Implementasi Sistem WhatsApp Blast Berbasis Web dengan Fitur Segmentasi Calon Mahasiswa Menggunakan K-Means Clustering
-
Dilo Elvonsyah
*
Universitas Pembangunan Jaya, Banten,
Indonesia
- Denny Ganjar Purnama Universitas Pembangunan Jaya, Banten, Indonesia (*) Corresponding Author
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.
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References
Akande, O. N., Asani, E. O., & Dautare, B. T. (2024). Customer segmentation through RFM analysis and K-means clustering: Leveraging data-driven insights for effective marketing strategy. Ceddi Journal of Information System and Technology (JST), 3(1), 14–25. https://doi.org/10.56134/jst.v3i1.81
Aslantaş, G., Gençgül, M., Rumelli, M., Özsaraç, M., & Bakırlı, G. (2023). Customer segmentation using K-Means clustering algorithm and RFM model. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 25(74), 491–503. https://doi.org/10.21205/deufmd.2023257418
Astuti, J., & Yuniarti, T. (2023). Data mining modeling in clustering car products sales data in the automotive industry in Indonesia. Jurnal Manajemen Industri dan Logistik, 7(2), 261–281. https://doi.org/10.30988/jmil.v7i2.1258
Cendana, M., & Kuo, R.-J. (2024). Categorical data clustering: A bibliometric analysis and taxonomy. Machine Learning and Knowledge Extraction, 6(2), 1009–1054. https://doi.org/10.3390/make6020047
Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
Djun, S. F., Gunadi, I. G. A., & Sariyasa, S. (2024). Analisis segmentasi pelanggan pada bisnis dengan menggunakan metode K-Means clustering pada model data RFM. JTIM: Jurnal Teknologi Informasi dan Multimedia, 5(4), 354–364. https://doi.org/10.35746/jtim.v5i4.434
Elsa, M. T., Berutu, S. S., & Maedjaja, F. (2024). Analisis perbincangan dalam grup WhatsApp dengan K-Means clustering. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 9(4), 1960–1971. https://doi.org/10.29100/jipi.v9i4.5554
Fauzan, R. M., & Alfian, G. (2024). Segmentasi pelanggan e-commerce menggunakan fitur recency, frequency, monetary (RFM) dan algoritma klasterisasi K-Means. JISKA (Jurnal Informatika Sunan Kalijaga), 9(3), 170–177. https://doi.org/10.14421/jiska.2024.9.3.170-177
Fazri, A. H., Muhammad, A., & Purnama, P. A. W. (2023). Algoritma K-Means cluster untuk segmentasi pelanggan. Computer Based Information System Journal, 11(2), 42–51. https://doi.org/10.33884/cbis.v11i2.7156
Hadinata, W., & Stianingsih, L. (2024). Analisis perbandingan performa RESTful API antara Express.js dengan Laravel framework. Jurnal Informatika dan Teknik Elektro Terapan, 12(1), 531–540. https://doi.org/10.23960/jitet.v12i1.3845
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/j.ins.2022.11.139
John, J. M., Shobayo, O., & Ogunleye, B. (2023). An exploration of clustering algorithms for customer segmentation in the UK retail market. Analytics, 2(4), 809–823. https://doi.org/10.3390/analytics2040042
Kumala, N., & Dana, R. D. (2023). Penerapan algoritma K-Means clustering berdasarkan hasil penjualan produk Inez Cosmetic di Toko Rosalia. E-Link: Jurnal Teknik Elektro dan Informatika, 18(1), 50–56. https://doi.org/10.30587/e-link.v18i1.5343
Maspupah, A. (2024). Literature review: Advantages and disadvantages of black box and white box testing methods. Jurnal Techno Nusa Mandiri, 21(2), 151–162. https://doi.org/10.33480/techno.v21i2.5776
Nugraha, M., Sakinah, L., Setiawan, R. A., & Mulyani, H. (2024). Rancang bangun sistem informasi penerimaan mahasiswa baru berbasis web dengan menggunakan framework Laravel. Jurnal Informatika dan Teknik Elektro Terapan, 12(2), 1162–1169. https://doi.org/10.23960/jitet.v12i2.4179
Prastyabudi, W. A., Alifah, A. N., & Nurdin, A. (2024). Segmenting the higher education market: An analysis of admissions data using K-Means clustering. Procedia Computer Science, 234, 96–105. https://doi.org/10.1016/j.procs.2024.02.156
Rahmawati, D. P., Hidayati, S., Arismawati, P., & Johan, A. W. S. B. (2024). Prospective new college student dashboard: Insights from K-Means clustering with principal component analysis. Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, 9(2), 137–144. https://doi.org/10.25139/inform.v9i2.8462
Riady, A. M. N., Paniran, P., & Suksmadana, I. M. B. (2024). Perancangan backend API berbasis REST-API pada aplikasi rekomendasi resep makanan. Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer, 2(3), 94–106. https://doi.org/10.61132/mars.v2i3.137
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Sari, S. P., & Putri, R. A. (2023). Analisis dan visualisasi data penjualan menggunakan exploratory data analysis dan K-Means clustering. Jurnal Sistem Komputer dan Informatika (JSON), 5(2), 423–433. https://doi.org/10.30865/json.v5i2.7180
Silaban, R., Fauzi, A., & Kadim, L. A. N. (2024). Penerapan K-Means clustering untuk menentukan lokasi promosi penerimaan mahasiswa baru: Studi kasus STMIK Kaputama Kota Binjai. Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika, 2(5), 310–318. https://doi.org/10.61132/merkurius.v2i5.322
Syahra, Y., Fadlil, A., & Yuliansyah, H. (2025). Customer segmentation using RFM and K-Means clustering to support CRM in retail industry. Sinkron: Jurnal dan Penelitian Teknik Informatika, 9(3), 1120–1131. https://doi.org/10.33395/sinkron.v9i3.14907
Yunita, I., Ali, P. R., Kartawidjaja, M. A., & Sukwadi, R. (2025). Segmentasi pelanggan menggunakan K-Means clustering: Menganalisis metrik RFM untuk strategi pemasaran. Jurnal Media Teknik dan Sistem Industri, 9(1), 58–66. https://doi.org/10.35194/jmtsi.v9i1.4452
Zen, M., Irwan, I., Hafni, H., & Ananda, M. D. P. (2024). Implementasi dan pengujian menggunakan metode BlackBox testing pada sistem informasi tracer study. Bulletin of Computer Science Research, 4(4), 327–340. https://doi.org/10.47065/bulletincsr.v4i4.359
Zheng, Y. (2023). Customer segmentation research in marketing through clustering algorithm analysis. Journal of Intelligent & Fuzzy Systems, 45(4), 5441–5448. https://doi.org/10.3233/JIFS-232589
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