Side Dish Store Recommendation System Utilizes A Collaborative Filtering Methodology
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Suryani Suryani
*
Universitas Dipa Makassar, Makassar,
Indonesia
- Y Johny W Soetikno Universitas Dipa Makassar, Makassar, Indonesia
- Michael Octavianus Universitas Dipa Makassar, Makassar, Indonesia
- Faizal Faizal Universitas Dipa Makassar, Makassar, Indonesia
- Hasriani Hasriani Universitas Dipa Makassar, Makassar, Indonesia
- Nurdiansah Nurdiansah Universitas Dipa Makassar, Makassar, Indonesia
- Akbar Bahtiar Universitas Dipa Makassar, Makassar, Indonesia (*) Corresponding Author
Abstract
Side Dish Stores serve as traditional venues for buying and selling a diverse array of products, including fish, foodstuffs, beverages, kitchen spices, clothing, souvenirs, and more. In their pursuit of maximizing profits, traders use competitive strategies, such as online marketing, to expand their sales channels and promote products digitally. This research aims to generate recommendations for selling fish, and traditional supermarkets through a collaborative filtering method. The criteria used to generate recommendations for shops or fish sellers among 25 alternatives include product type, availability, service quality, shop cleanliness, product quality, and sales volume. The research results indicate that the highest predicted value is 0.70, where user 16 exhibits the highest similarity with user 14. Consequently, TSF 1= 4.17, TSF 5= 3.5, TSF 9= 3.33, TSF 13= 3.83, and TSF 15= 4.5. When ranked, TSF 15 will be the first recommendation for user 16, followed by TSF 1, TSF 13, TSF 5, and TSF 9. This recommendation system facilitates consumers in determining the appropriate shop or fish seller to visit when shopping at the Side Dish Store. Traders can easily market products to enhance sales. Moreover, the system streamlines buying and selling transactions for traders and consumers without the necessity of direct visits to the Side Dish Store
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References
H. Muharam, W. Susilawati, and S. Sarofah, “Strategi Pengembangan Pasar Tradisional Pada Pasar Guntur Ciawitali Garut,” Jurnal Industri Kreatif dan Kewirausahaan, vol. 6, no. 1, pp. 85–94, 2023.
I. A. Priatana and G. Supiandi, “Analisa Potensi Bersaing Pasar Tradisional Terhadap Pasar Modern di Kota Bogor dan Kabupaten Bogor,” JIIP-Jurnal Ilmiah Ilmu Pendidikan, vol. 4, no. 3, pp. 191–197, 2021.
N. Muzayyana, A. B. Salsabil, and N. N. Ayni, “Perancangan Sistem Rekomendasi Perbelanjaan item Kursi Kayu Pada Toko Online Berbasis Website dengan Metode Knowledge Based Recommendation,” in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis, 2023.
Y. Murdianingsih and I. Isbahatunnisa, “Implementasi Metode Fuzzy Tahani dalam Menentukan Rekomendasi Pembelian Laptop (Studi Kasus di Toko Mega Alvindo Kalijati Subang),” J. Teknol. Inf. dan Komun. ISSN, vol. 2252, p. 4517, 2020.
S. Suryani, A. Annah, N. Nurdiansah, F. Faizal, A. Bahtiar, and H. Hardi, “Decision Support System for the Presidential Election of the Student Executive Board Using the Multi-Factor Evaluation Process Method,” Journal of Computer Networks, Architecture and High Performance Computing, vol. 6, no. 1, pp. 364–370, 2024.
Z. Ren, B. Peng, T. K. Schleyer, and X. Ning, “Hybrid collaborative filtering methods for recommending search terms to clinicians,” J Biomed Inform, vol. 113, Jan. 2021, doi: 10.1016/j.jbi.2020.103635.
X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,” Comput Commun, vol. 41, pp. 1–10, 2014.
P. A. Pratama and G. R. Sutama, “Sistem Pendukung Keputusan Pemilihan Toko Online Peralatan Olahraga Dengan Metode SAW (Simple Additive Weighting),” KOMTEKS, vol. 1, no. 1, 2022.
