Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing untuk Deposito
DOI:
https://doi.org/10.30812/matrik.v20i2.1183Keywords:
Seleksi Fitur, Correlation Based, Klasifikasi, Prediksi, Multilayer PerceptronAbstract
Pre-processing merupakan tahap yang penting dalam melakukan klasifikasi data. Pre-processing berguna untuk mempersiapkan data sehingga teknik klasifikasi yang diterapkan menghasilkan pola yang berkualitas dan akurat. Salah satu teknik data pre-processing yang sering digunakan untuk mengetahui atribut yang paling berpengaruh pada sebuah dataset adalah feature selection. Data yang digunakan dalam penelitian ini adalah customer data collection dari a Portuguese banking institution in UCI Machine Learning Repository. Penelitian ini menggunakan metode feature selection correlation-based yang dikombinasikan dengan metode klasifikasi Multilayer Perceptron Neural Networks. Tujuan penelitian ini untuk mengidentifikasi atribut yang paling relevan dan berpengaruh dari dataset dalam memprediksi nasabah yang potensial untuk penawaran deposito berjangka. Penelitian ini menghasilkan 10 atribut yang memiliki ranking teratas. Atribut-atribut tersebut adalah duration, previous, contact, cons.price.idx, month, cons.cof.idx, age, job, marital, dan housing. Hasil klasifikasi dari atribut yang terpilih memiliki tingkat akurasi tertinggi sebesar 80.5% dan tingkat akurasi terendah 79.1%.
Downloads
Download data is not yet available.
References
[1] A. E. Maulana and K. Nurulfirdausi, “Permissive, Aggressive or Apathetic? Indonesian Telemarketing Customer,†in Procedia - Social and Behavioral Sciences, Jan. 2015, vol. 169, pp. 69–74.
[2] J. Nalic and A. Svraka, “Importance of data pre-processing in credit scoring models based on data mining approaches,†in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), May 2018, pp. 1046–1051.
[3] I. made B. Adnyana, “Penerapan Feature Selection untuk Prediksi Lama Studi Mahasiswa,†Jurnal Sistem Dan Informatika, vol. 13, no. 2, pp. 72–76, 2019.
[4] N. K. Suchetha, A. Nikhil, and P. Hrudya, “Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification,†in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019, pp. 1–6.
[5] S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,†Decision Support System, vol. 62, pp. 22–31, 2014.
[6] J. Asare-Frempong and M. Jayabalan, “Predicting customer response to bank direct telemarketing campaign,†in 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Sep. 2017, vol. 2017-Janua, pp. 1–4.
[7] K. Morani, E. K. Ayana, and S. N. Engin, “Developement of Prediction in Clients’ Consent to a Bank Term Deposit Using Feature Selection,†in 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Oct. 2018, pp. 1–5.
[8] A. S. B. Asmoro, W. S. G. Irianto, and U. Pujianto, “Perbandingan Kinerja Hasil Seleksi Fitur pada Prediksi Kinerja Akademik Siswa Berbasis Pohon Keputusan,†Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 4, no. 2, pp. 84–89, Dec. 2018.
[9] A. N. Puteri, Dewiani, and Z. Tahir, “Comparison of Potential Telemarketing Customers Predictions with a Data Mining Approach using the MLPNN and RBFNN Methods,†in 2019 International Conference on Information and Communications Technology (ICOIACT), Jul. 2019, pp. 383–387.
[10] P. Hosein, S. Ramoudith, and I. Rahaman, “On the Optimal Allocation of Resources for a Marketing Campaign,†in Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, 2021, pp. 169–176.
[11] M. Dash and H. Liu, “Feature Selection for Classification,†Intelligent Data Analysis., vol. 1, no. 4, pp. 131–156, 1997.
[12] G. S, T. M, M. V.T, and G. V, “Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA,†International Journal of Advanced Networking and Applications, vol. 9, no. 6, pp. 3640–3644, 2018.
[13] Y. Sugianela and T. Ahmad, “Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System,†in 2020 International Conference on Smart Technology and Applications (ICoSTA), Feb. 2020, pp. 1–5.
[14] A. Kustiyo, H. Firqiani, and E. Giri, “Seleksi Fitur Menggunakan Fast Correlation Based Filter pada Algoritma Voting Feature Intervals 5,†Jurnal Ilmu Komputer, vol. 6, no. 2, pp. 1–12, 2008.
[15] M. A. Halali, V. Azari, M. Arabloo, A. H. Mohammadi, and A. Bahadori, “Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines,†Journal of the Taiwan Institute Chemical Engineers, vol. 58, pp. 189–202, Jan. 2016.
[2] J. Nalic and A. Svraka, “Importance of data pre-processing in credit scoring models based on data mining approaches,†in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), May 2018, pp. 1046–1051.
[3] I. made B. Adnyana, “Penerapan Feature Selection untuk Prediksi Lama Studi Mahasiswa,†Jurnal Sistem Dan Informatika, vol. 13, no. 2, pp. 72–76, 2019.
