FAHRI RITONGA, NPM 2109100029 (2025) IMPLEMENTASI ALGORITMA K-MEANS PADA TINGKAT MINAT BELANJA MELALUI ONLINE SHOPE PADA MASYARAKAT NEGERI LAMA. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Perkembangan teknologi informasi telah mendorong perubahan pola konsumsi masyarakat menuju belanja daring (online shop). Penelitian ini bertujuan untuk menganalisis tingkat minat belanja masyarakat Negeri Lama melalui platform Shopee dengan menerapkan algoritma K-Means sebagai metode klasterisasi. Data diperoleh dari hasil kuesioner yang mencakup atribut seperti jenis kelamin, usia, kemudahan akses, tampilan antarmuka, metode pembayaran, dan program promosi. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, transformasi data, dan pengelompokan ke dalam tiga klaster: minat belanja tinggi, sedang, dan rendah. Proses pengolahan data dilakukan secara manual dan menggunakan RapidMiner untuk validasi hasil. Hasil klasterisasi menunjukkan bahwa preferensi belanja masyarakat dipengaruhi oleh kemudahan akses, tampilan antarmuka aplikasi, dan promosi yang ditawarkan. Temuan ini memberikan kontribusi praktis bagi pelaku e-commerce dalam merancang strategi pemasaran yang tepat sasaran berdasarkan karakteristik konsumen masing-masing klaster. Dengan demikian, penerapan algoritma K-Means terbukti efektif dalam memahami pola perilaku belanja daring masyarakat dan dapat digunakan sebagai dasar dalam pengambilan keputusan strategis. Kata Kunci : K-Means, Minat Belanja, Toko Online, Shopee, Klasterisasi, e Commerce =================================================================================================== The advancement of information technology has transformed consumer behavior, with a growing shift toward online shopping. This study aims to analyze the shopping interest levels of residents in Negeri Lama through the Shopee platform by implementing the K-Means algorithm as a clustering method. Data were collected through questionnaires covering attributes such as gender, age, ease of access, user interface, payment methods, and promotional programs. The research process includes data collection, preprocessing, data transformation, and grouping into three clusters: high, moderate, and low shopping interest. Data processing was carried out manually and validated using RapidMiner. The clustering results indicate that shopping preferences are influenced by ease of access, the application's user interface, and promotional offers. These findings provide practical insights for e-commerce businesses to design targeted marketing strategies based on the characteristics of each consumer cluster. Thus, the implementation of the K-Means algorithm proves effective in understanding online shopping behavior and can serve as a basis for strategic decision-making. Keywords: K-Means, Shopping Interest, Online Shop, Shopee, Clustering, e Commerce
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | K-Means, Minat Belanja, Toko Online, Shopee, Klasterisasi, e Commerce====================K-Means, Shopping Interest, Online Shop, Shopee, Clustering, e Commerce |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 14 Jul 2025 03:07 |
| Last Modified: | 14 Jul 2025 03:07 |
| URI: | http://repository.ulb.ac.id/id/eprint/1569 |
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