Analisis Data Transaksi Penjualan Obat di Apotek X Samarinda Menggunakan Algoritma Apriori dan FP-Growth Berbasis Association Rule Mining: Analysis of Drug Sales Transaction Data at Pharmacy X Samarinda Using Apriori and FP-Growth Algorithms Based on Association Rule Mining - Nurmayanti, Wiwit Pura; Hasanah, Siti Hadijah; Rahim, Abdul (Fakultas Farmasi, Universitas Mulawarman, Samarinda, Indonesia, 2024)
Association Rule Mining is a data mining technique that is used to search for a group of items that often appear together in an event and is often analogous to a market basket. Algorithms in the association rule include apriori and frequent pattern growth (fp-growth). We can apply these two algorithms in various fields, one of which is in the pharmaceutical sector, namely related to drug sales transactions in pharmacies. The aim of this research is to see a picture of drug sales transactions at Pharmacy X, Samarinda City, and to find out the best algorithm for determining drug sales transaction patterns at the pharmacy. Based on the results of the analysis, information was obtained that out of 100 drug sales transactions at Pharmacy The product that consumers purchased the most was the ChargeR type of medicine, namely 7 transactions and the one that was purchased the least was Grape Tempra Syrup 60 ml which was purchased in only 1 transaction, and seen from the higher support and confidence values, the fp-growth algorithm could produce rules better to the apriori algorithm. Keywords: association rule, fp-growth algorithm, apriori algorithm, pharmacies Abstrak Association Rule Mining merupakan teknik data mining untuk menemukan aturan asosiasi antara suatu kombinasi item. Algoritma dalam Association Rule dapat diterapkan diberbagai bidang, salah satunya adalah bidang farmasi terkait transaksi penjualan obat di Apotek, adapun algoritma tersebut adalah Apriori dan Frequent pattern Growth (Fp-Growth). Tujuan penelitian ini adalah untuk melihat gambaran transaksi penjualan obat di Apotek X Kota Samarinda, dan mengetahui algoritma terbaik dalam menentukan pola transaksi penjualan obat di apotek tersebut. Berdasarkan hasil analisis diperoleh informasi bahwa dari 100 transaksi penjualan obat di Apotek X kota Samarinda, obat yang paling banyak terjual dalam 1 transaksi terdapat pada transaksi ke 41 dengan jenis obat sebanyak 17 jenis. Produk yang paling banyak dibeli konsumen adalah jenis obat ChargeR yaitu sebanyak 7 transaksi dan yang paling sedikit dibeli adalah Sirup Tempra Anggur 60 ml yang dibeli hanya dalam 1 transaksi, dan dilihat dari nilai support dan confident yang lebih tinggi algoritma fp-growth mampu menghasilkan aturan algoritma yang lebih baik dibandingkan dengan algoritma apriori. Kata Kunci: association rule, apriori, frequent pattern growth, apotek