Prediksi Penjualan Brand di HGVR Store Menggunakan Algoritma C4.5 dan Naïve Bayes | Jurnal Informatika Ekonomi Bisnis

Journal Content

Search

Search Scope

Browse

Information

Current Issue

Template
Similarity Checker

Member of

Statistics

Prediksi Penjualan Brand di HGVR Store Menggunakan Algoritma C4.5 dan Naïve Bayes

Candra Naya Universitas Pelita Bangsa Elkin Rilvani Universitas Pelita Bangsa DOI: https://doi.org/10.37034/infeb.v7i3.1242 Keywords: HGVR Brand, Data Mining, Sales Prediction, C4.5 Algorithm, Naïve Bayes Abstract HGVR Brand is a creative industry engaged in the production and distribution of ready-to-wear clothing established in 2015, which has a reseller network in several major cities in Java. This study aims to analyze the prediction of HGVR Store product sales levels using data mining methods, specifically the C4.5 and Naïve Bayes algorithms, so that it can assist the company in determining marketing strategies and inventory management. The data used in this study consists of 500 sales data collected in June 2019 through observation, interviews, and internal company documentation. The input variables used include the number of orders (PO), quantity, price, and sales status, while the target variable is the classification of sales into "high" and "low" categories. The analysis process is carried out through the stages of data cleaning, transformation, and validation using the split validation technique (70% training data and 30% testing data). The C4.5 algorithm is used to build a decision tree model, while the Naïve Bayes algorithm is used to calculate the classification probability. The test results show that the C4.5 algorithm has a 100% accuracy rate with an excellent classification category based on the ROC curve (AUC = 1.00). Meanwhile, the Naïve Bayes algorithm also produced good classification results, although its accuracy was lower than that of C4.5. The conclusion of this study is that the C4.5 algorithm is more optimal than Naïve Bayes in predicting sales levels at the HGVR Store. These findings are expected to inform decision-making for the HGVR Brand in formulating business strategies. Downloads Download data is not yet available. References Salsabila, S. M., Alim Murtopo, A., & Fadhilah, N. (2022). Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier. Jurnal Minfo Polgan, 11(2), 30–35. DOI: https://doi.org/10.33395/jmp.v11i2.11640 . Aditya Restu Hapriyanto. (2024). Strategi Inovatif dalam Meningkatkan Daya Saing Bisnis di Era Digital. Nusantara Journal of Multidisciplinary Science, 2(1), 115–124. DOI: https://doi.org/10.60076/njms.v2i1.255 . Khairunnisa, C. M. (2022). Pemasaran Digital sebagai Strategi Pemasaran: Conceptual Paper. JAMIN : Jurnal Aplikasi Manajemen Dan Inovasi Bisnis, 5(1), 98. DOI: https://doi.org/10.47201/jamin.v5i1.109 . Aditya, R. (2021). Infrastruktur Cloud Pintar dalam Sistem Layanan Informasi Berbasis Big Data. INTEGRATED (Journal of Information Technology and Vocational Education), 3(1), 29–38. DOI: https://doi.org/10.17509/integrated.v3i1.64423 . Anggita, S. D., & Ikmah, I. (2021). Implementasi PSO untuk Optimasi Bobot Atribut pada Algoritma C4.5 dalam Prediksi Kelulusan Mahasiswa. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 6(2), 416–423. DOI: https://doi.org/10.29100/jipi.v6i2.2440 . Rifky, L., Nugraha, Z., Saputra, B., Pratama, D., Raswir, E., & Pratama, Y. (2022). Implementasi Data Mining untuk Penjualan Mobil Menggunakan Metode Naive Bayes. Jurnal Informatika dan Rekayasa Komputer(JAKAKOM), 2(2), 225–230. DOI: https://doi.org/10.33998/jakakom.2022.2.2.109 . Arifin, N. B. A. B., & Asmianto, A. (2023). Sistem Prediksi Penjualan Menggunakan Kombinasi Metode Monte Carlo dan Decision Tree Berbasis Website. MATHunesa: Jurnal Ilmiah Matematika, 11(2), 274–286. DOI: https://doi.org/10.26740/mathunesa.v11n2.p274-286 . Pendra Mahardika, I. M. (2015). Pengembangan Sistem Otomasi