FAUZAN, AHMAD (2023) IMPLEMENTASI ALGORITMA APRIORI PADA PENJUALAN MINUMAN COFFEE DAN MAKANAN DI HAIIYOU KAFE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Nowadays, the growth of umkm cafes is starting to become an everyday sight. It can be seen by the many existence of cafes in Indonesia. Various kinds, such as coffee shops, coffee shops, and even cafes are increasingly spreading in all walks of life, especially among millennials. The data used is within 5 months, from August to December 2022 obtained from the owner of Haiiyou Cafe with a total of 8,702 raw data and has variable menus, prices, number of transactions. The results of the analysis using the Apriori Algorithm are obtained after using different minimum support and minimum confidence comparisons based on existing transaction data using a minimum support of 20% (the strength of the combination of these items in the database) and a minimum confidence of 80% (the strong relationship between items in the database). association rules) generates 3186 association rules. One example is if consumers buy A1(Americano) then 100% (consumer certainty in buying item 2) will buy A3(Banana Cookies), then if consumers buy A1(Americano) and A3(Banana Cookies) then 100% (consumer certainty in buying item 3) will buy A47(Sate Taichan) . From the results of the rules that have been obtained, it can be seen which menus are often purchased simultaneously by each consumer. This information can be useful to increase sales, namely by knowing what menus are often purchased by consumers, so that with this Haiiyou Café can make business decisions by determining menu package recommendations. Keywords: Data Mining, Apriori Algorithm, Association Rules Di masa sekarang ini pertumbuhan umkm café mulai menjadi suatu pemandangan sehari-hari. Dapat dilihat dengan banyaknya keberadaan cafe – cafe di Indonesia. Berbagai , seperti kedai kopi, coffee shop, bahkan kafe semakin menyebar di segala lapisan masyarakat, khususnya di kalangan milenial. Data yang digunakan yaitu dalam kurun waktu 5 bulan yaitu dari bulan Agustus sampai Desember 2022 yang didapat dari owner Haiiyou Kafe dengan jumlah data mentah 8.702 dan mempunyai variable menu, harga, jumlah transaksi. Hasil analisis menggunakan Algoritma Apriori yang didapatkan setelah menggunakan perbandingan minimum support dan minimum confidence yang berbeda-beda berdasarkan data transaksi yang ada adalah dengan menggunakan minimum support 20% (kuatnya kombinasi item tersebut dalam database) dan minimum confidence 80% (kuatnya hubungan antar item dalam aturan asosiasi) menghasilkan 3186 aturan asosiasi. Salah satu contohnya yaitu jika konsumen membeli A1(Americano) maka 100% (kepastian konsumen dalam membeli item 2) akan membeli A3(Banana Cookies), lalu jika konsumen membeli A1(Americano) dan A3(Banana Cookies) maka 100%(kepastian konsumen dalam membeli item 3) akan membeli A47(Sate Taichan) . Dari data hasil aturan yang telah diperoleh, dapat diketahui menu apa saja yang sering dibeli secara bersamaan oleh setiap konsumen. Informasi ini dapat berguna untuk menaikkan penjualan yaitu dengan mengetahui Menu apa saja yang sering dibeli oleh konsumen, sehingga dengan hal tersebut pihak Haiiyou Kafe dapat membuat keputusan bisnis dengan menentukan rekomendasi paket menu. Kata Kunci : Data Mining, Algoritma Apriori, Association Rules
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 053 |
| NIM/NIDN Creators: | 41519010035 |
| Uncontrolled Keywords: | Data Mining, Algoritma Apriori, Association Rules |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | ADELINA HASNA SETIAWATI |
| Date Deposited: | 22 Jul 2023 04:14 |
| Last Modified: | 22 Jul 2023 04:14 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/79474 |
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