PENERAPAN ALGORITMA GENETIKA DAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK OPTIMASI K-MEANS PADA PENGELOMPOKAN PENGGUNA SHOPEE.

Annisya Safa Kusyanti, . (2024) PENERAPAN ALGORITMA GENETIKA DAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK OPTIMASI K-MEANS PADA PENGELOMPOKAN PENGGUNA SHOPEE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Shopee is one of the most widely used electronic shopping channels in Indonesia. In promoting their products, businesses on the Shopee platform can use clustering on Shopee users to obtain their target market. The most well-known clustering algorithm is K-Means. However, K-Means has a disadvantage in that it is susceptible to being trapped in local optima caused by the random initialization of cluster center points. Therefore, researchers have conducted clustering on Shopee user data using the K-Means algorithm and Genetic and PSO optimization algorithms to address this K-Means limitation. The data used by the researchers had demographic and user behavior variable categories, obtained from a survey with a sample size determined by the Slovin method. The researchers created three clustering models, namely standard K-Means, K-Means with Genetic Algorithm (GA-KMeans), and K-Means with PSO (PSO-KMeans), which were then evaluated using the Silhouette Coefficient (SC). The research results prove that both GA-KMeans and PSO-KMeans can optimize K-Means. With several clusters k = 3, GA-KMeans obtained an SC value of 0.4281 and PSO-KMeans obtained an SC value of 0.4075. Meanwhile, the SC value of standard K-Means was 0.2725.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1910511057] [Pembimbing 1: Iin Ernawati] [Pembimbing 2: Ika Nurlaili Isnainiyah] [Penguji 1: Didit Widiyanto] [Penguji 2: Kraugusteeliana]
Uncontrolled Keywords: clustering, K-Means, Genetic Algorithm, PSO, Shopee users
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Annisya Safa Kusyanti
Date Deposited: 20 Feb 2024 06:41
Last Modified: 20 Feb 2024 06:43
URI: http://repository.upnvj.ac.id/id/eprint/29157

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