Comparison of Classification C4.5 Algorithms and Naive Bayes Classifier in Determining Merchant Acceptance on Sponsorship Program

research
  • 23 Aug
  • 2020

Comparison of Classification C4.5 Algorithms and Naive Bayes Classifier in Determining Merchant Acceptance on Sponsorship Program

The large number of merchants that make sponsorship held by the Bank reaches thousands, data mining is used to classifying thousands of data. Naïve Bayes algorithm and C 4.5 are classification algorithms in data mining. The classification results are used as determinant where the merchant deserves to receive the sponsorship program, which potentially provides the source of funds and increase the brand awareness of the company by looking at the performance, transaction amount, total nominal, average daily transaction, average transaction nominal. Comparison results show that The C 4.5 algorithm is the best model for handling case of Merchant eligibility in the Sponsorship Program. This can be proved by looking at the level of accuracy generated on the testing and validation process of the model. Both models have the same AUC value but the C 4.5 algorithm produces a superior accuracy value with a difference of 0.45% compared to Naïve Bayes.

Unduhan

  • File unduhan

    ProgramBook.pdf

    Terakhir download 22 Jun 2025 14:06

    Program Book ICAISD 2020

    •   diunduh 705x | Ukuran 2,422 KB
  • File unduhan

    Sertifikat Author.pdf

    Terakhir download 19 Jun 2025 10:06

    Sertifikat Author-Comparison of Classification C 4.5 Algorithms and Naive Bayes Classifier in Determining Merchant Acceptance in Sponsorship Program

    •   diunduh 236x | Ukuran 843 KB
  • File unduhan

    ICAISD_2020_paper_28.pdf.pdf

    Terakhir download 19 Jun 2025 10:06

    Hasil Turnitin

    •   diunduh 302x | Ukuran 1,410 KB

 

REFERENSI

[1]    D. A. Muthia, “Sentiment Analysis Of Hotel Review Using Naïve Bayes Algorithm And Integration Of Information Gain And,” Int. Semin. Sci. Issues Trends, Pp. 25–30, 2014.
[2]    G. Masterman, Sponsorship For A Return On Investment, 2nd Ed. New York: Routledge, 2011.
[3]    P. S. B. Hans Mathias Thjømøe, Erik L. Olson, “Decision-Making Processes Surrounding Sponsorship Activities,” J. Advert. Res., Vol. 42, No. 6, Pp. 6–5.
[4]    R. Nofitri And Juna Eska, “Seminar Nasional Royal (Senar) 2018,” Vol. 9986, No. September, 2018.
[5]    M. F. Arifin And D. Fitrianah, “Rekomendasi Penerimaan Mitra Penjualan Studi Kasus : Pt Atria Artha Persada,” No. December 2017.
[6]    C. Algorithm, B. P. Candra, E. Rachmat, And S. Hidayat, “Decision Support System For Stock Prediction And Supplier Selection Using Least Square,” 2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng., Pp. 241–246, 2018.
[7]    S. Bagui, K. Devulapalli, And S. John, “Mapreduce Implementation Of A Multinomial And Mixed Naive Bayes Classifier,” J. Intell. Inf. Technol., Vol. 16, No. 2, P. 23, 2020.
[8]    A. Kadafi And W. Gata, “Particle Swarm Optimization Based C4.5 For Teacher Performance Classification,” Pp. 1–12, 2019.
[9]    F. N. Hasan; And M. Wahyudi, “Analisis Sentimen Artikel Berita Tokoh Sepak Bola Dunia Menggunakan Algoritma Support Vector Machine Dan Naive Bayes Berbasis Particle Swarm Optimization,” Akrab Juara, Vol. 3, No. 4, Pp. 42–55, 2018.
[10]  I. Yulianti, B. S. Informatika, And S. Reuse, “Analisis Komparasi Klasifikasi Algoritma C4.5 Dan Naïve,” vol. 7, no. 1, 2019.

Cari

Bidang Ilmu

Kategori

Kiriman Terbaru