ANALISIS PERAN TEKNOLOGI AI PADA PERSONALISASI FOR YOU TERHADAP APLIKASI THREADS - Angelina Paramitha; Evan Gunawan; Nelson Nelson; Dicky Pratama (Jurnal Riset Sistem Informasi, 2026)
This study analyzes the role of Artificial Intelligence (AI) technology in personalizing the For You on the Threads app, a text-based social media platform developed by Meta. This study aims to bridge the gap in quantitative and qualitative understanding of how AI algorithms influence users’ content consumption patterns, particularly through adaptive filtering based on search keywords, interactions (likes, reposts), followed accounts, and trends. A quantitative approach was employed by distributing an online Likert-scale questionnaire to 51 active Threads users. Satisfaction levels were analyzed using the End-User Computing Satisfaction (EUCS) method through three main indicators: For You Accuracy (Q1), Interest Relevance (Q2), and Keyword Search Influence (Q3). Reliability test results indicate that the research instrument is reliable with a Cronbach’s Alpha value of 0.836. Demographic analysis revealed that respondents were predominantly young adults aged 18–24. Key findings based on descriptive statistics showed that the keyword search feature in the For You received the highest average score of 3.78, followed by content relevance to personal interests at 3.59, and recommendation accuracy at 3.49. Overall, the Grand Mean score of 3.62 places user satisfaction in the “Satisfied” category, with 62.7% of respondents stating they were satisfied or very satisfied. Correlation analysis reveals a fairly strong positive relationship among indicators, with the highest correlation between For You Accuracy and Interest Alignment ($r = 0.679$). However, user awareness of AI mechanisms remains limited, such as a lack of knowledge regarding the "Dear Algo" feature, which could potentially restrict information diversity. The study concludes that AI-based For You significantly increases engagement and satisfaction, but highlights the need for transparency and better user control through specific topic filter features. These insights are expected to provide strategic guidance for the Threads development team and contribute to broader discussions regarding AI implementation on digital platforms.