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All Journal Zeta - Math Journal Prosiding Seminar Nasional Sains dan Teknologi Terapan Journal Research of Social Science, Economics, and Management
Audrey, Talitha Naifa
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Computer Science & IT Environmental Science Mathematics

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Penerapan Algoritma DBSCAN Untuk Clustering Penjualan di Supermarket Hapsari, Rinci Kembang; Audrey, Talitha Naifa; Widodo, Muhammad Amiruddin; Islamiyah, Mitha
Zeta - Math Journal Vol 9 No 2 (2024): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2024.9.2.83-89

Abstract

Supermarkets are shopping places that provide various daily necessities. Many customers visit supermarkets to buy necessities. The growth of supermarkets is increasing. Supermarkets have a variety of products with different brands, branches, and types of customers. To create a sales strategy, need to know the products that customers are interested in. In this research, supermarket product clustering was carried out based on sales data. The clustering algorithm used in this research is the DBSCAN algorithm. This algorithm is an algorithm for grouping data objects based on density, which is influenced by input parameters, namely the Eps and MinPts values. The data used in this research is secondary data consisting of 100 supermarket sales data, taking 2 attributes. The clustering results show that using the Eps parameter value = 6 and the MinPts value = 9, the product data is divided into 3 clusters, namely cluster 1 of products that are not in demand, cluster 2 of products that are in demand and cluster 3 of very popular products.
Sistem Rekomendasi Tempat Makan Berbasis Konten Berdasarkan Metode Best Match 25 Lucene (BM25L) Wardhana, Septiyawan Rosetya; Audrey, Talitha Naifa; Nugroho, Hendro
Prosiding Seminar Nasional Sains dan Teknologi Terapan 2024: Menjembatani Energi Berkelanjutan dan Ekonomi Hijau melalui Transformasi Riset dan Teknologi T
Publisher : Institut Teknologi Adhi Tama Surabaya

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Abstract

Sistem rekomendasi adalah sistem yang dirancang untuk memberikan rekomendasi dari suatu produk dengan tujuan membantu pengguna dalam melakukan pengambilan keputusan. Tempat makan yang beragam dan kurangnya rekomendasi kuliner yang mencakup jenis makanan, variasi menu, harga, fasilitas, jam operasional, serta lokasi yang menjadi permasalahan bagi warga lokal dan wisatawan dalam menentukan tempat makan yang sesuai. Untuk mengatasi permasalahan tersebut, dalam penelitian ini diusulkan pengembangan sistem rekomendasi tempat makan berbasis konten dengan metode Best Match 25 Lucene (BM25L) di Pamekasan. Sistem ini membantu pengguna menemukan tempat makan sesuai preferensi mereka berdasarkan menu yang ditawarkan. Data diperoleh dari sumber publik seperti Google dan Instagram serta observasi langsung. Metode BM25L dipilih karena kemampuannya mengatasi pengaruh panjang dokumen pada peringkat, sehingga menghasilkan nilai yang lebih akurat. Pengujian menggunakan precision @k oleh 10 pengguna menunjukkan precision @10 sebesar 0,93 dan precision @20 sebesar 0,84. Perbedaan ini terjadi karena hasil yang lebih relevan biasanya muncul di posisi awal. Sistem rekomendasi dioptimalkan untuk menampilkan hasil paling relevan di peringkat 1-10, sehingga hasil pertama lebih akurat. Ketika jumlah hasil yang dievaluasi meningkat menjadi 20, relevansi rata-rata menurun. Ini menunjukkan bahwa sistem mampu memberikan hasil sangat akurat pada jumlah hasil yang lebih kecil namun tetap relevan pada hasil yang lebih besar. Dengan sistem ini, pengguna dapat dengan mudah menemukan tempat makan yang diinginkan
Content-Based Restaurant Recommendation System Using the Best Match 25 Lucene (Bm25l) Method Audrey, Talitha Naifa
Journal Research of Social Science, Economics, and Management Vol. 5 No. 3 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i3.1117

Abstract

Choosing a suitable dining place can be challenging for both locals and tourists due to the wide variety of culinary options and the limited availability of comprehensive recommendations covering food types, menu variations, prices, facilities, operating hours, and locations. To address this issue, this study aims to develop a Content-Based Restaurant Recommendation System Using the Best Match 25 Lucene (BM25L) Method in Pamekasan that assists users in selecting dining places aligned with their preferences. The system leverages the Best Match 25 Lucene (BM25L) method, which effectively accounts for document length in ranking, providing more precise recommendations. Data were collected from public sources such as Google and Instagram, complemented by direct field observations. System performance was evaluated through precision@k testing with 10 users, yielding a precision@10 score of 0.93 and a precision@20 score of 0.84. The results indicate that the most relevant recommendations typically appear within the top 10 positions, while relevance slightly decreases as the number of evaluated results increases. This demonstrates that the system is highly accurate for smaller result sets while maintaining acceptable relevance for larger sets. The research contributes to improving user experience by enabling faster and more reliable decision-making when choosing dining venues. Furthermore, the system provides a practical framework for future applications of content-based recommendation methods in the culinary domain and other service sectors. By prioritizing the most relevant options, this system enhances convenience, supports informed choices, and can serve as a model for similar smart recommendation solutions in regional tourism and hospitality contexts.
Co-Authors Hendro Nugroho, Hendro Islamiyah, Mitha Rinci Kembang Hapsari Wardhana, Septiyawan Rosetya Widodo, Muhammad Amiruddin