YULIA PRESTIAWATI, NPM 2209100143 (2026) PENERAPAN DATA MINING DENGAN MENGGUNAKAN ALGORITMA APRIORI DALAM MEPREDIKSI PENJUALAN BOLEN (STUDI KASUS : BOLEN BUNDA IBRA). Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Industri kuliner di Indonesia, khususnya sektor Usaha Kecil dan Menengah (UKM), mengalami perkembangan pesat namun menghadapi tantangan dalam mengolah data transaksi penjualan secara optimal. Bolen Bunda Ibra, sebagai salah satu pelaku usaha kuliner di Rantauprapat, masih mengandalkan intuisi dalam menentukan strategi penjualan dan pengelolaan stok, yang sering menyebabkan ketidaktepatan persediaan barang. Penelitian ini bertujuan menerapkan data mining dengan algoritma Apriori untuk mengidentifikasi pola pembelian konsumen (frequent itemset). Metode penelitian mengikuti tahapan Knowledge Discovery in Database (KDD) terhadap 187 transaksi periode 1- Mei 2025 hingga 1- November 2025. Analisis dilakukan menggunakan software RapidMiner dengan parameter minimum support 30% dan minimum confidence 70%. Hasil penelitian berhasil membentuk aturan asosiasi pola pembelian sehingga bisa di prediksi penjualan bolen mana yang paling banyak terjual agar bisa di manfaatkan untuk menawarkan paket atau promo kombinasi agar lebih meningkatkan penjualan.sebagai dasar pengambilan keputusan strategis, seperti promosi paket produk dan optimalisasi pengelolaan stok guna meningkatkan efisiensi operasional bisnis Kata Kunci: Data Mining, Algoritma Apriori, Penjualan Bolen, Association Rules, RapidMiner ================================================================================================= The culinary industry in Indonesia, particularly the Small and Medium Enterprise (SME) sector, is experiencing rapid growth but faces challenges in optimally processing sales transaction data. Bolen Bunda Ibra, a culinary entrepreneur in Rantauprapat, still relies on intuition to determine sales strategies and manage inventory, which often leads to inaccurate inventory. This study aims to apply data mining with the Apriori algorithm to identify consumer purchasing patterns (frequent itemsets). The research method follows the Knowledge Discovery in Database (KDD) process for 187 transactions from May 1, 2025, to November 1, 2025. The analysis was conducted using RapidMiner software with parameters of minimum support of 30% and minimum confidence of 70%. The results successfully established association rules for purchasing patterns, allowing predictions of which bolen products sell best, which can be utilized to offer packages or combination promotions to further increase sales. This can be used as a basis for strategic decision-making, such as product package promotions and optimizing inventory management to improve business operational efficiency. Keywords: Data Mining, Apriori Algorithm, Bolen Sales, Association Rules, RapidMiner
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Data Mining, Algoritma Apriori, Penjualan Bolen, Association Rules, RapidMiner===============Data Mining, Apriori Algorithm, Bolen Sales, Association Rules, RapidMiner |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 09 Jul 2026 03:12 |
| Last Modified: | 09 Jul 2026 03:12 |
| URI: | http://repository.ulb.ac.id/id/eprint/2586 |
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