ANALISIS CLUSTERING BANK PAYMENT BERDASARKAN PENJUALAN BARANG PADA GOGOMALL MENGGUNAKAN ALGORITMA K-MEANS DAN PARTICLE SWARM OPTIMIZATION

NUGROHO, AGUNG (2023) ANALISIS CLUSTERING BANK PAYMENT BERDASARKAN PENJUALAN BARANG PADA GOGOMALL MENGGUNAKAN ALGORITMA K-MEANS DAN PARTICLE SWARM OPTIMIZATION. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Bank Indonesia launched the National Non-Cash Movement (GNNT) in order to create a safe, efficient and smooth payment system, improve transaction efficiency so that people no longer need to carry large amounts of cash, and minimize problems with cash payments. The use of the K-Means Clustering and Particle swarm Optimization methods to analyze group clustering is very appropriate to be applied to determine patterns of clustering so that higher quality information is produced. The method to be used in this study is the CRISP-DM method. By implementing the K-Means optimization algorithm, namely Particle swarm Optimization, it has a more optimal final result. With the value of Sum of Square Error = 21.0290053407 and Quantization = 2.70774885672. Clusterring Use of Bank Payments with low (C1=1816), Medium (C2=1298) and high (C3=883) categories. From the analysis of segmentation calculations using Bank Payments at Gogomall, there are 2 Payment Banks that have high potential for use as payment transactions, namely Bank BCA and BRI Keywords: Clustering, K-Means, Particle swarm Optimization, Python Bank Indonesia mencanangkan Gerakan Nasional Non Tunai (GNNT) dalam rangka menciptakan sistem pembayaran yang aman, efisien, dan lancar, meningkatkan efisiensi transaksi agar masyarakat tidak perlu lagi membawa uang tunai dalam jumlah besar, dan meminimalisir kendala dalam pembayaran tunai. Penggunaan metode K-Means Clustering dan Particle swarm Optimization untuk menganalisa clustering kelompok sangat tepat diterapkan untuk mengetahui pola dari clustering sehingga dihasilkan informasi yang lebih bermutu . Metode yang akan digunakan dalam penelitian ini adalah metode CRISP-DM. Dengan implementasi algoritma optimasi K-Means yaitu Particle swarm Optimization memiliki hasil akhir yang lebih optimal. Dengan nilai Sum of Square Error = 21.0290053407 dan Quantization = 2.70774885672. Clusterring Penggunaan Bank Payment dengan kategori rendah (C1=1816), Sedang(C2=1298) dan tinggi (C3=883). Dari hasil analisis perhitungan segmentasi Penggunaan Bank Payment di Gogomall yaitu terdapat 2 Bank Payment yang memiliki potensi Tinggi dalam penggunaan sebagai transaksi pembayaran yaitu, Bank BCA dan BRI. Kata Kunci : Clustering, K-Means, Particle swarm Optimization, Python

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 132
NIM/NIDN Creators:	41519010167
Uncontrolled Keywords:	Clustering, K-Means, Particle swarm Optimization, Python
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 > 003 Systems/Sistem-sistem 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 > 003 Systems/Sistem-sistem > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer 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 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 > 004.1 General Works on Specific Types of Computers/Karya Umum tentang Tipe-tipe Khusus Komputer
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	CALVIN PRASETYO
Date Deposited:	11 Oct 2023 06:23
Last Modified:	11 Oct 2023 06:23
URI:	http://repository.mercubuana.ac.id/id/eprint/82336

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