Machine learning is a field in artificial intelligence that aims to make computer systems learn from data, without having to be explicitly programmed manually. By using algorithms and mathematical models, machines or computer systems can learn to perform certain tasks more accurately and efficiently as more data becomes available. Graph learning or graph learning is a branch in machine learning which aims to model data in the form of graphs or networks. A graph is a representation of data consisting of nodes connected by edges that represent the relationships or connections between these nodes. Graph Neural Network or GNN is a neural network specifically designed to process data in the form of graphs or networks.

As is the case with graph learning, GNN also utilizes graph structures to identify patterns and relationships between nodes in a network. As a form of downstreaming, PUI-PT Combinatorics and Graph held a training program that was held for 2 weeks. Participants in this training were Devi Eka Wardani Meganingtyas, S.Si., M.Si. from Jakarta State University and Desi Febriani Putri, S.Si., M.Si. from Mulawarman University. The material presented in this training includes Artificial Neural Networks, Graph Neural Networks, Rainbow Antimagic Coloring, and Disaster Sensors. Some of the presenters who delivered the material included: Prof. Drs. Dafik, M.Sc., Ph.D., Dr. Ika Hesti Agustin, S.Si., M.Si., Zainur Rasyid Ridlo, S.Pd., M.Pd., and Rifki Ilham Baihaki, S.Si., M.Mat.

This webinar also available on youtube with this link: (88) Komersialisasi Program Training - YouTube