Main Article Content
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.
Authors who publish with Positif : Jurnal Sistem dan Teknologi Informasi agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License.
 M. Fuady, H. P. Kontemporer, and C. A. Bakti, “Munir Fuady, Hukum Perkreditan Kontemporer , Citra Aditya Bakti, Bandung, 2002, h. 5.”
 Hariati, M. Wati, and B. Cahyono, “Penerapan Algoritma C4.5 Decision Tree pada Penentuan Penerima Program Bantuan Pemerintah Daerah Kabupaten Kutai Kartanegara,” Jurti, vol. 2, no. 1, pp. 27–36, 2018.
 I. Choina, R. Aulia, and A. Zakir, “Penerapan Algoritma ID3 Untuk Menyeleksi Pegawai Kontrak Di Kantor Pengadilan Kota Langsa,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 1, pp. 47–52, 2020.
 Ilayani, J. Nangi, and yuwanda purnamasari Pasrun, “APLIKASI DATA MINING UNTUK PENILAIAN KREDIT MENGGUNAKAN DECISION TREE ALGORITMA ID3 STUDI KASUS PT. MANDALA MULTI FINANCE CABANG KENDARI Ilayani*1,” semanTIK, vol. 4, no. 1, pp. 65–76, 2018.
 M. Y. Helmy and D. Kushartantya, “Implementasi Data Mining Untuk Memprediksi Kelayakan Permintaan Pinjaman Nasabah Di Lembaga Keuangan,” vol. 2, no. 1, pp. 267–274, 2013.