PREDIKSI POTENSI PELUNASAN SEBAGAI SALAH SATU KRITERIA KELAYAKAN CALON DEBITUR MENGGUNAKAN DECISION TREE C4.5 (STUDI KASUS PADA PERUSAHAAN PEMBIAYAAN KENDARAAN); PREDICTION POTENTIAL REPAYMENT FEASIBILITY CRITERIA AS ONE CANDIDATE DEBTOR USING DECISION TREE C4.5 (CASE STUDY ON FINANCING AUTOMOTIVE)

HERMANTO, BAMBANG, Azhari SN

2016 | Disertasi | FMIPA

In an effort to anticipate the occurrence of errors in the selection of potential borrowers and increase the quality of customer service along with the continued increase in the number of new prospective borrower credit financing the purchase of motorcycles, finance companies require decision-making tools that simplify and accelerate the process of prediction prospective borrowers are unable to repay the loan. The study discusses the process of building a decision tree algorithm C4.5 and utilize training data group motorcycle debtor financing. The decision tree is then interpreted into the form of decision rules are easy to understand to be used as a reference in assessing the potential repayment of credit borrowers. The test results through the five categories of tests performed in the process generate tree takes an average time of 112 seconds with the acquisition of the fastest time on the first test categories with the number of 3000 data records worth of 9 seconds. While in the process of generating rules takes an average time of 1.78 seconds in the fastest time with the acquisition of the first test categories with the number of 3000 data records worth of 1.23 seconds. Comparison of the amount of data affecting the value of each category of test execution time, the more data the more longer to generate tree and rules. In testing the accuracy of the data obtained by the average percentage of the value of the data accuracy of 51.2% with the highest gains in the first test categories with a total of 3000 data record is worth 54%.

Kata Kunci : credit, C4.5, decision tree