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
- Indra Indra, Nur Aliza, Detecting Disaster Trending Topics on Indonesian Tweets Using BNgram , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Susandri Susandri, Ahmad Zamsuri, Nurliana Nasution, Yoyon Efendi, Hiba Basim Alwan, The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 2 (2025)
- Muhammad Ilhamdi Rusydi, Yoan Winata, Dhiny Yurichy Putri, Budi Agung Santoso, Nurul Azizah Dhuha, Muhammad Khalish, Ikhwan Arief, Hermawan Nugroho, Perancangan Platform Pengaduan Perundungan Berlandasarkan Bukti menggunakan Metode Agile , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 2 (2022)
- Tri Astuti, Galuh Kusumastuti, Rudi Fitriyanto, PEMANFAATAN ANALYTICAL HIERARCHY PROCESS (AHP) PADA E-VOTING PEMILIHAN KETUA OSIS , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 17 No. 1 (2017)
- Perani Rosyani, Saprudin Saprudin, Deteksi Citra Bunga Menggunakan Analisis Segmentasi Fuzzy C-Means dan Otsu Threshold , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 20 No. 1 (2020)
- Rizky Afrinanda, Lusiana Efrizoni, Wirta Agustin, Rahmiati Rahmiati, Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Muhamad Azwar, Eka Nurul Qomaliyah, Nurul Indriani, Development of a Smart System for Optimizing Treatment Using Forward Chaining Method , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- Angelina Ervina Jeanette Egeten, Yanes Hardianto S, Putri Ayu P, Okky Marita S, Analisis dan Perancangan Sistem Informasi E-Procurement Modul pada Pemesanan Barang Non Produksi di PT Toyota Motor Manufacturing Indonesia , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 17 No. 2 (2018)
- Siti Ummi Masruroh, Andrew Fiade, Muhammad Ikhsan Tanggok, Rizka Amalia Putri, Luigi Ajeng Pratiwi, Convolutional Neural Network for Colorization of Black and White Photos , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Magdalena Ariance Ineke Pakereng, Alz Danny Wowor, Yos Richard Beeh, Felix David, Erwien Christianto, Vincent Exelcio Susanto, Claudio Canavaro, Square Transposition Method with Adaptive Key Flexibility and Strong Diffusion Performance , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
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)
