The Empowering Village Coffee Farmers through Website Optimization with Predictive RankMath SEO - Nia Komalasari; Haryanto; Fauzi; Abdul Azis; Adila Yuansa (Universitas Islam Kuantan Singingi, 2025)
This research is motivated by the low visibility and limited marketing of Kopi Kobaki MSME products, managed by the Kobaki Coffee Farmers Association in Banten. Although the community has significant potential to produce high-quality local coffee, the lack of digital literacy and online marketing efforts has caused sales to rely mainly on local intermediaries. This study aims to develop a village economic model based on a website integrated with predictive SEO strategies using the RankMath plugin, expand the market, and support farmers' economic independence. The research methods include needs analysis through surveys and interviews, website design and development using the WordPress platform, implementation of on-page and off-page SEO, and monitoring and evaluation through Google Search Console. The research results show that the kopikobaki.com website can increase the digital visibility of village coffee, even in communities with low digital literacy. The integration of a user-centered design approach, RankMath Pro optimization techniques, and Google Search Console metrics-based evaluation results demonstrates strong digital performance with high CTR and competitive search positions. These findings demonstrate that website-based digitalization can be an effective model for strengthening marketing, improving farmers' digital literacy, and supporting village economic independence through the use of sustainable technology.