[4] N. K. Suchetha, A. Nikhil, and P. Hrudya, “Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification,†in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019, pp. 1–6.
[5] S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,†Decision Support System, vol. 62, pp. 22–31, 2014.
[6] J. Asare-Frempong and M. Jayabalan, “Predicting customer response to bank direct telemarketing campaign,†in 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Sep. 2017, vol. 2017-Janua, pp. 1–4.
[7] K. Morani, E. K. Ayana, and S. N. Engin, “Developement of Prediction in Clients’ Consent to a Bank Term Deposit Using Feature Selection,†in 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Oct. 2018, pp. 1–5.
[8] A. S. B. Asmoro, W. S. G. Irianto, and U. Pujianto, “Perbandingan Kinerja Hasil Seleksi Fitur pada Prediksi Kinerja Akademik Siswa Berbasis Pohon Keputusan,†Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 4, no. 2, pp. 84–89, Dec. 2018.
[9] A. N. Puteri, Dewiani, and Z. Tahir, “Comparison of Potential Telemarketing Customers Predictions with a Data Mining Approach using the MLPNN and RBFNN Methods,†in 2019 International Conference on Information and Communications Technology (ICOIACT), Jul. 2019, pp. 383–387.
[10] P. Hosein, S. Ramoudith, and I. Rahaman, “On the Optimal Allocation of Resources for a Marketing Campaign,†in Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, 2021, pp. 169–176.
[11] M. Dash and H. Liu, “Feature Selection for Classification,†Intelligent Data Analysis., vol. 1, no. 4, pp. 131–156, 1997.
[12] G. S, T. M, M. V.T, and G. V, “Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA,†International Journal of Advanced Networking and Applications, vol. 9, no. 6, pp. 3640–3644, 2018.
[13] Y. Sugianela and T. Ahmad, “Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System,†in 2020 International Conference on Smart Technology and Applications (ICoSTA), Feb. 2020, pp. 1–5.
[14] A. Kustiyo, H. Firqiani, and E. Giri, “Seleksi Fitur Menggunakan Fast Correlation Based Filter pada Algoritma Voting Feature Intervals 5,†Jurnal Ilmu Komputer, vol. 6, no. 2, pp. 1–12, 2008.
[15] M. A. Halali, V. Azari, M. Arabloo, A. H. Mohammadi, and A. Bahadori, “Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines,†Journal of the Taiwan Institute Chemical Engineers, vol. 58, pp. 189–202, Jan. 2016.
Downloads
Published
2021-05-30
Issue
Section
Articles
How to Cite
[1]
A. N. Puteri, A. Arizal, and A. D. Achmad, “Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing untuk Deposito”, MATRIK, vol. 20, no. 2, pp. 335–342, May 2021, doi: 10.30812/matrik.v20i2.1183.
Similar Articles
- Imam Riadi, Herman Herman, Nur Hamida Siregar, Mobile Forensic of Vaccine Hoaxes on Signal Messenger using DFRWS Framework , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- Annisa Nurul Puteri, Suryadi Syamsu, Topan Leoni Putra, Andita Dani Achmad, Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Sucipto Sucipto, Didik Dwi Prasetya, Triyanna Widiyaningtyas, Educational Data Mining: Multiple Choice Question Classification in Vocational School , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- I Putu Hariyadi, Akbar Juliansyah, Analisa Penerapan Private Cloud Computing Berbasis Proxmox Virtual Environment Sebagai Media Pembelajaran Praktikum Manajemen Jaringan , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 1 (2018)
- Sirojul Hadi, Puspita Dewi, Radimas Putra Muhammad Davi Labib, Parama Diptya Widayaka, Sistem Rumah Pintar Menggunakan Google Assistant dan Blynk Berbasis Internet of Things , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- Reo Wicaksono, Didik Dwi Prasetya, Ilham Ari Elbaith Zaeni, Nadindra Dwi Ariyanta, Tsukasa Hirashima, Machine Learning for Open-ended Concept Map Proposition Assessment: Impact of Length on Accuracy , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 25 No. 1 (2025)
- Ni Luh Putu Merawati, Adam Bachtiar, Apriani Apriani, Penerapan SMS Gateway pada Aplikasi Pendaftaran Siswa Baru di SMAN 1 Tanjung , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 1 (2018)
- Abd Mizwar A Rahim, Andi Sunyoto, Muhammad Rudyanto Arief, Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- Herman Kabetta, Hermawan Setiawan, Fetty Amelia, Muhammad Qolby Fawzan, Seamless Security on Mobile Devices Textual Password Quantification Model Based Usability Evaluation of Secure Rotary Entry Pad Authentication , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Syafri Arlis, Muhammad Reza Putra, Musli Yanto, Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 3 (2024)
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Annisa Nurul Puteri, Suryadi Syamsu, Topan Leoni Putra, Andita Dani Achmad, Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Muhammad Yusuf, Arizal Arizal, Ira Rosianal Hikmah, Implementation Cryptography and Access Control on IoT-Based Warehouse Inventory Management System , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 1 (2022)
.png)
