Application of Data Mining Classification to Store Customer Satisfaction Bombay Textiles

Authors

  • Siti Sundari STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Yuegilion Pranayama Purba STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i4.1672

Keywords:

C4.5 Algorithm, Data Mining, Satisfaction, Classification, Textiles

Abstract

This study aims to obtain a model of rules in classifying the level of customer satisfaction at Bombay Textile Stores. By knowing the level of customer satisfaction, shop owners can improve service if it is not good and further improve service if the level of satisfaction is good. This study measures the level of customer satisfaction at the Bombay Textile Store. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire/questionnaire technique given to Bombay Textile Store customers. The variables used include Service, Quality of Goods, Price, Facilities, and Promotion. The results obtained 11 rules for the classification of customer satisfaction levels with 5 rules satisfied status and 6 rules dissatisfied status. The C4.5 algorithm can be used in the case of customer satisfaction levels with an accuracy rate of 96.67%. From the results of the analysis, it is hoped that it can be applied so that it can be used as a decision to improve service to customers.

References

C. B. Dewa and L. A. Safitri, “Analisa Pengaruh Kualitas Pelayanan dan Kepuasan Pelanggan terhadap Loyalitas Pelanggan OVO pada Masa Physical Distancing,” ASSET: Jurnal Manajemen dan Bisnis, vol. 3, no. 1, 2020.

R. Fauzy, “Bulletin Of Computer Science Research Analisis Tingkat Kepuasan Pelanggan dengan Menerapkan,” vol. 2, no. 2, pp. 41–46, 2022.

F. Anggraini and A. Budiarti, “Pengaruh Harga, Promosi, dan Kualitas Pelayanan Terhadap Loyalitas Pelanggan Dimediasi Kepuasan Pelanggan Pada Konsumen Gojek,” Jurnal Pendidikan Ekonomi (JUPE), vol. 8, no. 3, pp. 86–94, 2020.

Y. Indah Lestari and S. Defit, “Jurnal Informatika Ekonomi Bisnis Prediksi Tingkat Kepuasan Pelayanan Online Menggunakan Metode Algoritma C.45,” vol. 3, pp. 148–154, 2021.

I. G. B. Subawa and E. Sulistyawati, “Populasi dalam penelitian ini adalah pelanggan yang loyal menggunakan jasa Lila,” vol. 9, no. 2, pp. 718–736, 2020.

K. F. Irnanda and A. P. Windarto, “Penerapan Klasifikasi C4.5 Dalam Meningkatkan Kecakapan Berbahasa Inggris dalam Masyarakat,” Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), pp. 304–308, 2020.

S. Sundari, S. M. Sinaga, I. S. Damanik, and A. Wanto, “Sistem Pendukung Keputusan Pemilihan Peserta Olimpiade Matematika SMA Swasta Teladan Pematangsiantar Dengan Metode Electre,” in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2019, pp. 793–799.

S. Sundari, A. Wanto, Saifullah, and I. Gunawan, “Sistem Pendukung Keputusan Dengan Menggunakan Metode Electre Dalam Merekomendasikan Dosen Berprestasi Bidang Ilmu Komputer (Study Kasus di AMIK & STIKOM Tunas Bangsa),” in Seminar Nasional Multi Disiplin Ilmu, 2017, pp. 1–6.

H. Wanto, Anjar. Damanik, “Analisis Penerapan Sistem Pendukung Keputusan Terhadap Seleksi Penerima Beasiswa BBM (Bantuan Belajar Mahasiswa) Pada Perguruan Tinggi Menggunakan Metode Simple Additive Weighting (SAW) (Studi Kasus : AMIK Tunas Bangsa Pematangsiantar),” Prosiding Seminar Nasional Rekayasa (SNTR) II Volume (2) 25 November 2015 ISSN : 2407- 735, vol. 2, no. November, pp. 323–324, 2015.

S. R. Ningsih, D. Hartama, A. Wanto, I. Parlina, and Solikhun, “Penerapan Sistem Pendukung Keputusan Pada Pemilihan Objek Wisata di Simalungun,” in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2019, pp. 731–735.

S. F. Damanik, A. Wanto, and I. Gunawan, “Penerapan Algoritma Decision Tree C4.5 untuk Klasifikasi Tingkat Kesejahteraan Keluarga pada Desa Tiga Dolok,” Jurnal Krisnadana Volume, vol. 1, no. 2, pp. 21–32, 2022.

A. Wanto and H. Damanik, “Analisis Penerapan Sistem Pendukung Keputusan Terhadap Seleksi Penerima Beasiswa BBM (Bantuan Belajar Mahasiswa) Pada Perguruan Tinggi Menggunakan Metode Simple Additive Weighting (SAW) (Studi Kasus : AMIK Tunas Bangsa Pematangsiantar),” in Prosiding Seminar Nasional Rekayasa (SNTR) II, 2015, no. 2, pp. 323–333.

