelty, methodological rigor, reliable experimental or empirical evaluation, and meaningful contributions to computer science and informatics. Studies involving the application of established technologies are considered when they provide substantial methodological improvements, new technical insights, rigorous comparative evaluations, or significant findings that advance knowledge and practice.

JCB encourages interdisciplinary research in which computational methods and informatics technologies are developed or applied to address complex problems in industry, business, public services, education, healthcare, and other relevant application domains.

Scope

JCB welcomes original research articles, methodological studies, experimental investigations, and rigorous system development and evaluation studies in the following areas.

1. Computational Methods and Algorithm Development

This area covers the development, analysis, optimization, and evaluation of computational methods and algorithms for solving scientific, engineering, and real-world problems.

Topics include, but are not limited to:

  • Algorithm design, analysis, and optimization.
  • Computational modeling and simulation.
  • Numerical and scientific computing.
  • Data structures and algorithmic techniques.
  • Optimization algorithms and metaheuristic methods.
  • Computational problem-solving and decision-support methods.
  • Performance analysis and comparative evaluation of computational algorithms.
  • Efficient computational techniques for large-scale and complex data.
  • Reproducible computational experiments and benchmarking.

Research in this area should demonstrate methodological relevance, technical rigor, and a clear contribution beyond the routine application of existing algorithms.

2. Software Engineering and Software Systems

This area focuses on methods, architectures, processes, and technologies for the systematic development, testing, evaluation, and maintenance of software systems.

Topics include, but are not limited to:

  • Software requirements engineering and specification.
  • Software architecture, design patterns, and architectural evaluation.
  • Software development methods and engineering processes.
  • Software testing, verification, validation, and quality assurance.
  • Software quality measurement and reliability engineering.
  • Software maintenance, evolution, and reengineering.
  • Model-driven engineering and formal methods.
  • DevOps, continuous integration, and continuous delivery.
  • Cloud-native applications, distributed software, and microservices.
  • Software performance, scalability, and resource optimization.
  • Empirical software engineering and comparative studies of software engineering methods.

JCB particularly welcomes studies that evaluate the effectiveness, reliability, quality, or performance of software engineering approaches through systematic methods and verifiable evidence.

3. Information Security and Cybersecurity

This area covers the development, analysis, implementation, and evaluation of methods and technologies for protecting information, digital assets, computing infrastructure, and software systems.

Topics include, but are not limited to:

  • Network and application security.
  • Cyberattack detection and intrusion detection systems.
  • Malware analysis and threat detection.
  • Cryptographic methods and secure communication.
  • Authentication, authorization, and access control.
  • Privacy-preserving computing and data protection.
  • Security assessment and vulnerability analysis.
  • Secure software engineering and application security testing.
  • Security monitoring and incident detection.
  • Digital forensics and cybercrime investigation methods.
  • Security analytics using computational intelligence.
  • Risk assessment and evaluation of cybersecurity mechanisms.

Research involving security testing or vulnerability analysis must be conducted with appropriate authorization and in accordance with applicable legal, ethical, and responsible-disclosure requirements.

4. Computational Intelligence, Machine Learning, and Data Mining

This area focuses on computational techniques that enable systems to learn from data, recognize patterns, make predictions, optimize decisions, and solve complex problems.

Topics include, but are not limited to:

  • Machine learning algorithms and model development.
  • Deep learning architectures and optimization.
  • Data mining, knowledge discovery, and pattern recognition.
  • Artificial neural networks and representation learning.
  • Evolutionary computation and swarm intelligence.
  • Fuzzy systems and hybrid intelligent methods.
  • Reinforcement learning and adaptive computational systems.
  • Explainable and interpretable artificial intelligence.
  • Intelligent prediction, classification, clustering, and anomaly detection.
  • Feature engineering, feature selection, and dimensionality reduction.
  • Model evaluation, benchmarking, and generalization analysis.
  • Responsible, robust, and trustworthy computational intelligence.

JCB prioritizes studies that establish a clear research problem, use appropriate experimental designs, apply suitable evaluation metrics, compare methods against relevant baselines, and provide a critical interpretation of their findings.

Studies that merely apply an existing algorithm to a dataset without a meaningful methodological contribution, rigorous evaluation, or substantive analytical insight may be considered insufficient for publication.

