Main Article Content
Abstract
Abstrak: Telemarketing menjadi salah satu media pemasaran produk maupun jasa yang sampai saat ini masih banyak digunakan oleh berbagai perusahaan terutama di bidang perbankan, asuransi, dan telekomunikasi. Telemarketing dianggap lebih efektif dibandingkan dengan penggunaan baliho, flyer maupun brosur karena menghubungkan secara langsung antara perusahaan dengan calon pelanggan. Pada proses implementasinya, penggunaan telemarketing ini justru seringkali menemui kegagalan dan bahkan berujung pada sikap antipati calon pelanggan yang dihubungi bahkan sebelum proses komunikasi dilakukan. Salah satu penyebab terjadinya kegagalan dari proses telemarketing adalah tidak adanya proses seleksi untuk mengetahui potensi dari calon pelanggan. Machine learning menjadi salah satu alternatif yang dapat digunakan untuk membantu memilih dan menentukan calon pelanggan potensial dan sesuai dengan target perusahaan. Akan tetapi, missing values yang ada pada data telemarketing seringkali menjadi permasalahan baru yang harus ditangani agar penggunaan machine learning menjadi lebih efektif dan mendukung keberhasilah telemarketing. Pada penelitian ini, K-means digunakan sebagai salah satu cara untuk menangani adanya missing values pada data telemarketing. Hasilnya, terdapat peningkatan hingga sebesar 6% jika diukur berdasarkan nilai akurasi menggunakan model Decision Tree.
Kata Kunci: Telemarketing, Machine Learning, Missing Values, K-Means, Klasifikasi.
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References
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References
N. Dewi, R. Purnama, and R. Andari, “Pengaruh Direct Mail dan Telemarketing Terhadap Keputusan Pembelian Paket Umrah Raka Tours and Travel,” Tour. Hosp. Essentials J. (THE Journal), vol. III, no. 2, pp. 633–648, 2013.
Teemu V, “Telemarketing Data Analysis and Predictive Modelling,” no. December, 2014.
R. Vaidehi, “Predictive Modeling to Improve Success Rate of Bank Direct Marketing Campaign,” Int. J. Manag. Bus. Stud., vol. 6, no. 1, pp. 22–24, 2016.
K. C. Schneider, “Telemarketing as a promotional tool-its effects and side effects,” J. Consum. Mark., vol. 2, no. 1, p. 29, 1985.
J. Asare-Frempong and M. Jayabalan, “Predicting customer response to bank direct telemarketing campaign,” 2017 Int. Conf. Eng. Technol. Technopreneurship, ICE2T 2017, vol. 2017-Janua, pp. 1–4, 2017.
S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decis. Support Syst., vol. 62, pp. 22–31, 2014.
Q. R. Zhuang, Y. W. Yao, and O. Liu, “Application of data mining in term deposit marketing,” Lect. Notes Eng. Comput. Sci., vol. 2, pp. 14–17, 2018.
S. Palaniappan, A. Mustapha, C. F. Mohd Foozy, and R. Atan, “Customer Profiling using Classification Approach for Bank Telemarketing,” JOIV Int. J. Informatics Vis., vol. 1, no. 4–2, p. 214, 2017.
M. Purnachary, B. S. S. P. Kumar, and H. Shaziya, “Performance Analysis of Bayes Classification Algorithms in WEKA Tool using Bank Marketing Dataset,” India, vol. 5, no. 2, pp. 128–133, 2018.
L. Sing’oei and J. Wang, “Data Mining Framework for Direct Marketing: A Case Study of Bank Marketing,” IJCSI Int. J. Comput. Sci. Issues, vol. 10, no. 2, pp. 198–203, 2013.
Y. Pristyanto, I. Pratama, and A. F. Nugraha, “Data level approach for imbalanced class handling on educational data mining multiclass classification,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, pp. 310–314.
L. Rahman, N. A. Setiawan, and A. E. Permanasari, “Feature Selection Methods in Improving Accuracy of Classifying Students’ Academic Performance,” 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. (ICITISEE)., no. 1, pp. 267–271, 2017.
M. Albayrak, K. Turhan, and B. Kurt, “A missing data imputation approach using clustering and maximum likelihood estimation,” in 2017 Medical Technologies National Congress (TIPTEKNO), 2017, pp. 1–4.
Y. Pristyanto and I. Pratama, “Missing Values Estimation on Multivariate Dataset : Comparison of Three Type Methods Approach,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019, pp. 342–347.
S. Hua-Yan, L. Ye-Li, Z. Yun-Fei, and H. Xu, “Accelerating EM Missing Data Filling Algorithm Based on the K-Means,” in 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC), 2018, pp. 401–406.
U. Machine Learning, “UCI Machine Learning Repository: Bank Marketing Dataset,” 2019. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/bank+marketing.
J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,” J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.
Kusrini and E. T. Luthfi, Algoritma Data Mining. Penerbit Andi, 2009.
S. Riama and Chafid, “Algorithma K-Means Clustering Strategi Pemasaran Penerimaan Mahasisswa Baru Universitas Satya Negara Indondesia [Algorithma K-Means Clustering Strategy Marketing Admission Universitas Satya Negara Indonesia],” Semin. Nas. Cendekiawan ke 4, no. 2, pp. 685–690, 2018.
A. F. Nugraha and L. Rahman, “Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process,” in 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2019, pp. 271–275.