%0 Thesis
%9 Skripsi
%A Zakaria, M. Adam
%A Universitas Pakuan,
%A Fakultas Matematika dan Ilmu Pnegetahuan Alam,
%A Program Studi Ilmu Komputer,
%B Fakultas Matematika dan Pengetahuan Alam
%D 2025
%F eprintsunpak:11229
%I Universitas Pakuan
%T IMPLEMENTATION OF AI SPEECH-TO-TEXT   IN MEDICINAL PLANT SEARCH   APPLICATIONS
%U http://eprints.unpak.ac.id/11229/
%X 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.