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All Journal Journal Of Artificial Intelligence And Software Engineering
Az Zahra, Erika Oktaviana
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Computer Science & IT

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Instagram Influencer Recommendation System Based On Content-Based Filtering To Support Digital Marketing Strategy Az Zahra, Erika Oktaviana; Pramono, Pramono; Suryani, Fajar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6939

Abstract

Influencer marketing is a popular promotional strategy on Instagram that involves influential individuals or public figures to promote products. However, there are problems where companies still find it difficult to find the right influencers. This research aims to build a Content-Based Filtering-based Instagram influencer recommendation system to support digital marketing strategies. The system development method used is Rapid Application Development (RAD) with 4 stages, namely requirements planning, system design, development, and implementation. With this system, users can recommend other influencers who have similar characteristics such as number of followers, average likes, and comments, engagement rate, and growth rate. System testing was conducted on 10 test data with different inputs. The results showed that 9 out of 10 tests matched the user input, indicating a system accuracy of 90% and has the potential to assist users in selecting relevant influencers.
Co-Authors Pramono Pramono Suryani, Fajar