H. Februariyanti, A. D. Laksono, J. S. Wibowo, and M. S. Utomo, “Implementasi Metode Collaborative Filtering Untuk Sistem Rekomendasi Penjualan Pada Toko Mebel,” Jurnal Khatulistiwa Informatika, vol. 9, no. 1, 2021.
A. Solichin and P. Painem, “Sistem Rekomendasi Keterampilan Teknologi Informasi Dengan Metode User-Based Collaborative Filtering dan Log-Likelihood Similarity,” CogITo Smart Journal, vol. 6, no. 2, pp. 141–154, 2020.
B. Cui, H. Feng, S. Li, and L. Liu, “The recommendation service of the shareholding for fund companies based on improved collaborative filtering method,” Procedia Comput Sci, vol. 162, pp. 68–75, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.259.
S. Yu et al., “Privacy-preserving recommendation system based on social relationships,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 2, p. 101923, 2024, doi: https://doi.org/10.1016/j.jksuci.2024.101923.
P. Chinnasamy, W.-K. Wong, A. A. Raja, O. I. Khalaf, A. Kiran, and J. C. Babu, “Health Recommendation System using Deep Learning-based Collaborative Filtering,” Heliyon, vol. 9, no. 12, p. e22844, 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e22844.
M. Bałchanowski and U. Boryczka, “Collaborative Rank Aggregation in Recommendation Systems,” Procedia Comput Sci, vol. 207, pp. 2213–2222, 2022, doi: https://doi.org/10.1016/j.procs.2022.09.281.
A. Fareed, S. Hassan, S. B. Belhaouari, and Z. Halim, “A collaborative filtering recommendation framework utilizing social networks,” Machine Learning with Applications, vol. 14, p. 100495, 2023, doi: https://doi.org/10.1016/j.mlwa.2023.100495.
A. Fareed, S. Hassan, S. B. Belhaouari, and Z. Halim, “A collaborative filtering recommendation framework utilizing social networks,” Machine Learning with Applications, vol. 14, p. 100495, 2023, doi: https://doi.org/10.1016/j.mlwa.2023.100495.
Z. Zhu, “Analysis of Collaborative Filtering Algorithm in Personalized Push Service of University Library,” Procedia Comput Sci, vol. 228, pp. 502–510, 2023, doi: https://doi.org/10.1016/j.procs.2023.11.057.
G. Behera and N. Nain, “Collaborative Filtering with Temporal Features for Movie Recommendation System,” Procedia Comput Sci, vol. 218, pp. 1366–1373, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.115.
S. Nath Sharma and P. Sadagopan, “Influence of conditional holoentropy-based feature selection on automatic recommendation system in E-commerce sector,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, Part A, pp. 5564–5577, 2022, doi: https://doi.org/10.1016/j.jksuci.2020.12.022.
S. Suhendri, D. Zaliluddin, and A. Muharom, “SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KOMPONEN PERSONAL COMPUTER DENGAN MENGGUNAKAN METODE ITEM-BASED COLLABORATIVE FILTERING,” INFOTECH journal, vol. 8, no. 1, pp. 53–59, 2022.
D. Susianto and Z. Rusdi, “SISTEM REKOMENDASI PADA PENJUALAN ELEKTRONIK MENGGUNAKAN METODE COLLABORATIVE FILTERING,” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 11, no. 1, 2023.
G. Jain, T. Mahara, and S. C. Sharma, “Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique,” Procedia Comput Sci, vol. 218, pp. 1834–1844, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.161.
F. Fkih, “Enhancing item-based collaborative filtering by users’ similarities injection and low-quality data handling,” Data Knowl Eng, vol. 144, p. 102126, 2023, doi: https://doi.org/10.1016/j.datak.2022.102126.
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Copyright (c) 2024 Y Johny W Soetikno, Suryani, Michael Octavianus, Faizal, Hasriani, Nurdiansah, Akbar Bahtiar
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