Abstract— The increasingly complex business environment
necessitates businesses to design more effective and efficient
strategies for company development, including market expansion.
To understand customer behaviors, customer data analysis
becomes crucial. One common approach used to group customers
is segmentation based on RFM analysis (Recency, Frequency, and
Monetary). This study aims to compare the performance of two
clustering algorithms, namely DBSCAN and Affinity Propagation
(AP), in providing customer profile segment recommendations
using RFM analysis. DBSCAN algorithm is employed due to its
ability to identify arbitrarily shaped clusters and handle data
noise. On the other hand, Affinity Propagation (AP) algorithm is
chosen for its capability to discover cluster centers without
requiring a pre-defined number of clusters. The transaction
dataset used in this research is obtained from one of the business
incubator tenants at STIKOM Bali. The dataset undergoes
preprocessing steps before being segmented using both DBSCAN
and AP algorithms. Performance evaluation of the algorithms is
conducted using the Silhouette Scores and Davies-Bouldin Index
(DBI) matrices. The research findings indicate that the AP
algorithm outperforms DBSCAN in this customer segmentation
case. The AP algorithm yields Silhouette Scores of 0.699 and DBI
of 0.429, along with recommendations for 4 customer segments.
Furthermore, further analysis is performed on the AP results
using a statistical approach based on the mean values of each
segment for the RFM variables. The four customer segments
generated by the AP algorithm, based on the mean values of the
RFM variables, can be associated with the concept of customer
relationship management.