Implementasi Iterative Dichotomiser 3 (ID3) Untuk Penentuan Kelayakan Pemberian Kredit Pada PT.BPR Ploso Saranaartha Jombang
DOI:
https://doi.org/10.31961/positif.v7i1.1064Keywords:
classification, data mining, ID3, PT.BPR Ploso Saranaartha JombangAbstract
PT. BPR Ploso Saranaartha Jombang has several problems that often arise regarding to the provision of credit to debtors. At present the bank is still giving credit to its customers by selecting debtors, there is no systematic procedure in determining whether a customer is eligible for credit or not. This is what causes a lot of bad credit that can harm the bank. Iterative Dichotomiser 3 (ID3) algorithm can be used to solve this case. In completing it, ID3 will do a data preprocessing process first, which aims to discard data that is not important to get the data that is needed. After that ID3 will form a decision tree based on the rules generated. Each root node in a decision tree is formed based on the rules generated. Each root node in a decision tree is formed based on the largest gain value of each input attribute. In calculating this algorithm, a sufficient dataset is needed to use the training process. The dataset used for this training process is 300 data records consisting of 272 data with good collectability and 28 data with bad collectability. There is also data that will be used for the testing process totaling 20 new customer data records consisting of 8 data with bad collectability and 12 data with good collectability. In the trials that have been carried out on the dataset produced 10 rules. After the data testing, the output with 88, 51% accuracy is produced. This means that from 300 data records that have been trained, they can cover 19 data from 20 testing data records.
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