Prediction of Customer Churn in the Banking Industry Using Artificial Neural Networks Prediction of Customer Churn in the Banking Industry Using Artificial Neural Networks
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Published:
May 31, 2020
Keywords:
Artificial Neural Network, Churn Prediction, Deep Learning, Machine Learning, Hard, TensorFlow
Section
Computer Science
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Article View : 661 Times
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Abstract
The Artificial Neural Network (ANN) is an architect in deep learning inspired by how the human brain works. As the name suggests, this technique works like a biological neural network possessed by a living creature, by accepting input data, processing it in a node/neuron, and displaying it in the output. ANN works by utilizing the large number of layers and the number of existing nodes/neurons to perform tasks, such as performing feature extraction, pattern recognition, regression, and classification. In this journal ANN's architect was used in the design of deep learning model, to solve classification problem, in case of banking churn prediction. The number of layers (layers) used by 4 layers, namely input layer, 2 hidden layer, and output layer with the number of nodes/neurons in sequence as much as 10.6, 6, 1. The final result is a model that is already (ready) deployed into a web-based application that can predict the churn (banking) with an accuracy of 84% on the customer's input data
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How to Cite
Satria, W. A., Fitri, I. and Ningsih, S. (2020) “Prediction of Customer Churn in the Banking Industry Using Artificial Neural Networks: Prediction of Customer Churn in the Banking Industry Using Artificial Neural Networks”, Jurnal Mantik, 4(1), pp. 936-943. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/871 (Accessed: 29July2026).
References
[1]. Ahmad, A. (2017). Mengenal Artificial Intelligence, Machine Learning, Neural Network, dan Deep Learning. October.
[2]. Arifin, M. (2015). Ig-Knn Untuk Prediksi Customer Churn Telekomunikasi. Simetris : Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 6(1), 1.
[3]. Bandi, R., & Amudhavel, J. (2018). Object recognition using Keras with backend tensor flow. International Journal of Engineering and Technology(UAE), 7(3.6 Special Issue 6), 229–233.
[4]. Buslim, N. (2019). Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat. Jurnal Teknik Informatika, 12(1), 79–96.
[5]. Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Peter Campbell, J. (2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science and Technology, 9(2), 1–12.
[6]. Goldsborough, P. (2016). A Tour of TensorFlow. October.
[7]. Gullo, F. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62, 18–22.
[8]. Jan, P., & Gotama, W. (2018). Pengenalan Pembelajaran Mesin dan Deep Learning. 2019, July, 1–199.
[9]. Kumar, V., & L., M. (2018). Deep Learning as a Frontier of Machine Learning: A Review. International Journal of Computer Applications, 182(1), 22–30.
[10]. Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1). https://doi.org/10.3390/risks7010029
[11]. Muhammad, I., & Yan, Z. (2015). Supervised Machine Learning Approaches: a Survey. ICTACT Journal on Soft Computing, 05(03), 946–952.
[12]. Pertiwi, R. W., & Widiyanto, I. (2015). Minat Churn Pelanggan Indosat Di Indonesia. 4, 1–13.
[13]. Rachmat, A. (2019). Survei Penerapan Model Machine Learning Dalam Bidang Keamanan Informasi. Jurnal Sistem Cerdas, 2(1), 47–60.
[14]. Ritonga, A. S., & Atmojo, S. (2018). Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra). Jurnal Ilmiah Teknologi Informasi Asia, 12(1), 15.
[15]. Vogt, M. (2019). An Overview of Deep Learning and Its Applications. January, 178–202. https://doi.org/10.1007/978-3-658-23751-6_17
Ndruru, T., & Riandari, F. (2019). Decision Support System Feasibility Lending At KSU Mitra Karya Cooperative Customer Unit XXVIII with Analytical Hierarchy Process Method. Jurnal Mantik, 3(3, Nov), 119-125. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/335
Sibagariang, R., & Riandari, F. (2019). Decision Support System for Determining the Best Wood For the Production Cabinet Using Bayes Method. Jurnal Mantik, 3(3, Nov), 99-103. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/327
Pamungkas, A., & Riandari, F. (2019). Analysis of Disease in Plants Guava Demspter Shafer Method Using Web Based in the village of Paradise Sei Rampah. Jurnal Mantik, 3(3, Nov), 69-72. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/318
Ridandari, F., & Panjaitan, A. (2019). Expert System to Diagnose Extra Lung Tuberculosis Using Bayes Theorem. Jurnal Mantik, 3(3, Nov), 34-39. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/285
Situmorang, E., & Riandari, F. (2019). Decision Support System For Selection Of The Best Doctors In Sari Mutiara Hospital Using Fuzzy Tsukamoto Method. Jurnal Mantik, 3(3, Nov), 28-33. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/265
Anggraini, D., & Sihotang, H. (2019). Decision Support System For Choosing The Best Class Guardian With Simple Additive Weighting Method. Jurnal Mantik, 3(3, Nov), 1-9. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/278
Puspa, M., & Sihotang, H. (2019). Decision Support System For Supplementary Food Recipients (PMT) By Using The Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 19-27. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/280
Purba, E., & Sihotang, H. (2019). Decision Support System For Prospective Recipients Of The Healthy Indonesia Card (Kis) In The Village Of Bah Sidua Dua With The Analytical Hierarchy Process (AHP) Method. Jurnal Mantik, 3(3, Nov), 82-90. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/320
Purba, R., & Sihotang, H. (2019). Decision Support Systems Recipient Program Keluarga Harapan (PKH) In Durian Kec.Pantai Labu Kab. Deli Serdang with the Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 91-98. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/325
Devi, S., & Sihotang, H. (2019). Decision Support Systems Assessment of the best village in Perbaungan sub-district with the Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 112-118. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/334
[2]. Arifin, M. (2015). Ig-Knn Untuk Prediksi Customer Churn Telekomunikasi. Simetris : Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 6(1), 1.
