ANALYSIS OF ONLINE STORE CONSUMER BEHAVIOUR WITH K-MEANS AND AGGLOMERATIVE CLUSTERING ALGORITHM

WIJAYA, VENANSIUS FORTUNATUS (2023) ANALYSIS OF ONLINE STORE CONSUMER BEHAVIOUR WITH K-MEANS AND AGGLOMERATIVE CLUSTERING ALGORITHM. Skripsi thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.

Preview	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_COVER_1.pdf Download (111kB) | Preview
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_BAB I_1.pdf Restricted to Registered users only Download (86kB)
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_BAB II_1.pdf Restricted to Registered users only Download (87kB)
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_BAB III_1.pdf Restricted to Registered users only Download (238kB)
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_BAB IV_1.pdf Restricted to Registered users only Download (853kB)
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_BAB V_1.pdf Restricted to Registered users only Download (84kB)
Preview	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_DAPUS_1.pdf Download (146kB) | Preview
	Text 20.K1.0001-VENANSIUS FORTUNATUS WIJAYA_LAMPIRAN_1.pdf Restricted to Registered users only Download (430kB)

Abstract

An online store or e-commerce is a business system that allows buyers and sellers to conduct electronic transactions over the internet. Along with the increasing use of the internet around the world, the e-commerce business is growing rapidly and becoming a very large industry. To find out consumer behaviour, data analysis can be done using clustering algorithms, namely K-Means compared with Agglomerative Clustering and K-Means compared with KMeans++. Through these three algorithms, the data is reduced in dimension by PCA and t-SNE methods. It is then also analysed using the attributes provided by the dataset to further determine its performance measures. The parameters that will be used for clustering are K values 4, 5, and 6 of K-Means algorithm compared to Agglomerative Clustering and the optimal K value of 4 with the initialised centroid center value for K-Means++. Then determining the distance between data using the Euclidean Distance method, while for data grouping using the Average Linkage method in the Agglomerative Clustering algorithm. Through the method, the results are Agglomerative with a K value of 4 and the best t-SNE data type because the K value is good and the data type used is very good so that the results are also good and K-Means++ with an optimal K value of 4 and the best t-SNE data type because the centroid value is well initialised so that this algorithm speeds up the clustering process rather than the usual K-Means which takes longer in the process of clustering online store consumer behaviour data.

Item Type:	Thesis (Skripsi)
Subjects:	000 Computer Science, Information and General Works
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	16 Apr 2024 02:16
Last Modified:	16 Apr 2024 02:16
URI:	http://repository.unika.ac.id/id/eprint/35190

Actions (login required)

View Item