TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING

  • Siti Masturoh (1*) STMIK Nusa Mandiri
  • Fitra Septia Nugraha (2)
  • Siti Nurlela (3)
  • M. Rangga Ramadhan Saelan (4)
  • Daniati Uki Eka Saputri (5)
  • Ridan Nurfalah (6)

  • (*) Corresponding Author
Keywords: Resampling, Multilayer Perceptron (MLP); Resampling, Telemarketing

Abstract

Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques.  Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values.

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References

Amrin, A., & Satriadi, I. (2018). Implementasi Jaringan Syaraf Tiruan Dengan Multilayer Perceptron Untuk Analisa Pemberian Kredit. Jurnal Riset Komputer (JURIKOM), 5(6), 605–610.

Dewi, S. (2016). Komparasi 5 Metode Algoritma Klasifikasi Data Mining Pada Prediksi Keberhasilan Pemasaran Produk Layanan Perbankan. None, 13(1), 60–66.

Fauzi, A., Wati, F. F., Sulistyowati, I., Akbar, M. F., Rahmawati, E., & Sari, R. K. (2020). Penerapan Metode Machine Learning Dalam Memprediksi Keberhasilan Panggilan Telemarketing Menjual Produk Bank. 6(2), 213–222.

Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S. (2019). An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing, 23(17), 7941–7958. https://doi.org/10.1007/s00500-018-3424-2

Masturoh, S., Nugraha, F. S., Nurlela, S., Saelan, M. R. R., & Saputri, D. U. E. (2021). PREDIKSI KEBERHASILAN TELEMARKETING BANK MENGGUNAKAN ALGORITMA MULTILAYER PERCEPTRON (MLP) DENGAN RESAMPLING.

Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31. https://doi.org/10.1016/j.dss.2014.03.001

Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN. Computer Engineering, Science and System Journal, 4(1), 78. https://doi.org/10.24114/cess.v4i1.11458

Pratiwi, P. G., Putra, I. K. G. D., & Putri, D. P. S. (2019). Peramalan Jumlah Tersangka Penyalahgunaan Narkoba Menggunakan Metode Multilayer Perceptron. Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 7(2), 143. https://doi.org/10.24843/jim.2019.v07.i02.p06

Pujianto, U. (2016). Strategi Resampling Berbasis Centroid Untuk Menangani Lunak. Teknno, 25(Maret), 1–6.

Purbaya, M. E., Nugraha, A. F., Gustina, S., & Azis, M. K. (2020). Meta-Algorithms untuk Meningkatkan Kinerja Klasifikasi dalam Keberhasilan Telemarketing Perbankan. Techno.Com, 19(4), 385–396. https://doi.org/10.33633/tc.v19i4.3725

Purnama, N., Putra, I. K. G. D., & Bayupati, P. A. (2014). Klasifikasi Website Menggunakan Algoritma Multilayer Perceptron. Teknologi Elektro, 13(2), 9–15.

Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y., & Ettaouil, M. (2016). Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 26. https://doi.org/10.9781/ijimai.2016.415

Saputra, E. P. (2017). Prediksi Keberhasilan Telemarketing Bank Untuk. Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, 2(2), 66–72.

Shelke, M. S., Deshmukh, P. R., & Shandilya, P. V. K. (2017). A Review on Imbalanced Data Handling Using Undersampling and Oversampling Technique. International Journal of Recent Trends in Engineering and Research, 3(4), 444–449. https://doi.org/10.23883/ijrter.2017.3168.0uwxm

Sulaehani, R. (2016). Prediksi Keputusan Klien Telemarketing Untuk Deposito Pada Bank Menggunakan Algoritma Naive Bayes Berbasis Backward Elimination. ILKOM Jurnal Ilmiah, 8(3), 182–189. https://doi.org/10.33096/ilkom.v8i3.83.182-189

Vajiramedhin, C., & Suebsing, A. (2014). Feature selection with data balancing for prediction of bank telemarketing. Applied Mathematical Sciences, 8(113–116), 5667–5672. https://doi.org/10.12988/ams.2014.47222

Published
2021-03-02
How to Cite
Masturoh, S., Nugraha, F., Nurlela, S., Saelan, M. R., Saputri, D., & Nurfalah, R. (2021). TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING. Pilar Nusa Mandiri: Journal of Computing and Information System, 17(1), 19-24. https://doi.org/10.33480/pilar.v17i1.2168
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