FADZLIN, JULIAN NUR (2025) REKOMENDASI PAKET MENU AMNESIA MENGGUNAKAN KOMBINASI ALGORITMA K-MEANS DAN FP-GROWTH. S1 thesis, Universitas Mercu Buana - Menteng.
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Abstract
Kafe Amnesia menghadapi tantangan dalam merancang strategi yang lebih efektif untuk meningkatkan penjualan melalui paket menu yang relevan. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian pelanggan menggunakan kombinasi algoritma K-Means untuk klasterisasi dan FP-Growth untuk analisis asosiasi. Dataset transaksi penjualan selama 8 bulan digunakan dalam penelitian ini, mencakup 8.313 transaksi dengan atribut seperti kode item, jumlah, dan total pembayaran. Proses penelitian dimulai dengan pengolahan data awal, dilanjutkan dengan penerapan algoritma K-Means untuk mengelompokkan data menjadi empat cluster berdasarkan pola pembelian yang teridentifikasi. Selanjutnya, analisis asosiasi dilakukan menggunakan algoritma FP-Growth untuk menemukan frequent itemsets dan association rules yang relevan dalam setiap cluster. Evaluasi kualitas klaster dilakukan menggunakan Davies-Bouldin Index (DBI) dan Silhouette Score, yang menunjukkan hasil clustering yang memadai. Hasil analisis menunjukkan bahwa setiap cluster memiliki karakteristik unik. Klaster pertama didominasi oleh preferensi minuman kopi dan mocktail tanpa pola pembelian bersamaan signifikan. Klaster kedua menonjol dengan hubungan kuat antara Pisang Keju dan Spaghetti Amnesia (lift 7.25, confidence 99.06%). Klaster ketiga memperlihatkan kombinasi seperti Chicken Skin dan Lemon Tea (lift 2.35, confidence 99.44%), serta Nasi Telur Dadar Beredar, Lychee Squash, dan Otak-Otak (lift 2.40, confidence 53.20%). Klaster keempat didominasi oleh Spaghetti Amnesia dan Red Black Summer (lift 2.43, confidence 99.57%). Penelitian ini memberikan rekomendasi paket menu untuk strategi penjualan yang lebih efektif dan berkontribusi pada inovasi dalam industri kuliner melalui pemanfaatan data transaksi. Amnesia Cafe faces challenges in designing more effective strategies to increase sales through relevant menu packages. This study aims to identify customer purchasing patterns using a combination of the K-Means algorithm for clustering and the FP- Growth algorithm for association analysis. The sales transaction dataset over 8 months was utilized in this study, consisting of 8,313 transactions with attributes such as item codes, quantities, and total payments. The research process began with data preprocessing, followed by applying the K- Means algorithm to group data into four clusters based on identified purchasing patterns. Subsequently, association analysis was performed using the FP-Growth algorithm to uncover frequent itemsets and relevant association rules within each cluster. The clustering quality was evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score, indicating satisfactory results. The analysis results reveal that each cluster has unique characteristics. The first cluster is dominated by preferences for coffee and mocktail beverages without significant co- purchasing patterns. The second cluster highlights a strong association between Pisang Keju and Spaghetti Amnesia (lift 7.25, confidence 99.06%). The third cluster reveals combinations such as Chicken Skin and Lemon Tea (lift 2.35, confidence 99.44%), as well as Nasi Telur Dadar Beredar, Lychee Squash, and Otak-Otak (lift 2.40, confidence 53.20%). The fourth cluster is dominated by Spaghetti Amnesia and Red Black Summer (lift 2.43, confidence 99.57%). This study provides menu package recommendations to develop more targeted and effective sales strategies and contributes to the culinary industry by leveraging transaction data for innovation and improving customer experiences.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520120052 |
| Uncontrolled Keywords: | Paket Menu, K-Means, FP-Growth, Analisis Pola Pembelian, Strategi Penjualan Kafe Menu Package, K-Means, FP-Growth, Purchasing Pattern Analysis, Cafe Sales Strategy |
| 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | FHADHILAH SHAFA ARISTA |
| Date Deposited: | 31 Jan 2025 05:03 |
| Last Modified: | 31 Jan 2025 05:03 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/93790 |
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