Pengujian Algoritma Clustering Affinity Propagation dan Adaptive Affinity Propagation terhadap IPK dan Jarak Rumah - Millati Izzatillah; Achmad Benny Mutiara (STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 2020)
Clustering which is a method to classify data easily is used for a purpose of looking at the correlation among data attributes. Clustering is also a data point grouping based on similarity value to determine the cluster center. Affinity Propagation (AP) and Adaptive Affinity Propagation (Adaptive AP) are clustering algorithms that produce number of cluster, cluster members and exemplar of each cluster. This research is conducted to find out a more effective algorithm when clustering data. Besides, to know the correction offered by Adaptive AP Algorithm which is the developed form of AP Algorithm, the researcher implemented and tested both algorithms by using Matlab R2013a 8.10 with 250 data taken from students’ GPA and the distance from their houses to campus. The analysis of test result application from both algorithms shows that the best algorithm is Adaptive AP because it produces optimal clustering. Another result is no correlation between GPA and home distance.