Pengolahan Koleksi Karya Ilmiah Mahasiswa Berbasis Web untuk Meningkatkan Kualitas Layanan Perpustakaan (Studi Kasus : Universitas Pendidikan Ganesha). JST (Jurnal Sains dan Teknologi), 4(1). DOI: https://doi.org/10.23887/jst-undiksha.v4i1.4932 . Lumbanraja, F. R., Lufiana, F., Heningtyas, Y., & Muludi, K. (2022). Implementasi Support Vector Machine (Svm) untuk Klasifikasi Pederita Diabetes Mellitus. Jurnal Komputasi, 10(1), 75–83. DOI: https://doi.org/10.23960/komputasi.v10i1.2940 . Djamaludin, I., & Nursikuwagus, A. (2017). Analisis Pola Pembelian Konsumen pada Transaksi Penjualan Menggunakan Algoritma Apriori. Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 8(2), 671. DOI: https://doi.org/10.24176/simet.v8i2.1566 . Asri, K. H. (2022). Pengembangan Ekonomi Kreatif di Pondok Pesantren Melalui Pemberdayaan Kewirausahaan Santri Menuju Era Digital 5.0. ALIF, 1(1), 17–26. DOI: https://doi.org/10.37010/alif.v1i1.710 . Asri, K. H. (2022). Pengembangan Ekonomi Kreatif di Pondok Pesantren Melalui Pemberdayaan Kewirausahaan Santri Menuju Era Digital 5.0. ALIF, 1(1), 17–26. DOI: https://doi.org/10.37010/alif.v1i1.710 . Aldisa, R. T., Nugroho, F., Mesran, M., Sinaga, S. A., & Sussolaikah, K. (2022). Sistem Pendukung Keputusan Menentukan Sales Terbaik Menerapkan Metode Simple Additive Weighting (SAW). Journal of Information System Research (JOSH), 3(4), 548–556. DOI: https://doi.org/10.47065/josh.v3i4.1955 . Sarwoko, E. (2008). Dampak Modernisasi Keberadaan Pasar Modern terhadap Pedagang Pasar Tradisional di Wilayah Kabupaten Malang. Jurnal Ekonomi Modernisasi, 4(2), 97–115. DOI: https://doi.org/10.21067/jem.v4i2.880 . Defrita Rufikasari, Y. (2023). Telaah Teologi, Ekonomi dan Ekologi terhadap Fenomena Fast Fashion Industry. Teologis-Relevan-Aplikatif-Cendikia-Kontekstual, 1(2), 64–83. DOI: https://doi.org/10.61660/tep.v1i2.23 . Arjang, A., Harwin, H., Hamid, W., & Jaya, A. R. (2019). Pelatihan Marketing Strategi Tenaga Pemasaran Guna Pencapaian Target Penjualan. BAKTIMAS : Jurnal Pengabdian Pada Masyarakat, 1(4), 212–217. DOI: https://doi.org/10.32672/btm.v1i4.1723 . Rerung, R. R. (2018). Penerapan Data Mining dengan Memanfaatkan Metode Association Rule untuk Promosi Produk. Jurnal Teknologi Rekayasa, 3(1), 89. DOI: https://doi.org/10.31544/jtera.v3.i1.2018.89-98 . Reza, F. (2016). Strategi Promosi Penjualan Online Lazada.Co.Id. Jurnal Kajian Komunikasi, 4(1), 63. DOI: https://doi.org/10.24198/jkk.v4i1.6179 . Steiner, A., & Teasdale, S. (2019). Unlocking the Potential of Rural Social Enterprise. Journal of Rural Studies, 70, 144–154. DOI: https://doi.org/10.1016/j.jrurstud.2017.12.021 . Astuti, Y., & Novitasari, H. (2022). Algoritma Apriori sebagai Penentu Pola Penjualan Produk Jeans. Jurnal Ilmiah Edutic : Pendidikan dan Informatika, 9(1), 20–28. DOI: https://doi.org/10.21107/edutic.v9i1.7416 . Arifiyani, F. C., & Pramaditya, H. (2023). Peningkatan Efektivitas Pemasaran pada Usaha Retail Melalui Digitalisasi Katalog dengan Microsite. Journal of Information System and Application Development, 1(1), 19–28. DOI: https://doi.org/10.26905/jisad.v1i1.9860 . Prabowo, D., Hidayat, F., Gumelar, G., Qintoro, D., & Setiawan, A. (2023). Perbandingan Algoritma Naïve Bayes dan C4.5 dalam Menentukan Tingkat Penjualan Motor Honda. Jurnal Informatika Komputer, Bisnis dan Manajemen, 16(3), 67–76. DOI: https://doi.org/10.61805/fahma.v16i3.91 .	Download Published 2025-09-13 Issue Vol. 7, No. 3 (September 2025) Section Articles How to Cite Naya, C., & Rilvani, E. (2025). Prediksi Penjualan Brand di HGVR Store Menggunakan Algoritma C4.5 dan Naïve Bayes. Jurnal Informatika Ekonomi Bisnis, 7(3), 646-652. https://doi.org/10.37034/infeb.v7i3.1242 ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexing and Abstractions:

Published:

This work is licensed under a Creative Commons Attribution 4.0 International Public License (CC BY 4.0).

Web Analytics Made Easy -
StatCounter