EVA INDRIANI RITONGA, NPM 2109100163 (2025) ANALISIS DATA PENJUALAN PUPUK DI UD JAYA TANI MENGGUNAKAN METODE APRIORI. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Persediaan merupakan salah satu faktor penting dalam menjaga kelancaran operasional suatu usaha perdagangan, termasuk UD Jaya Tani yang bergerak di bidang pertanian. Untuk memastikan ketersediaan stok sesuai dengan kebutuhan konsumen, diperlukan analisis pola belanja konsumen dengan pendekatan yang tepat. Data mining khususnya algoritma Apriori merupakan suatu metode yang mampu mengidentifikasi hubungan antar item berdasarkan frekuensi kemunculannya dalam data transaksi. Apriori bekerja dengan menghitung nilai support dan confidence untuk menemukan asosiasi antar item yang sering dibeli bersamaan. Penelitian ini menggunakan pendekatan kuantitatif dengan tahapan mulai dari pengumpulan data transaksi sebanyak 150, preprocessing ke dalam bentuk tabel biner, hingga perancangan model pada aplikasi RapidMiner. Berdasarkan pengolahan data dengan minimum support 50% dan confidence 70% ditemukan beberapa association rule yang dapat dijadikan dasar dalam penyediaan barang. Hasil tersebut menunjukkan bahwa beberapa barang sering kali dibeli secara bersamaan, sehingga dapat membantu toko dalam menyiapkan stok yang lebih tepat sasaran. Penelitian ini diharapkan dapat menjadi acuan dalam pengambilan keputusan penyediaan barang dan meminimalisir risiko kehabisan atau penumpukan barang. Kata Kunci : Data Mining, Algoritma Apriori, Support, Confidence ==================================================================================================== Inventory is one of the important factors in maintaining the smooth operation of a trading business, including UD Jaya Tani which is engaged in agriculture. To ensure the availability of stock according to consumer needs, an analysis of customer shopping patterns using the right approach is needed. Data mining, especially the Apriori algorithm, is a method that is able to identify relationships between items based on the frequency of their appearance in transaction data. Apriori works by calculating support and confidence values to find associations between items that are often purchased together. This study uses a quantitative approach with stages starting from collecting 150 transaction data, preprocessing into a binary tabular form, to designing a model in the RapidMiner application. Based on data processing with a minimum support of 50% and confidence of 70%, several association rules were found that can be used as a basis for providing goods. These results indicate that several items are often purchased together, so that they can help stores prepare more targeted stock. This study is expected to be a reference in making decisions about providing goods and minimizing the risk of running out or stockpiling. Keywords : Data Mining, Apriori Algorithm, Support, Confidence
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Data Mining, Algoritma Apriori, Support, Confidence ============================================ Data Mining, Apriori Algorithm, Support, Confidence |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 20 May 2025 08:39 |
| Last Modified: | 20 May 2025 08:39 |
| URI: | http://repository.ulb.ac.id/id/eprint/1412 |
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