PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS UNTUK ANALISIS DATA BELANJA ONLINE MAHASISWA - Deiva Verlyn Marjuki; Mutyara Safitri; Harun Al Rosyid (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram, 2026)
This study was conducted to analyze the online shopping behavior of college students using the K-Means algorithm as a clustering technique in data mining. This study was motivated by the lack of systematic segmentation of student shopping behavior, which limits the understanding of purchasing characteristics within this consumer group. Unlike previous studies that mostly examine general retail customers or broad e-commerce users, this study specifically focuses on university students by integrating demographic and behavioral attributes. The originality of this study is reflected in the simultaneous use of six variables, namely gender, shopping time, product type, expenditure level, payment method, and purchase decision factors. Data were collected through an online survey involving 200 active college students. The research stages consisted of data cleaning, data category transformation using One-Hot Encoding, clustering model construction using the K-Means algorithm, and cluster evaluation using the Silhouette method. The evaluation results showed that the optimal number of clusters was k = 3, achieving the of 0.0913. Three distinct segments of college students' online shopping behavior were identified, providing insights that can support more targeted marketing strategies and student-oriented e-commerce services.