About the Journal

The International Journal of Artificial Intelligence and Science (IJAIS) is independently organized and managed by the Asosiasi Doktor Sistem Informasi Indonesia (ADSII). IJAIS is an open-access journal designed for researchers, lecturers, and students to publish their findings in the fields of Artificial Intelligence and Science. IJAIS serves as a platform for sharing innovative and original research, showcasing the latest advancements and technological developments in Artificial Intelligence and Science.

Focus and Scope

The International Journal of Artificial Intelligence and Science (IJAIS) aims to advance research and knowledge in the fields of Artificial Intelligence and Science, aligning with the Sustainable Development Goals (SDGs) and the expansive universe of AI technologies. The scope of IJAIS encompasses the following areas:

1. AI Technology and Innovations:

  • Artificial Intelligence (AI): Broad exploration of AI technologies, including knowledge representation, planning and scheduling, natural language processing, computer vision, and robotics.
  • Machine Learning (ML): Research on various ML techniques such as supervised, unsupervised, and reinforcement learning, along with decision trees, support vector machines, and ensemble learning.
  • Neural Networks: Studies on neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multi-layer perceptrons (MLPs).
  • Deep Learning: Innovations in deep learning, focusing on deep neural networks, deep convolutional neural networks, long short-term memory (LSTM), and generative adversarial networks (GANs).
  • Generative AI: Research on generative AI technologies, including language modeling, transfer learning, transformer architectures, and text generation.

2. Emphasis Areas:

  • AI Ethics and Cognitive Computing: Ethical considerations and the integration of cognitive computing in AI applications.
  • AI in Natural Language Processing: Enhancements in natural language understanding, dialogue systems, and automated reasoning.
  • Feature Engineering and Dimensionality Reduction: Techniques in feature engineering and dimensionality reduction to improve AI model performance.
  • Clustering and Regression: Advanced methods in clustering and regression within AI and machine learning contexts.

3. Contributions to Sustainable Development Goals (SDGs):

  • No Poverty (SDG 1): AI applications that help alleviate poverty by creating economic opportunities and improving access to resources.
  • Zero Hunger (SDG 2): Innovations in AI to enhance food security through improved agricultural practices and food distribution systems.
  • Good Health and Well-being (SDG 3): AI solutions in healthcare, including predictive analytics, personalized medicine, and health monitoring.
  • Quality Education (SDG 4): AI-driven educational technologies that promote accessible and quality education for all.
  • Gender Equality (SDG 5): Studies on AI’s role in promoting gender equality and reducing biases in decision-making processes.
  • Clean Water and Sanitation (SDG 6): AI research focused on water resource management and sanitation improvements.
  • Affordable and Clean Energy (SDG 7): AI innovations in energy efficiency, renewable energy sources, and smart grid technologies.
  • Decent Work and Economic Growth (SDG 8): AI’s impact on job creation, workplace efficiency, and economic development.
  • Industry, Innovation, and Infrastructure (SDG 9): AI in enhancing industrial processes, fostering innovation, and improving infrastructure.
  • Reduced Inequalities (SDG 10): AI research aimed at reducing inequalities within and among countries.
  • Sustainable Cities and Communities (SDG 11): AI solutions for smart cities, urban planning, and community sustainability.
  • Responsible Consumption and Production (SDG 12): AI technologies promoting sustainable consumption and production patterns.
  • Climate Action (SDG 13): AI applications in climate change mitigation, adaptation, and environmental monitoring.
  • Life Below Water (SDG 14): AI research focused on marine conservation and sustainable use of ocean resources.
  • Life on Land (SDG 15): AI in biodiversity conservation, land use management, and ecological protection.
  • Peace, Justice, and Strong Institutions (SDG 16): AI’s role in promoting justice, security, and effective institutions.
  • Partnerships for the Goals (SDG 17): Collaborative AI research and partnerships that drive sustainable development.