http://eprints.unpak.ac.id/id/eprint/11229 2026-10-10T04:27:09Z info:eu-repo/semantics/descriptiveMetadata http://eprints.unpak.ac.id/11229/ IMPLEMENTATION OF AI SPEECH-TO-TEXT IN MEDICINAL PLANT SEARCH APPLICATIONS Zakaria, M. Adam Ilmu Komputer IMPLEMENTATION OF AI SPEECH-TO-TEXT IN MEDICINAL PLANT SEARCH APPLICATIONS M Adam zakaria1 , Herfina 2 , Halimah Tussa’diah 3* Study Program Computer Science, Faculty of Mathematics and Natural Science, University Bogor, West Java, 16143, Indonesia Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Department of Mathematical Sciences, Faculty ofScience, Abstract Abstract This study aims to develop an artificial intelligence (AI)-based application that helps users search for information about medicinal plants through voice, image, and text inputs. Indonesia is known to have more than 30,000 plant species, of which about 9,600 have potential medicinal properties. However, much of the traditional knowledge surrounding these plants remains undigitized and difficult to access. The AI Speech-to-Text (STT) technology was selected because it can convert human speech into text that can be processed automatically for plant information retrieval. The system integrates STT and image recognition services through an Application Programming Interface (API), developed using JavaScript and the React framework. The System Development Life Cycle (SDLC) Waterfall model was employed, covering planning, design, implementation, testing, deployment, and maintenance phases. Testing results show that the system accurately converts speech into text and provides quick and relevant information on medicinal plants. The study demonstrates that implementing AI STT can improve the efficiency and accessibility of medicinal plant information while supporting the preservation of Indonesia’s ethnobotanical heritage. Keywords: Artificial Intelligence, Speech-to-Text, Medicinal Plants, SDLC, Information Retrieval. 2025-12-08 Thesis NonPeerReviewed Zakaria, M. Adam (2025) IMPLEMENTATION OF AI SPEECH-TO-TEXT IN MEDICINAL PLANT SEARCH APPLICATIONS. Skripsi thesis, Universitas Pakuan. info:eu-repo/semantics/humanStartPage