Implementation of Data Mining to Predict the Eligibility Level for Prospective KPR (Home Ownership Credit) Subsidized Housing Customers Mitra Griya Indah Using the C4.5 Algorithm - Anggraini, Mia; Ruziq, Fahmi; Nuary Singarimbun, Roy (PT. Technology Laboratories Indonesia (TechnoLabs), 2024)
In the housing industry, data mining plays an important role in assisting the home loan application process by extracting knowledge from historical data, this process allows lenders to identify potentially high-risk home loan applicants and decide whether to approve or reject the loan application. Data mining helps in effective marketing strategies. By optimizing this process, response time to home loan applications can be accelerated, operational efficiency increased, and credit risk can be better managed. In the practice of providing KPR (Home Ownership Credit) to prospective consumers, there are possible problems that will occur like most other people, namely late installment payments or defaulted payments so that it will make it difficult for the bank to maintain the level of credit risk on the credit provided, this is because Mitra Griya Indah Housing has not paid much attention to data regarding the history of credit granting decisions, in other words, it has not maximally utilized data on previous credit granting decisions in supporting credit granting decisions. To solve this problem, the researcher designed a calculation information system. In this case the author uses the waterfall method in the research process. For system design, the author uses the PHP programming language with a database format using MySql. Finally, with this information system, it can facilitate the decision-making process for prospective customers of Home Ownership Credit.