A NOVEL MODEL OF MACHINE LEARNING CLASSIFIER FOR DECISION SUPPORT SYSTEM

AFIFAH, RAFA NABILA (2024) A NOVEL MODEL OF MACHINE LEARNING CLASSIFIER FOR DECISION SUPPORT SYSTEM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Digital payment is one of the innovations in the modern financial technology in this world. This technology has fundamentally changed the way today’s society conducts financial transactions. The benefit of digital payment is that it provides a technological solution that enables electronic payment transactions between sellers and consumers. Therefore, along with the development of technology in digital payment, there is great room to improve efficiency, security, and decision-making capabilities in the digital payment ecosystem. Several previous studies on decisionmaking systems have built decision-making models by applying the Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. However, these studies have several problems such as data processing that requires a long time and limited use of data with high dimensions. In this research, we propose a new model called Binary Classifier based on Machine Learning Approach that can be used to help company for decision making. The results of this study using the digital payment dataset had an evaluation value of precision 83.384%, recall 83.698%, dan f1 score 83.446%. Keywords: Decision Making System, Binary Classification, Digital Payment Digital payment, atau pembayaran digital, merupakan salah satu inovasi dalam dunia keuangan modern. Teknologi ini telah mengubah cara masyarakat saat ini dalam melakukan transaksi keuangan secara fundamental. Manfaat yang dapat dirasakan dengan adanya digital payment adalah menyediakan solusi teknologi yang memungkinkan transaksi pembayaran elektronik antara penjual dan konsumen. Maka dari itu, seiring berkembangnya teknologi pada digital payment terdapat ruang besar untuk meningkatkan efisiensi, keamanan, dan kemampuan pengambilan keputusan dalam ekosistem pembayaran digital. Beberapa penelitian mengenai sistem pengambilan keputusan sebelumnya telah membangun model pengambilan keputusan dengan menerapkan metode Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Akan tetapi, penelitian tersebut memiliki beberapa permasalahan seperti pengolahan data yang memerlukan waktu yang lama serta keterbatasan penggunaan data dengan dimensi yang tinggi. Pada penelitian ini kami mengusulkan model baru yang dinamakan Binary Classifier based on Machine Learning Approach yang dapat digunakan untuk membantu pengambilan keputusan perusahaan. Hasil dari penelitian ini dengan menggunakan dataset pembayaran digital menyimpulkan bahwa penelitian yang kami gunakan memiliki nilai evaluasi precision 83.384%, recall 83.698%, dan f1 score 83.446%. Kata kunci : Sistem Pengambilan Keputusan, Klasifikasi Biner, Pembayaran Digital

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 24 011
Call Number:	SIK/18/24/007
NIM/NIDN Creators:	41819120038
Uncontrolled Keywords:	Sistem Pengambilan Keputusan, Klasifikasi Biner, Pembayaran Digital
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.7 Peripherals/Periferal > 004.71 Peripherals for Digital Computers/Periferal Untuk Komputer Digital 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 653 Shorthand/Penulisan Cepat, Penulisan dengan Tangan > 653.3 Machine Systems/Sistem Mesin
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	26 Jan 2024 03:56
Last Modified:	26 Jan 2024 03:56
URI:	http://repository.mercubuana.ac.id/id/eprint/85587

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