Approaches to Customer Types Classification Method in the Supermarket
DOI:
https://doi.org/10.34288/jri.v6i1.269Keywords:
Supermarkets, Customer Types, Classification, SVM, Naïve BayesAbstract
The development of the retail industry in the economy is very rapid so it provides good economic growth, one of the retailers is supermarkets, in supermarkets consumers can buy goods directly, so consumers must be served well. The problem is how supermarkets can continue to increase their sales results, because there is a lot of competition from supermarket competitors, so the marketing team when creating events or promotions must be right on target so that loyalty for member or non-member customers can be measured, which will be used as the right marketing strategy and can increase customer satisfaction when the customer is satisfied with the services, products and promotional activities at the supermarket, the customer will continue to make purchases and will increase the results of achieving good sales. Based on this problem, how will this research apply the classification method, so that when we can make predictions from supermarket sales data for member and non-member customers, there will be a lot of insight for the marketing team, so that marketing activities are right on target for member or non-member customers. This research uses machine learning methods for data classification, using the Support Vector Machine (SVM) and Naïve Bayes algorithms. The results of this research are from the Support Vector Machine (SVM) algorithm. Accuracy is 0.493 while using the Naïve Bayes algorithm is 0.535. From the results of this research, the use of the Naïve Bayes algorithm is better than SVM so that it can approach the prediction of member and non-member customer classification in supermarket data in this research.
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References
Achyani, Y. E. (2017). Prediksi Pemasaran Langsung Menggunakan Metode Support Vector Machine. Jurnal Teknik Komputer, III(2), 2–7. https://ejournal.bsi.ac.id/ejurnal/index.php/jtk/article/download/1719/1503
Adnyana, I. made B. (2019). Penerapan Feature Selection untuk Prediksi Lama Studi Mahasiswa. Jurnal Sistem Dan Informatika, 13, 72–76. https://jsi.stikom-bali.ac.id/index.php/jsi/article/view/211/170
Akanmu Semiu Ayobami, S. R. (2012). Knowledge Discovery in Database : A knowledge management strategic Knowledge Discovery in Database : A knowledge management strategic approach. Proceedings of 6th Knowledge Management International Conference (KMICe), July 2012, 6–11. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2088965
Amsury, F., Ruhyana, N., & Mardiana, T. (2022). Comparison Of Classification Algorithms For Analysis Sentiment Of Formula E Implementation In Indonesia. Jurnal Riset Informatika, 4(3), 291–298. https://doi.org/10.1109/siu.2012.6204469
Chrisdiyanti, I. N., Fa’rifah, R. Y., & ... (2023). Klasifikasi Review Customer Di E-Commerce Bukalapak Menggunakan Metode Support Vector Machine (SVM). eProceedings …, 10(3), 3200–3206. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20576%0Ahttps://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20576/19889
Indrayana, Y. K., Ramadhan, R. K., & ... (2023). Implementasi Decision Tree untuk Mengklasifikasikan Metode Pembayaran di Supermarket. Prosiding Seminar …, November, 43–53. https://ojs.amikomsolo.ac.id/index.php/semnasa/article/view/86%0Ahttps://ojs.amikomsolo.ac.id/index.php/semnasa/article/download/86/5
Indriyani Indriyani, & Agus Bahtiar. (2023). Implementasi Data Mining Untuk Mengklasifikasikan Data Penjualan Pada Supermarket Menggunakan Algoritma Naïve Bayes. Jurnal Manajemen Dan Bisnis Ekonomi, 1(1), 207–220. https://doi.org/10.54066/jmbe-itb.v1i1.70
