Probabilitas Pembayaran Kredit Tepat Waktu Menggunakan Algoritma Naive Bayes Pada Koperasi Serba Usaha Daruzzakah Rensing Lombok Timur
DOI:
https://doi.org/10.29408/jit.v4i2.3573Keywords:
Naïve Bayes Algorithm, Alternative, Data Mining, Cooperative, Rentenir.Abstract
In general, the notion of a cooperative is a business entity that is owned and managed by its members. Meanwhile, multi-business cooperatives are cooperatives whose business activities are in various economic aspects such as savings and loans, production, consumption and services, which consist of people or cooperative legal entities by basing their business activities on the cooperative principle as well as a people's economic movement based on the principle of kinship. This research took place in one of the cooperatives in the village of Rensing, East Lombok, with the cooperative name "Daruzzakah". This cooperative is a multi-business cooperative with one type of activity is to provide savings and loans or credit to its members. The purpose of this cooperative is as an alternative means of borrowing money or credit as well as trying to prevent its members from loan sharks. However, in practice there are problems, namely the number of delays and credit payments that are not on time. Judging from the large number of customers who borrow funds, a strategy is needed to be able to fulfill all of these activities, the increasing number of prospective customers applying for credit with different economic conditions, requiring accuracy in making credit decisions. To avoid this, it should be necessary to analyze member data to determine the feasibility of providing credit, so that it can be classified as whether or not to get a loan. Data analysis can be done using data mining techniques. For this reason, the authors try to provide solutions to these problems by applying the naïve Bayes algorithm in predicting and determining creditworthiness. The Naive Bayes algorithm has been widely used by previous researchers and has high accuracy values. In this study, the Naive Bayes algorithm was used and resulted in an accuracy value of 96.45% with an AUC value of 0.942 which means it is a good classification.
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