Classification of Potential Blood Donors Using Artificial Neural Networks and Alternating Least Squares

Handojo, Andreas and Octavia, Tanti and ARDITANTI, WIDYA (2023) Classification of Potential Blood Donors Using Artificial Neural Networks and Alternating Least Squares. [UNSPECIFIED]

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Abstract

Blood has an important role in human life. Lack of blood can cause illness and even death. To meet the needs of human blood, it requires blood donors from other people. The problem that occurs is that everyone can only donate blood once every three months. The problem is that not all blood donors routinely donate blood every three months. Therefore, it is important for the Red Cross to be able to classify active and passive donors, especially when there is an urgent need for blood. A method is needed that can be used to provide recommendations for active donors who have a high probability of donating blood so that they can do so immediately. So that the Red Cross can conduct the selection of potential donors more effectively and so that there are not many expired blood stocks. In this study, the classification of potential donors was carried out using an Artificial Neural Network and Alternating Least Squares. The results from the list of potential donor recommendations are then tested using the Mean Reciprocal Rank. The test results show a result of 0.0052186103057361, with an average time of 127. With the Artificial Neural Network model that uses two dense layers of 1024 nodes, 10 epochs, and a sigmoid as the activation function, the training accuracy is 98.90% and the testing accuracy is 99.20%.

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