Penerapan Data Mining Untuk Memprediksi Ketersediaan Stok Produk HNI HPAI Menggunakan Algoritma C4.5

Murlena Murlena, Diwi Apriana

Abstract


In a company, the availability of this stock must be considered to avoid product losses caused by expiration and also to avoid order cancellations. The data contained in the HPAI company application, called the HSIS application, can be utilized and applied for data mining in determining which products are in demand in everyday life. This HSIS application can be used by the Business Center (BC) and all Stock Agents in the HPAI HNI business. To maintain the availability of product stock, the purpose of this study is to test the Stock Agent using the C4.5 Algorithm to predict that the availability of products that must be maintained is in accordance with customer needs. The use of the C4.5 algorithm on stock agents can overcome stockpiling and finance or stock agents capital can be used to buy stock that must be available. The results of the analysis using the C4.5 algorithm stated that although the price of the product was expensive, it was still selling well as herbal products. So of the three types of HPAI HNI products (Herbs product, Health food & beverage and Cosmetic & home care), the stock that must be increased or available is the Herbs Product.

Keywords


Data Mining; Algorithm C4.5; Stock, HPAI

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References


Fikri, Abdul, and Wiwi Verina. 2020. “Penerapan Data Mining Untuk Prediksi Penjualan Alat Medis Menggunakan Algoritma C4.5 PT. Murni Indah Sentosa.” Infosys (Information System) Journal 5 (1): 70–83. https://doi.org/10.22303/infosys.5.1.2020.70-83.

Jumiyati, and Putri Taqwa Prasetyaningrum. 2020. “Penerapan Algoritma C4.5 Untuk Mengklasifikasi Hasil Produksi Kunir Putih (Studi Kasus CV Windra Mekar).” Journal Of Information System And Artificial Intelligence 1 (1): 9–16. http://jisai.mercubuana-yogya.ac.id/index.php/jisai/article/view/30.

Kusrini, Kusrini, and Emha Taufiq Luthfi. 2009. Algoritma Data Mining. Yogyakarta: Andi Offset.

Larose, Daniel T. 2015. Data Mining and Predictive Analytics. John Wiley & Sons.

Minarni, S. Si, and S. T. Rahmad Hidayat. 2013. “Rancang Bangun Aplikasi Sistem Pakar Untuk Kerusakan Komputer Dengan Metode Backward Chaining.” Jurnal Teknoif Teknik Informatika Institut Teknologi Padang 1 (1): 26–35. https://doi.org/10.21063/jtif.2013.V1.1.26-35.

Murlena, Murlena, and Wandi Syahindra. 2020. “Data Mining Pengolahan Penempatan Library Books Menggunakan Metode Association Rule Dengan Algoritma Apriori.” Jurnal INSTEK (Informatika Sains Dan Teknologi) 5 (2): 199–208. https://doi.org/10.24252/instek.v5i2.16203.

Nofriansyah, Dicky, and Gunadi Widi Nurcahyo. 2015. Algoritma Data Mining Dan Pengujian. Vol. viii. Yogyakarta: Deepublsih.

Pritalia, Generosa Lukhayu. 2018. “Penerapan Algoritma C4.5 Untuk Penentuan Ketersediaan Barang E-Commerce.” Indonesian Journal of Information Systems 1 (1): 47–56. https://doi.org/10.24002/ijis.v1i1.1727.

Renhoran, Bahrun Said, Nova Nurhandayani, and Laila Septiana. 2018. “Penerapan Algoritma C4.5 Untuk Menentukan Data Stok Dan Target Permintaan Material Yang Paling Dibutuhkan Gudang Logistik Pada Pt.Pln (Persero) Area Kebon Jeruk.” INTI Nusa Mandiri 12 (2): 13–20. https://doi.org/10.33480/inti.v12i2.1564.

Widyastuti, Rini, Karmila Suryani, Ade Fitri Rahmadani, Triadmoko Denny Fatrosa, and Wandi Syahindra. 2021. “Matrix Integration with Jitsi Conference Server for Online Learning.” Knowbase : International Journal of Knowledge in Database 1 (2): 106–15. https://doi.org/10.30983/ijokid.v1i2.5040.




DOI: http://dx.doi.org/10.29240/arcitech.v2i1.5271

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