WIGUNA, ABID PRADIPTA (2026) PENERAPAN ALGORITMA APRIORI UNTUK ANALISIS KOMBINASI MENU MENGGUNAKAN DATA TRANSAKSI KEDAI SUSU GBK. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The ongoing digital transformation across various business sectors has also reshaped the culinary industry, particularly through the adoption of menu recommendation systems designed to enhance service quality and boost sales revenue. Such systems allow customers to discover menu combinations that are frequently ordered together based on prior purchasing patterns. This study aims to apply the Apriori method within a web-based ordering platform for Kedai Susu GBK in order to generate menu bundle recommendations derived from accumulated customer transaction records. Historical sales data served as the foundation for constructing association rules, processed through several stages: data collection, preprocessing, identification of frequent itemsets using the Apriori technique, formulation of association rules based on support, confidence, and lift values, and finally, integration of the resulting rules into the web application. Architecturally, the system relies on Laravel as its core framework, while the recommendation computation is handled by a separate Flask-based API service. System validation was carried out using Black Box Testing to confirm that all functionalities operated according to specified requirements. The findings reveal that the Apriori-based approach successfully produced meaningful association patterns capable of supporting menu recommendations aligned with customer buying behavior. Through API-based communication between Laravel and Flask, these recommendations are automatically displayed on both the Dashboard and Shopping Cart pages without requiring manual intervention. Testing results further confirm that the complete workflow from data transmission to the recommendation service, through association rule processing, to the final presentation of recommendations functions correctly. This implementation is expected to enrich the customer ordering experience while simultaneously reinforcing cross-selling strategies for Kedai Susu GBK. Keywords: Apriori Algorithm, Association rules, Recommendation System, Laravel, Flask, REST API. Cross-selling Transformasi digital yang terjadi di berbagai bidang usaha turut memengaruhi sektor kuliner, salah satunya dalam bentuk penerapan sistem rekomendasi menu untuk mendukung peningkatan layanan dan omzet penjualan. Sistem semacam ini memungkinkan pelanggan memperoleh saran kombinasi menu yang biasanya dipesan secara bersamaan berdasarkan kebiasaan pembelian sebelumnya. Studi ini bertujuan menerapkan metode Apriori pada platform pemesanan berbasis web milik Kedai Susu GBK guna membangkitkan rekomendasi paket menu dari catatan transaksi pelanggan yang telah terkumpul. Sebagai dasar pembentukan aturan asosiasi, digunakan data penjualan historis yang diolah melalui beberapa tahapan, yakni pengumpulan data, pembersihan dan penyiapan data (preprocessing), penentuan itemset yang sering muncul dengan metode Apriori, penyusunan aturan asosiasi dengan mempertimbangkan nilai support, confidence, dan lift, hingga akhirnya diterapkan pada sistem web yang dibangun. Dari segi arsitektur, aplikasi ini memanfaatkan Laravel sebagai backbone sistem utama, sedangkan proses komputasi rekomendasi ditangani oleh layanan API berbasis . Untuk memvalidasi kesesuaian fungsi terhadap kebutuhan sistem, dilakukan pengujian dengan pendekatan Black Box Testing. Temuan penelitian memperlihatkan bahwa penerapan Apriori mampu menghasilkan pola-pola aturan asosiasi yang relevan sebagai dasar penyusunan rekomendasi menu sesuai kecenderungan belanja pelanggan. Melalui komunikasi API antara Laravel dan , hasil rekomendasi tersebut dapat langsung tersaji pada tampilan Dashboard maupun halaman Keranjang Belanja tanpa intervensi manual. Hasil pengujian juga mengonfirmasi bahwa keseluruhan alur sistem mulai dari pertukaran data menuju layanan rekomendasi, pemrosesan aturan asosiasi, sampai penyajian hasil kepada pengguna berfungsi sebagaimana mestinya. Dengan demikian, sistem ini diharapkan mampu memperkaya pengalaman pengguna selama proses pemesanan sekaligus menjadi sarana pendukung strategi cross-selling bagi Kedai Susu GBK. Kata kunci: Algoritma Apriori, Association rules, Sistem Rekomendasi, Laravel, Flask, REST API. Cross-selling
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41522010060 |
| Uncontrolled Keywords: | Algoritma Apriori, Association rules, Sistem Rekomendasi, Laravel, Flask, REST API. Cross-selling |
| 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 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 28 Aug 2026 10:00 |
| Last Modified: | 28 Aug 2026 10:00 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/103481 |
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