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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Denny Ganjar Purnama
Universitas Pembangunan Jaya, Banten
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Implementasi Sistem WhatsApp Blast Berbasis Web dengan Fitur Segmentasi Calon Mahasiswa Menggunakan K-Means Clustering Dilo Elvonsyah; Denny Ganjar Purnama
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11246

Abstract

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.
Implementasi Contextual Multi-Armed Bandit dengan Algoritma LinUCB untuk Rekomendasi Template pada Sistem Kampanye WhatsApp Berbasis Web Reigan Chenartha; Denny Ganjar Purnama
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11248

Abstract

Uniform WhatsApp campaign templates may overlook differences in timing and audience characteristics. This study develops an adaptive and auditable template recommendation module for a web-based campaign system. Design Science Research Methodology guides problem identification, objective definition, artifact development, demonstration, and evaluation. The disjoint Linear Upper Confidence Bound (LinUCB) algorithm treats Informative, Persuasive, and Urgency templates as three available actions. A 19-dimensional context vector encodes day, time bucket, audience, and a bias term. Template selection maximizes a linear reward estimate plus an exploration bonus, while the selected action is updated using delivered, read, and replied rewards weighted at 0.2, 0.3, and 0.5. Synthetic evaluation comprises 30 trials of 100 contexts each, with an exploration parameter of 0.3 and parent seed 1790543727863. LinUCB achieves a mean total reward of 39.520 with a standard deviation of 0.631, compared with 36.800 and 1.790 for the context-blind baseline. The mean improvement is 7.39%, with a mean regret of 2.769. The contribution is an integrated context–decision–feedback workflow with decision logging, operator control, and repeated evaluation isolated from operational data. These findings support artifact feasibility in the synthetic environment; improvements in real recipient responses require field validation.
Co-Authors Dilo Elvonsyah Reigan Chenartha