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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL MEDIA INFORMATIKA BUDIDARMA ILKOM Jurnal Ilmiah JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Teknologi Terpadu Journal of System and Computer Engineering Jurnal Informatika Terpadu Global Abdimas: Jurnal Pengabdian Masyarakat Sentra Dedikasi: Jurnal Pengabdian Masyarakat Journal Pharmacy and Application of Computer Sciences Jurnal Informatika: Jurnal Pengembangan IT
Firman Aziz
Universitas Pancasakti Makassar, Indonesia
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Jurnal Informatika: Jurnal Pengembangan IT

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Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost Firman Aziz; Benny Leonard Enrico Panggabean
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5675

Abstract

In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.
Co-Authors Akbar Taufik Ali Akbar Velayaty Amri, Nur Fadhila Andi Nurilmi Adriana Andyka Wahab Anirwan Anirwan Armin Lawi Benny L.E.P Benny Leonard Enrico Panggabean Dhilan Sasmita Faisal Rahman Farid Fani Temarwut Firmansyah Firmansyah Firmansyah Hechmi SHILI Imran Iskandar Ishak, Pertiwi Jeffry jeffry jeffry - Jeffry Jeffry Jeffry Jeffry Lutfi Budi Ilmawan M Rezky Armansyah Mardewi Mardewi Mardewi Mardewi Muhammad Arfah Asis Muhammad Lutfi Muhammad Rijal Nur Ayu Asrhi Nur Ayu Asrhi Nur Hamadani Nur Nur Hamdani Nur Nurafni Shahnyb Nuru Yarkuran Nurul Fathanah Mustamin Rahmat Fuadi Syam Rohmah Nur Hidayah Satar Satar Sofyan Sofyan Sofyan Sofyan Sustrin Abasa Syahrul Usman Syahrul Usman Syahrul Usman Syahrul Usman Usman, Syahrul Wungo, Supriyadi La Zulkarnain Zulkarnain