Implementasi Sistem WhatsApp Blast Berbasis Web dengan Fitur Segmentasi Calon Mahasiswa Menggunakan K-Means Clustering

Dilo Elvonsyah (Universitas Pembangunan Jaya, Banten)
Denny Ganjar Purnama (Universitas Pembangunan Jaya, Banten)

TIN: TERAPAN INFORMATIKA NUSANTARA
Vol 7 No 5 (2026): October 2026

The delivery of prospective student admission information through WhatsApp requires structured contact management and target-group selection. This study develops a web-based WhatsApp Blast system with prospective student segmentation using K-Means Clustering. The system uses React and Next.js, Node.js and Express, PostgreSQL, a Python preprocessing and clustering service, and REST APIs. The clustering variables are study-program interest, school of origin, school major, and domicile; names and WhatsApp numbers are retained only as identity attributes. Actual testing used 120 controlled synthetic records. The system stored 113 valid records, detected 3 duplicates and 4 invalid records, and recorded one missing value in each clustering attribute. All missing values were imputed as Tidak Diketahui, so all 113 valid records entered clustering with no exclusion. ILKOM was normalized to Ilmu Komunikasi, and One-Hot Encoding produced 35 features. Evaluation for K=2–6 selected K=5 through agreement among Elbow, Silhouette, and Davies-Bouldin. The K-Means run produced an SSE of 300.1250, a Silhouette Score of 0.1110, a Davies-Bouldin Index of 2.6207, and cluster sizes of 30, 27, 20, 19, and 17. All thirteen executed Black Box scenarios passed. These results validate the implementation on synthetic data and do not generalize the prospective-student population.