Penerapan Algoritma Naive Bayes dan Multiple Linear Regression Untuk Prediksi Status dan Plafon Kredit (Studi Kasus: Bank ABC) - Arya Prawira; Desi Arisandi; Tri Sutrisno (Journal on Education, 2022)
Along with changing technology, Human resources are still needed in many parts of decision making. The companies and organizations still use human to analysis data. Despite of that performance, human analysis took longer time and effort to complete. And sometimes there is always a negative factor of human resources such as unmanageable human. Therefore, it’s always important to provide an excellent training source so this human resource able to reach an outcome that needed. Machine learning is one of the most common knowledge that use in decision making. There are many forms of machine learning such as regression, classification, clustering, etc. two of which is used in this application, regression and classification. Naive Bayes regression is one of classification method which rooted on Bayes theorem. Naïve Bayes use historical data to predict future outcome based on the characteristic on that historical data. Multiple Linear Regression involves more than one independent variable or predictor. With machine learning and human resources, man can easily to analyse credit worthiness and determine the credit limit of one bank costumer without taking a long time and much effort.