2 research outputs found
Predicting Potential Blood Donors Who Can Attend Blood Donation Activities using a Support Vector Machine
Lack of blood will be fatal for the human
body. Current technology has not been able to produce
human blood, therefore blood donors from other people
are needed. Because of this need, the Red Cross
organized blood donation activities to obtain blood
supplies. Blood donation is given by people voluntarily,
therefore it is difficult to predict how much blood
supply will be obtained in an organized blood donation
activity. A system is needed to predict the number of
potential donors so that the supply is sufficient. This
study will classify potential blood donors and predict
the number of blood donors who have the possibility to
attend a blood donation activity held certain location at
a certain time by using a support vector machine. With
this prediction, the Red Cross can predict in advance
how many bags of blood can be obtained before
carrying out blood donation activities at a certain
location at a certain time. In this way, the Red Cross
will be able to find the right place and date to obtain
maximum blood donation. The dataset used is data on
blood donations carried out by donors at the Indonesian
Red Cross in 2015 - 2019 with a total data of 53708
donors. From this study, it was found that the
application made was able to classify potential donors
and predict donors who could be present to give donors
at a certain location and at a certain time with an F1
Score of 85%
Predicting Potential Blood Donors Who Can Attend Blood Donation Activities using a Support Vector Machine
Lack of blood will be fatal for the human body. Current technology has not been able to produce human blood, therefore blood donors from other people are needed. Because of this need, the Red Cross organized blood donation activities to obtain blood supplies. Blood donation is given by people voluntarily, therefore it is difficult to predict how much blood
supply will be obtained in an organized blood donation
activity. A system is needed to predict the number of
potential donors so that the supply is sufficient. This
study will classify potential blood donors and predict
the number of blood donors who have the possibility to
attend a blood donation activity held certain location at
a certain time by using a support vector machine. With
this prediction, the Red Cross can predict in advance
how many bags of blood can be obtained before
carrying out blood donation activities at a certain
location at a certain time. In this way, the Red Cross
will be able to find the right place and date to obtain
maximum blood donation. The dataset used is data on
blood donations carried out by donors at the Indonesian
Red Cross in 2015 - 2019 with a total data of 53708
donors. From this study, it was found that the
application made was able to classify potential donors
and predict donors who could be present to give donors
at a certain location and at a certain time with an F1
Score of 85%