5. Intelligent Information Systems and Decision Support

This area focuses on the design, development, integration, and evaluation of information systems that incorporate computational intelligence, data-driven methods, automation, or adaptive capabilities to support complex processes and decisions.

Topics include, but are not limited to:

  • Intelligent information system architectures.
  • Decision-support systems and intelligent decision-making.
  • Knowledge-based systems and expert systems.
  • Data-driven information systems.
  • Intelligent business and enterprise applications.
  • Recommendation systems and personalized information services.
  • Intelligent process automation.
  • Predictive analytics and operational decision support.
  • Integration of machine learning into information systems.
  • Knowledge representation and knowledge-based reasoning.
  • Evaluation of intelligent information system performance and effectiveness.
  • Human-centered evaluation of intelligent systems.

Research in this area should establish the technical or methodological contribution of the system and provide appropriate evidence of its performance, reliability, usability, effectiveness, or decision-support value.

6. Internet of Things and Intelligent Computing Applications

This area covers computing methods, architectures, and technologies for connected devices, embedded systems, distributed sensing, and intelligent data processing.

Topics include, but are not limited to:

  • Internet of Things architectures and protocols.
  • Embedded and sensor-based computing systems.
  • Edge and fog computing.
  • Intelligent sensor data processing.
  • IoT data analytics and anomaly detection.
  • Resource-efficient computing for connected devices.
  • IoT security and privacy.
  • Smart monitoring and automated control systems.
  • Performance, latency, energy-efficiency, and reliability evaluation.
  • Integration of IoT with machine learning and intelligent information systems.

Studies should provide an appropriate technical evaluation of the proposed architecture, method, prototype, or application rather than relying solely on a description of system implementation.

7. Data Engineering and Intelligent Data Processing

This area focuses on methods and technologies for collecting, managing, processing, analyzing, and transforming data into reliable computational results and actionable information.

Topics include, but are not limited to:

  • Data preprocessing and data quality assessment.
  • Data integration and heterogeneous data processing.
  • Big data architectures and distributed data processing.
  • Data analytics and computational data modeling.
  • Information extraction and text mining.
  • Time-series analysis and forecasting.
  • Graph analytics and network analysis.
  • Data representation and feature engineering.
  • Efficient data processing and query optimization.
  • Data pipelines for machine learning and intelligent systems.
  • Evaluation of data-driven computational methods.

Research should explain the methodological choices, data characteristics, evaluation procedures, and contribution of the proposed approach.

Research Contribution and Editorial Priority

JCB prioritizes manuscripts that demonstrate one or more of the following contributions:

  1. Methodological contribution: A new computational method, algorithm, model, or substantial improvement to an existing approach.
  2. Technical contribution: A novel or rigorously evaluated software architecture, engineering technique, security mechanism, or intelligent system.
  3. Empirical contribution: Reliable experimental evidence, systematic benchmarking, comparative analysis, or reproducible findings that improve understanding of computational methods and systems.
  4. Analytical contribution: New insights into algorithm behavior, system performance, model limitations, computational efficiency, or the conditions under which a method is effective.
  5. Applied scientific contribution: A well-designed and rigorously evaluated computational solution that addresses a significant problem and provides insights beyond routine system implementation.

The expected contribution depends on the research topic and methodology. Not every manuscript is required to introduce a completely new algorithm; however, every manuscript must establish a clear scholarly contribution supported by appropriate evidence.

Manuscripts Outside the Scope

JCB generally does not prioritize manuscripts that consist primarily of routine software implementation, basic information system development, descriptive technology adoption without a substantial computational contribution, or straightforward application of existing algorithms without adequate evaluation or analytical insight.

Manuscripts focused exclusively on business management, accounting, general organizational behavior, or non-computational information systems issues may be more appropriate for journals specializing in those areas unless they make a substantial and clearly demonstrated contribution to computer science or informatics.

Manuscripts must also comply with the journal’s requirements concerning originality, research ethics, authorship, plagiarism, conflicts of interest, and peer review.

Publication Objective

Through its focused scope and commitment to scholarly quality, JCB aims to contribute to the advancement of computer science and informatics by publishing research that is original, methodologically sound, technically relevant, and valuable to the international research community.

The journal seeks to strengthen the connection between computational theory, engineering practice, intelligent technologies, and evidence-based technological innovation.