Ihsan, Muhammad Awaluddin Azhari
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PENERAPAN ALGORITMA APRIORI UNTUK ANALISIS POLA PEMILIHAN MENU DI RH STORE: IMPLEMENTATION OF THE APRIORI ALGORITHM FOR ANALYZING MENU SELECTION PATTERNS AT RH STORE Pratama, Dimas Limanov; Kristy, Natasya; Saputra, Rayhan Daffananda; Ihsan, Muhammad Awaluddin Azhari; Amsury, Fachri; Supendar, Hendra
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p19-28

Abstract

Fluktuasi penjualan yang dialami RH Store menunjukkan perlunya pemanfaatan data transaksi secara optimal untuk mendukung pengambilan keputusan bisnis. Selama ini, data transaksi penjualan belum dimanfaatkan secara maksimal untuk mengidentifikasi pola pemilihan menu pada data transaksi penjualan RH Store. Metode yang digunakan adalah pendekatan kuantitatif deskriptif dengan mengikuti tahapan Knowledge Discovery in Databases (KDD), meliputi seleksi data, pembersihan data, transformasi ke bentuk market basket, serta pembentukan aturan asosiasi. Data yang digunakan berupa 101 transaksi penjualan pada periode Juli hingga September 2025 dan dianalisis menggunakan aplikasi Orange Data Mining. Pengujian dilakukan dengan beberapa kombinasi nilai support dan confidence, yaitu 30%-60%, 40%-80%, dan 50%-90%. Hasil penelitian menunjukkan bahwa pada nilai support 50% dan confidence 90% diperoleh 17 aturan asosiasi dengan nilai confidence tertinggi sebesar 95% dan seluruh nilai lift lebih besar dari 1. Produk Roti Tumpuk, Roti Bulat, dan Roti Kukus memiliki tingkat keterkaitan paling dan sering muncul sebagai consequent. Hasil analisis ini dapat dimanfaatkan sebagai dasar dalam penyusunan menu paket, strategi promosi, serta pengelolaan persediaan produk di RH Store.   Sales fluctuations experienced by RH Store indicate the need to optimize the use of transaction data to support business decision-making. To date, sales transaction data have not been fully utilized to identify menu selection patterns at RH Store. This study employs a descriptive quantitative approach following the Knowledge Discovery in Databases (KDD) stages, including data selection, data cleaning, transformation into a market basket format, and association rule generation. The dataset consists of 101 sales transactions collected from July to September 2025 and was analyzed using Orange Data Mining. Experiments were conducted using several combinations of support and confidence thresholds, namely 30%–60%, 40%–80%, and 50%–90%. The results show that at a support threshold of 50% and a confidence threshold of 90%, 17 association rules were generated, with the highest confidence value reaching 95% and all lift values exceeding 1. The products Roti Tumpuk, Roti Bulat, and Roti Kukus exhibit the strongest associations and frequently appear as consequents. These findings can be utilized as a basis for designing menu packages, promotional strategies, and inventory management at RH Store.