Pengelompokan Harga Ponsel Pintar berdasarkan Spesifikasi menggunakan Metode K-Means Clustering - Ahmad Agung Zefi Syahputra; Annisa Dwi Atika; Muhammad Adam Aslamsyah; Meida Cahyo Untoro; Winda Yulita (Institute of Computer Science (IOCS), 2021)
The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.