SEGMENTASI PEMEGANG KARTU KREDIT MELALUI KLASTERISASI K-MEANS MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON

Penulis

  • Agil Haubi Zikri Universitas Muhammadiyah Jakarta
  • Rita Dewi Risanty Universitas Muhammadiyah Jakarta

Abstrak

Dalam perkembangan ekonomi yang cepat, penggunaan kartu kredit telah menjadi sangat penting. Kartu kredit, sebagai instrumen pembayaran yang diterbitkan oleh bank, memberikan kemampuan kredit kepada pemiliknya untuk melakukan pembelian barang atau layanan. Namun, penggunaannya mengalami penurunan yang signifikan akibat perubahan pola hidup dan dampak pandemi Covid-19. Survei perbankan menunjukkan tren penurunan dalam transaksi kartu kredit, yang dipicu oleh pengurangan pendapatan masyarakat selama pandemi. Untuk mengatasi tantangan ini, bank perlu mengembangkan strategi bisnis yang efektif. Salah satu pendekatan yang dapat digunakan adalah klasterisasi dengan algoritma K-Means. Algoritma K-Means merupakan teknik klasterisasi yang efektif digunakan untuk mengelompokkan data dengan tujuan memaksimalkan kesamaan antara variabel sambil tetap mempertahankan perbedaan karakteristiknya. Dengan mengimplementasikan algoritma ini dalam bahasa pemrograman Python, bank dapat memperoleh pemahaman yang lebih mendalam tentang preferensi dan kebutuhan dari setiap segmen pemegang kartu kredit. Demikian, algoritma K-Means dapat membantu dalam pengembangan strategi bisnis yang lebih efektif untuk meningkatkan penggunaan kartu kredit, serta mendapatkan nasabah baru. Selain itu, algoritma ini juga bermanfaat dalam mengatasi dampak penurunan penggunaan kartu kredit yang disebabkan oleh perubahan pola hidup masyarakat dan pandemi Covid-19. Hasil dari penelitian ini adalah pengelompokan data pengguna kartu kredit menjadi tiga kelompok yang dapat digunakan sebagai panduan dalam merumuskan strategi bisnis perbankan.

Referensi

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2025-08-21

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Vol 15 No 3 (2025): Volume 15 No 3, Mei Tahun 2025

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