Advancements in information technology have transformed sales transaction data from mere administrative records into valuable sources of information for decision-making. At UD. Pratama, fertilizer sales transaction data has not yet been optimally utilized to identify customer purchasing patterns or the relationships between products frequently purchased together. This situation complicates the formulation of sales strategies, inventory management, and the provision of product recommendations. This study aims to apply the Apriori method to discover fertilizer purchasing patterns and identify the "best rule" based on the generated association rules. The study employs the Apriori algorithm—a data mining technique based on the association rule concept—using UD. Pratama’s fertilizer sales transaction data from 2025, which covers various fertilizer types. The research stages include preparation, literature review, data collection, data analysis, system testing and implementation, and a final phase. Transaction data underwent selection, cleaning, and transformation into binary format before being analyzed using minimum support and minimum confidence thresholds to derive frequent itemsets and association rules. The study resulted in a web-based system capable of processing fertilizer sales transaction data using the Apriori method. The system performs itemset generation, calculates product occurrence frequencies, generates association rules, and identifies the best rule based on the applied analysis parameters. The developed system presents information such as frequent itemsets, association rules, the best rule, analysis history, reports, and analysis results, thereby assisting UD. Pratama in understanding customer purchasing patterns and supporting decision-making regarding sales strategies and inventory management.
References
Anjar Permadi, (2023), Data Mining Pengelompokan Dataset Bus Biskita Di Kota Bogor Menggunakan Metode K -Means, Jurnal: Manajemen Informatika, STMIK IKMI Cirebon
Ardianti. (2023). Penerapan Algoritma Apriori Menentukan Korelasi Data Penjualan Pupuk (Studi Kasus : PT. Karunia Rotorindo Tani) . Jurnal : Sistem Informasi, STMIK KAPUTAM
Dimas Prayuda. (2024). Penerapan Data Mining Untuk Pola Penjualan Pupuk Menggunakan Algoritma Apriori . Jurnal: Sistem Informasi, STMIK Triguna Dharma
Lamhot Sitorus, 2015, Algoritma dan Pemrograman, Yogyakarta: Andi Ofset
Muh. Ardiansyah, Indah Puji Astuti dan Ghulam Asrofi Buntoro, (2022), Analisis Minat Siswa Siswi Smp/Mts Terhadap Ekstrakurikuler Ma Muhammadiyah 1 Ponorogo Menggunakan Metode Association Rules Berbasis Web, Jurnal: Teknik Informatika, Universitas Muhammadiyah Ponorogo
Muhammad Arhami dan Muhammad Natsir, 2020, Data Mining Algoritma dan Implementasi, Yogyakarta: CV. Andi Ofset
Prasetyowati, Erwin. 2017. Data Mining Pengelompokan Data untuk Informasi dan Evaluasi. Pamekasan: Duta Media Publishing.
Rezky Randy, (2023), Penerapan Data Mining Untuk Menentukan Pola Penjualan Menggunakan Metode Algoritma Apriori , Jurnal: Teknik Informatika, Universitas Pamulang, Tangerang Selatan
Sugiyono. (2016). Metode Penelitian Kuantitatif, Kualitatif dan R&D, Cetakan ke-24. Bandung: Alfabeta
Yuli Mardi, 2017, Data Mining Klasifikasi Menggunakan Algoritma C4.5 , Jurnal: APIKES Iris Padang
Werdiningsih, I., Nuqoba, B., & Muhammadun. (2020). Data Mining Menggunakan Android, Weka, Dan Spss (p. 209). Airlangga University Press.
Prastyadi Wibawa Rahayu, I Nyoman Bernadus dan Aulia Iefan Datya, (2024), Penerapan Data Mining Dalam Memahami Pola Transaksi Pembelian Obat Dengan Menggunakan Algoritma Apriori Di Apotek Kharisma Farma Tiga, Jurnal: Teknologi dan Informatika, Universitas Dhyana Pura, Bali
Windy Indah Wahyuni, Yusfrizal dan Zira Fatmaira, (2024), Data Mining Correlation Of Student Discipline With Level Of Achievement Using Apriori Method, Jurnal: Sistem Informasi, STMIK Kaputama Binjai
Wiranto Hernandesz Sirait. (2022). Penerapan Algoritma Apriori dalam Menentukan Pola Penjualan Produk Pertanian. Jurnal : STIKOM Tunas Bangsa, Pematangsiantar, Indonesia