Implementation of Neighborhood-Based Collaborative Filtering Algorithm on Laundry Service Recommendation System

  • Tony Darmanto Universitas Widya Dharma Pontianak
  • Sandi Tendean Universitas Widya Dharma Pontianak
  • Yogi Tinnando Universitas Widya Dharma Pontianak
Keywords: Recommendation System, Laundry Service, Neighborhood Based, Collaborative Filtering


Modern information technology has advanced human life significantly, particularly in the way that it presents crucial information. People often find it challenging to choose the right kind of information for their needs due to the sheer volume of data that information technology may offer. To assist users in selecting the information they require, a recommendation system has been implemented. It is possible to use collaborative filtering algorithms to the use of recommendation systems in industries like laundry services. by the application of an experimental, causal connection research design as the research methodology. A literature review, which comprises scientific books, research reports, scientific journals, theses, and textual materials in both print and electronic format, is the method of data collection employed by the author. The Unified Modeling Language (UML), which the author uses to describe the processes by which the collaborative filtering recommendation system applies its recommendations for laundry services, is one of the methods of system modeling that the author employs. The collaborative filtering algorithm creates new information based on the pattern of a group of users who share similar features by filtering laundry service data according to the user's desired attributes. The filtering results are displayed through an interactive user interface in an algorithm that is implemented in a web application. The user's demands and preferences can be taken into account when generating suggestion results by the laundry service recommendation system.


H. Ko, S. Lee, Y. Park, and A. Choi, “A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields,” Electronics (Switzerland), vol. 11, no. 1. 2022. doi: 10.3390/electronics11010141.

S. K. Raghuwanshi and R. K. Pateriya, “Collaborative Filtering Techniques in Recommendation Systems,” in Data, Engineering and Applications: Volume 1, vol. 1, 2019. doi: 10.1007/978-981-13-6347-4_2.

A. Pamuji, “Sistem Rekomendasi Kredit Perumahan Rakyat Dengan Menggunakan Metode Collaborative Filtering,” Faktor Exacta, vol. 10, no. 1, 2017.

A. H. Ritdrix and P. W. Wirawan, “Sistem Rekomendasi Buku Menggunakan Metode Item-Based Collaborative Filtering,” Jurnal Masyarakat Informatika, vol. 9, no. 2, 2018, doi: 10.14710/jmasif.9.2.31482.

N. Yanti et al., “Penerapan Algoritma Collaborative Filtering Untuk Rekomendasi Games Hardware,” Artikel 4 - JUTISI, vol. 2, no. 1, 2013.

C. C. Aggarwal, “Knowledge-Based Recommender Systems,” in Recommender Systems, 2016. doi: 10.1007/978-3-319-29659-3_5.

A. B. Hikmah, D. Supriadi, and T. Alawiyah, Cara Cepat Membangun Website Dari Nol. Yogyakarta: Andi, 2015.

K. Harianto, H. Pratiwi, and Y. Suahariyadi, Sistem Monitoring Lulusan Perguruan Tinggi Dalam Memasuki Dunia Kerja Menggunakan Trace Study. Surabaya: Media Sahabat Cendikia, 2019.

R. A. Sukamto and M. Shalahuddin, Rekayasa Perangkat Lunak Terstruktur dan Berorientasi Objek. Bandung: Informatika, 2014.

S. R. Wicaksono, Rekayasa Perangkat Lunak. Malang: Seribu Bintang, 2017.

How to Cite
Darmanto, T., Tendean, S., & Tinnando, Y. (2024). Implementation of Neighborhood-Based Collaborative Filtering Algorithm on Laundry Service Recommendation System. Journal of Information Technology, 4(1), 126-131.