Pengembangan Verifikasi Informasi Cek Bank dengan Menggunakan CNN-XGBoost Image Classification untuk Verifikasi Tanda Tangan dan Pengenalan Tulisan Tangan

Kevin Saputra Utomo; Yefta Christian; Andik Yulianto

doi:10.23887/insert.v5i2.85907

Authors

Kevin Saputra Utomo Universitas International Batam
Yefta Christian Universitas International Batam
Andik Yulianto Universitas International Batam

DOI:

https://doi.org/10.23887/insert.v5i2.85907

Keywords:

Convolutional Neural Network (CNN), XGBoost, Verifikasi Tanda Tangan, Pengenalan Tulisan Tangan, Cek Bank

Abstract

Sistem bank menjadi lebih maju di mana banyak orang yang sekarang bahkan tidak memegang uang tunai dan melakukan transaksi melalui aplikasi atau transfer online.

Namun, ada beberapa transaksi bank yang ketinggalan jaman, seperti cek bank tertulis. Verifikasi dan kliring cek bank tertulis membutuhkan tenaga kerja yang sangat besar. Oleh karena itu, ada berbagai penelitian yang dilakukan untuk membuat transaksi cek bank menjadi lebih efisien, salah satunya adalah penggunaan machine learning. Penelitian ini dilakukan dengan tujuan mengetahui akurasi CNN-XGBoost untuk verifikasi tanda tangan dan rekognisi angka dan huruf. Untuk pengembangan penelitian ini, sebuah simulasi verifikasi dan pengecekan informasi cek bank akan dilakukan dengan model CNN-XGBoost. Dataset yang dipakai untuk pelatihan model di penelitian ini adalah IDRBT bank cheque dataset, EMNIST Digit, dan EMNIST Letter. Dataset akan di preprocess menggunakan beberapa cara seperti menghilangkan background, grayscale, padding, pixel normalize, histogram equalization, gaussian blur, dan canny edge detection. Dari penelitian didapatkan hasil accuracy model untuk verifikasi tanda tangan sebesar 97.06%, untuk rekognisi huruf 98.76%, dan rekognisi angka 99.57%. Model tidak dapat digunakan untuk sepenuhnya menggantikan tenaga kerja manusia dalam kliring cek bank tertulis. Ini karena risiko kesalahan dalam transfer keuangan sangat berbahaya, kesalahan dalam transfer dapat menyebabkan uang yang dikirim jauh berbeda dari yang tertera di cek. CNN-XGBoost masih berguna untuk membantu ekstraksi informasi untuk membantu mengurangi tenaga kerja yang dibutuhkan.

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