@thesis{thesis,
		author={Chamzah  Sultan Maula},
		title ={PENERAPAN SMOTE UNTUK MENANGANI IMBALANCE CLASS DATA PADA REVIEW APLIKASI TOKOPEDIA MENGGUNAKAN ALGORITMA KNN},
		year={2022},
		url={https://eprints.umm.ac.id/97591/},
		abstract={Marketplace is a type of e-commerce website where product or service information is provided by many third parties, one example is Tokopedia. Tokopedia gets an average number of website and application visitors 147.79 million per month. Even though it has many users, of course an application has advantages and disadvantages. This was conveyed by users through reviews or reviews found on the Google Play Store. In this review, it can be seen that more users gave 5 star reviews than users gave a 1 star rating. The Synthetic Minority Oversampling Technique or SMOTE is a popular method used to deal with class imbalance. This study aims to determine the performance of the K-Nearest Neighbor algorithm in handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). This study uses 5000 data consisting of 3975 negative data and 1025 positive data. Of the 5000 data divided into two parts, 70% training data and 30% test data. The purpose of this study is to determine the performance of handling class imbalances in the data review of the Tokopedia application using the KNN and SMOTE algorithms. The SMOTE-kNN method shows better accuracy of 90% compared to only using kNN with an accuracy value of 82%.}
		}