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Submitted
Feb 23, 2023
Published
Aug 21, 2023
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Abstract

This study addresses the problem faced by Onesnet in its environment, which is how to provide discounts or rewards to customers who have not received them before. This is due to the increasing competition in the internet installation service industry. The aim of this research is to determine the best customers in order to maintain customer loyalty in the increasingly competitive industry. The Weighted Product method is used as a decision-making technique by multiplying the ratings of each connected attribute, where the ratings of each attribute must be exponentiated with the corresponding attribute weight. The data used in this research comes from interviews and observations at Onesnet, which will be processed using the Rank Order Centroid method for weighting and Weighted Product method to evaluate and make a decision. The result of this study is the most loyal customer who becomes the best customer with a value of 0.14578.

Keywords

Customer; Rank Order Centroid; Weighted Products

Article Details

References

  1. [1] N. T. Pratiwi and A. Chintya, “Studi Komperatif Hak dan Kewajiban Konsumen Menurut UU No. 8 Tahun 1999 Tentang Perlindungan Konsumen dan Hukum Islam,” Fikri, vol. 2, no. 1, 2017.
  2. [2] D. Puspitasari, V. Safitri, M. Uniersitas, B. Darma, and D. Universitas, “Analisis Kepuasan Konsumen Terhadap Pengguna Speedy Pada Pt . Telkom,” pp. 1–12.
  3. [3] P. A. Wicaksana, I. B. A. Swamardika, and R. S. Hartati, “Literature Review Analisis Perilaku Pelanggan Menggunakan RFM Model,” Maj. Ilm. Teknol. Elektro, vol. 21, no. 1, p. 21, 2022, doi: 10.24843/mite.2022.v21i01.p04.
  4. [4] W. Romadhona, B. Indarmawan Nugroho, and A. Alim Murtopo, “Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means,” J. Minfo Polgan, vol. 11, no. 2, pp. 100–104, 2022, doi: 10.33395/jmp.v11i2.11797.
  5. [5] N. Nurfajriani, “Pengaruh Ekspektasi Pelanggan (Harapan Pelanggan) Dan Kepuasan Pelanggan Terhadap Loyalitas Pelanggan Internet (Studi Kasus Pada Pelanggan Internet Pt. Telkom Cabang Bima),” J. Manaj. Mandiri Saburai, vol. 4, no. 3, pp. 19–28, 2020, doi: 10.24967/jmms.v4i3.795.
  6. [6] R. Oktaviana and H. Himawan, “Sistem Pendukung Keputusan Untuk Menentukan Pelanggan Terbaik Pada TB. Bangun Jaya Menggunakan Metode Weighted Product (WP),” Univ. Dian Nuswantoro, pp. 1–9, 2020.
  7. [7] Y. Perwira, “Penentuan Peringkat Pelanggan Terbaik Dengan Metode Weighted Product (Studi Kasus Pt.Asia Raya Foundry),” J. Mantik Penusa, vol. 3, no. 1, pp. 138–147, 2019.
  8. [8] F. Setiawan, L. Lusiyanti, and D. Setiawan, “Implementasi Metode Weighted Product dan Pembobotan Rank Order Centroid Dalam Pemberian Penghargaan Predikat Kader Terbaik,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 215, 2022, doi: 10.30865/mib.v6i1.3375.
  9. [9] S. Damanik and D. P. Utomo, “Implementasi Metode ROC (Rank Order Centroid) Dan Waspas Dalam Sistem Pendukung Keputusan Pemilihan Kerjasama Vendor,” … Teknol. Inf. dan …, vol. 4, pp. 242–248, 2020, doi: 10.30865/komik.v4i1.2690.
  10. [10] R. Khalida, B. Bangun, M. Mesran, and N. Oktari, “Penerapan Metode ROC dan Weighted Aggregated Sum Product Assesment (WASPAS) dalam Penerimaan Asisten Perkebunan,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 937, 2021, doi: 10.30865/mib.v5i3.3092.