[3]. Bandi, R., & Amudhavel, J. (2018). Object recognition using Keras with backend tensor flow. International Journal of Engineering and Technology(UAE), 7(3.6 Special Issue 6), 229–233.
[4]. Buslim, N. (2019). Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat. Jurnal Teknik Informatika, 12(1), 79–96.
[5]. Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Peter Campbell, J. (2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science and Technology, 9(2), 1–12.
[6]. Goldsborough, P. (2016). A Tour of TensorFlow. October.
[7]. Gullo, F. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62, 18–22.
[8]. Jan, P., & Gotama, W. (2018). Pengenalan Pembelajaran Mesin dan Deep Learning. 2019, July, 1–199.
[9]. Kumar, V., & L., M. (2018). Deep Learning as a Frontier of Machine Learning: A Review. International Journal of Computer Applications, 182(1), 22–30.
[10]. Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1). https://doi.org/10.3390/risks7010029
[11]. Muhammad, I., & Yan, Z. (2015). Supervised Machine Learning Approaches: a Survey. ICTACT Journal on Soft Computing, 05(03), 946–952.
[12]. Pertiwi, R. W., & Widiyanto, I. (2015). Minat Churn Pelanggan Indosat Di Indonesia. 4, 1–13.
[13]. Rachmat, A. (2019). Survei Penerapan Model Machine Learning Dalam Bidang Keamanan Informasi. Jurnal Sistem Cerdas, 2(1), 47–60.
[14]. Ritonga, A. S., & Atmojo, S. (2018). Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra). Jurnal Ilmiah Teknologi Informasi Asia, 12(1), 15.
[15]. Vogt, M. (2019). An Overview of Deep Learning and Its Applications. January, 178–202. https://doi.org/10.1007/978-3-658-23751-6_17
Ndruru, T., & Riandari, F. (2019). Decision Support System Feasibility Lending At KSU Mitra Karya Cooperative Customer Unit XXVIII with Analytical Hierarchy Process Method. Jurnal Mantik, 3(3, Nov), 119-125. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/335
Sibagariang, R., & Riandari, F. (2019). Decision Support System for Determining the Best Wood For the Production Cabinet Using Bayes Method. Jurnal Mantik, 3(3, Nov), 99-103. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/327
Pamungkas, A., & Riandari, F. (2019). Analysis of Disease in Plants Guava Demspter Shafer Method Using Web Based in the village of Paradise Sei Rampah. Jurnal Mantik, 3(3, Nov), 69-72. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/318
Ridandari, F., & Panjaitan, A. (2019). Expert System to Diagnose Extra Lung Tuberculosis Using Bayes Theorem. Jurnal Mantik, 3(3, Nov), 34-39. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/285
Situmorang, E., & Riandari, F. (2019). Decision Support System For Selection Of The Best Doctors In Sari Mutiara Hospital Using Fuzzy Tsukamoto Method. Jurnal Mantik, 3(3, Nov), 28-33. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/265
Anggraini, D., & Sihotang, H. (2019). Decision Support System For Choosing The Best Class Guardian With Simple Additive Weighting Method. Jurnal Mantik, 3(3, Nov), 1-9. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/278
Puspa, M., & Sihotang, H. (2019). Decision Support System For Supplementary Food Recipients (PMT) By Using The Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 19-27. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/280
Purba, E., & Sihotang, H. (2019). Decision Support System For Prospective Recipients Of The Healthy Indonesia Card (Kis) In The Village Of Bah Sidua Dua With The Analytical Hierarchy Process (AHP) Method. Jurnal Mantik, 3(3, Nov), 82-90. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/320
Purba, R., & Sihotang, H. (2019). Decision Support Systems Recipient Program Keluarga Harapan (PKH) In Durian Kec.Pantai Labu Kab. Deli Serdang with the Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 91-98. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/325
Devi, S., & Sihotang, H. (2019). Decision Support Systems Assessment of the best village in Perbaungan sub-district with the Simple Additive Weighting (SAW) Method. Jurnal Mantik, 3(3, Nov), 112-118. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/334