Kojongian, V., Lapian, J., & Lumanauw, B. (2021). Pengaruh Bauran Pemasaran Terhadap Minat Beli Konsumen Di Cool Supermarket Tomohon. Jurnal EMBA, 9(4), 811–820. https://ejournal.unsrat.ac.id/index.php/emba/article/view/36618
Liao, S. H., & Yang, L. L. (2020). Mobile payment and online to offline retail business models. Journal of Retailing and Consumer Services, 57(151), 102230. https://doi.org/10.1016/j.jretconser.2020.102230
Maya Nur Annisaa, Nuryasman MN, & Ira Geraldina. (2023). Factors Affecting The Choice Of Payment Method In Modern Retail Shops. Jurnal Ekonomi, 28(3), 327–348. https://doi.org/10.24912/je.v28i3.1780
Monika, I. P., & Furqon, M. T. (2018). Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(10), 3165–3166. http://j-ptiik.ub.ac.id
Nengah Widya Utami, & I Wayan Budi Suryawan. (2021). Implementasi Data Mining Untuk Mengklasifikasikan Produk Pada Sebuah Supermarket Mengunakan Algoritma Id3 Pada Orange. Smart Techno (Smart Technology, Informatics and Technopreneurship), 3(1), 33–36. https://doi.org/10.59356/smart-techno.v3i1.33
Noviyanto, N. (2020). Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia. Paradigma - Jurnal Komputer dan Informatika, 22(2), 183–188. https://doi.org/10.31294/p.v22i2.8808
Nurelasari, E. (2019). Segmentasi Dan Klasifikasi Perilaku Pembayaran Pelanggan Pada Perusahaan Multimedia Dengan Algoritma K-Means Dan C4.5. XXI(1), 69–76. https://doi.org/10.31294/p.v20i2
Putri Ayu Mardhiyah, Riki Ruli A Siregar, P. P. (2020). Klasifikasi Untuk Memprediksi Pembayaran Kartu Kredit Macet Menggunakan Algoritma C4.5 Putri. Jurnal Teknologia, 3(1), 91–101. https://aperti.e-journal.id/teknologia/article/view/66/44
Refo, Y., Rostianingsih, S., & Liliana, L. (2022). Penerapan SVM untuk Klasifikasi Sentimen pada Review Comment Berbahasa Indonesia di Online Shop. Jurnal Infra, Vol 10, No, 1–6. https://publication.petra.ac.id/index.php/teknik-informatika/article/view/12813%0Ahttps://publication.petra.ac.id/index.php/teknik-informatika/article/download/12813/11113
Rifka Agustianti, Pandriadi, Lissiana Nussifera, Wahyudi, L. Angelianawati, Igat Meliana, Effi Alfiani Sidik, Qomarotun Nurlaila, Nicholas Simarmata, Irfan Sophan Himawan, Elvis Pawan, Faisal Ikhram, Astri Dwi Andriani, Ratnadewi, I. R. H. (2022). Metode penelitian kuantitatif & kualitatif. In N. M. Ni Putu Gatriyani (Ed.), Tohar Media (Cetakan Pe, Nomor Mi). TOHAR MEDIA.
Riyadi, A. A., Amsury, F., Ruhyana, N., & Rahman, I. A. (2022). Implementation of the Association Method in the Analysis of Sales Data from Manufacturing Companies. Jurnal Riset Informatika, 5(1), 593–598. https://doi.org/10.34288/jri.v5i1.491
Ruhyana, N., Mardiana, T., Amsury, F., & Sulistyowati, D. N. (2021). Classification of Student Satisfaction With Online Lecture. Jurnal Riset Informatika, 4(1), 105–110. https://doi.org/10.34288/jri.v4i1.299
Wardhana, A. W., Patimah, E., Shafarindu, A. I., Siahaan, Y. M., Haekal, B. V., & Prasvita, D. S. (2021). Klasifikasi Data Penjualan pada Supermarket dengan Metode Decision Tree. Senamika, 2(1), 660–667. https://conference.upnvj.ac.id/index.php/senamika/article/view/1389
Woro Isti Rahayu, Cahyo Prianto, E. A. N. (2021). Perbandingan Algoritma K-Means dan Naïve Bayes Untuk Memprediksi Prioritas Pembayaran Tagihan Rumah Sakit Berdasarkan Tingkat Kepentingan Pada PT. PERTAMINA (PERSERO). Jurnal Teknik Informatika, 13(2), 1–8. https://ejurnal.poltekpos.ac.id/index.php/informatika/article/view/1383/809
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