Penerapan Data Mining Menggunakan Metode Algoritma Naive Bayes Untuk Menentukan Kelayakan Kredit Rumah Bersubsidi - Muhammad Makmun Effendi (Jurnal SIGMA, 2020)
Data mining has been implemented in various fields, including business, education and telecommunications. In the business sector, for example, the results of implementing data mining can help in making decisions about the feasibility of subsidized home loans. In determining the feasibility of subsidized home loans, PT. Gernis Pratama Properti conducts an analysis so that it can be determined whether the subsidized home loan process can be approved or not. Currently there are several obstacles in the assessment process, namely the inaccurate results of the decision at interview stage 1 in the company as an initial stage of the consumer eligibility process. Naive Bayes Algorithm Method is an algorithm found in the classification technique that uses a simple probability method based on the theory of infants with high independent assumptions. The process carried out in this study uses Rapid Miner tools to process data with the Naive Bayes algorithm, from the tests carried out it produces an accuracy of 96.23%. With the application of the Naive Bayes method, it uses data to produce the probability of each criterion for different classes, so that the probability values of these criteria can be optimized to determine the eligibility of "Eligible" and "Eligible" subsidized home loans quickly and efficiently based on the classification made by Naive Bayes method. Keyword : Creditworthiness of subsidies ,Data Mining, Algorithm Naive Bayes.