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%.
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References
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[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.
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Published
2021-05-30
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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.
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