ANALISIS CLUSTER DENGAN MENGGUNAKAN K-MEANS UNTUK PENGELOMPOKKAN ONLINE CUSTOMER REVIEWS (OCR) PADA ONLINE MARKETPLACE - Rena Nainggolan; Fenina A.T Tobing (Universitas Methodist Indonesia, 2020)
Technological advances at this time are very influential on people's shopping culture, plus during the current pandemic, it has resulted in an increasing number of people shopping for daily necessities online. There are many conveniences offered in online shopping that make people switch to using these facilities. Besides the advantages of online shopping, there are also some disadvantages of online shopping, including the rise of online sales fraud such as goods not being shipped, damaged goods, items not as ordered, and much more. For this reason, in conducting online transactions, trust is needed between the seller and the buyer, and one of the factors that greatly affect the prospective buyer is to know the history of the seller, namely by looking at the reviews given by the buyer on the seller's homepage which is called Online Customers Reviews (OCR). OCR is considered to be very influential on customer buying interest. One of the indicators that are considered very important in influencing consumer buying interest and trust is OCR. This study aims to analyze OCR clustering in one of the marketplaces in Indonesia using the K-Means Clustering Method. K-Means is a clustering algorithm that is quite effective because it has the ability to group large amounts of data and with high speed, the K-Means algorithm partitions data into clusters so that they have the similarity of being in one cluster.