M. Widyasuti, A. Wanto, D. Hartama, and E. Purwanto, “Rekomendasi Penjualan Aksesoris Handphone Menggunakan Metode Analitycal Hierarchy Process (AHP),” Konferensi Nasional Teknologi Informasi dan Komputer (KOMIK), vol. I, no. 1, pp. 27–32, 2017.

R. Watrianthos, W. A. Ritonga, A. Rengganis, A. Wanto, and M. Isa Indrawan, “Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation,” Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012067, 2021.

D. R. Sari, I. Gunawan, and Z. M. Nasution, “Sistem Pakar Untuk Pemilihan Rujukan Online Pada Klinik Murni Kantor Cabang Pematang Siantar Menerapkan Metode Forward Chaining,” TIN: Terapan Informatika Nusantara, vol. 2, no. 5, pp. 319–327, 2021.

V. V. Utari, A. Wanto, I. Gunawan, and Z. M. Nasution, “Prediksi Hasil Produksi Kelapa Sawit PTPN IV Bahjambi Menggunakan Algoritma Backpropagation,” Journal of Computer System and Informatics (JoSYC, vol. 2, no. 3, pp. 271–279, 2021.

Y. Andriani, A. Wanto, and H. Handrizal, “Jaringan Saraf Tiruan dalam Memprediksi Produksi Kelapa Sawit di PT. KRE Menggunakan Algoritma Levenberg Marquardt,” Prosiding Seminar Nasional Riset Information Science (SENARIS), vol. 1, no. September, pp. 249–259, 2019.

A. Wanto, “Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan,” Klik - Kumpul. J. Ilmu Komput, vol. 5, no. 1, p. 61, 2018.

I. S. Purba and A. Wanto, “Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation,” Jurnal Teknologi Informasi Techno, vol. 17, no. 3, pp. 302–311, 2018.

S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018.

M. Julham, S. Sumarno, F. Anggraini, A. Wanto, and S. Solikhun, “Penerapan Jaringan Syaraf Tiruan dalam Memprediksi Tingkat Kriminal di Kabupaten Simalungun Menggunakan Algoritma Backpropagation,” BRAHMANA: Jurnal Penerapan Kecerdasan Buatan, vol. 1, no. 1, pp. 64–73, 2019.

M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation Dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara,” Jurnal semanTIK, vol. 4, no. 1, pp. 77–86, 2018.

A. Wanto et al., “Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

N. L. W. S. R. Ginantra et al., “Performance One-step secant Training Method for Forecasting Cases,” Journal of Physics: Conference Series, vol. 1933, no. 1, pp. 1–8, 2021.

I. Parlina et al., “Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–5, 2019.

N. A. Febriyati, A. D. GS, and A. Wanto, “GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020.

A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, and J. Jalaluddin, “The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, pp. 31–36, 2019.

R. H. Alawiah, Saifullah, and I. S. Damanik, “Analisis Kepuasan Konsumen Terhadap Pelayanan Bengkel Menggunakan Metode Algoritma C4.5,” Januari, vol. 2, no. 1, pp. 31–38, 2021.

Hendri and D. Oscar, “Penerapan Algoritma C4 . 5 Dalam Mengukur Kepuasan Pengunjung Terhadap Fasilitas di Taman Margasatwa Jakarta,” vol. 3, no. 1, 2021.

R. E. Putri, Suparti, and R. Rahmawati, “Perbandingan Metode Klasifikasi Naãve Bayes Dan K-Nearest Neighbor Pada Analisis Data Status Kerja Di Kabupaten Demak Tahun 2012,” Jurnal Gaussian, vol. 3, no. 4, pp. 831–838, 2014.

E. Nurlia and U. Enri, “Penerapan Fitur Seleksi Forward Selection Untuk Menentukan Kematian Akibat Gagal Jantung Menggunakan Algoritma C4.5,” Jurnal Teknik Informatika Musirawas) Elin Nurlia, vol. 6, no. 1, p. 42, 2021.

Downloads

Published

2022-12-30

How to Cite

Sundari, S., Windarto, A. P., & Purba, Y. P. (2022). Application of Data Mining Classification to Store Customer Satisfaction Bombay Textiles. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(4), 319–324. https://doi.org/10.55123/jomlai.v1i4.1672

Issue

Vol. 1 No. 4 (2022): December

Section

Articles

License

Copyright (c) 2022 Siti Sundari, Agus Perdana Windarto, Yuegilion Pranayama Purba

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:their article is original, written by the mentioned author(s),

  • has never been published before,
  • does not contain statements that violate the law, and
  • does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to the article, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce the article for its own purposes,
  • The right to archive all versions of the article in any repository, and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published