<mets:mets OBJID="eprint_11229" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2026-10-13T01:16:51Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Repository Universitas Pakuan</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_11229_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>IMPLEMENTATION OF AI SPEECH-TO-TEXT &#13;
IN MEDICINAL PLANT SEARCH &#13;
APPLICATIONS</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">M. Adam</mods:namePart><mods:namePart type="family">Zakaria</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>IMPLEMENTATION OF AI SPEECH-TO-TEXT &#13;
IN MEDICINAL PLANT SEARCH &#13;
APPLICATIONS&#13;
M Adam zakaria1&#13;
, Herfina 2&#13;
, Halimah Tussa’diah 3*&#13;
Study Program Computer Science, Faculty of Mathematics and Natural Science,&#13;
University Bogor, West Java, 16143, Indonesia&#13;
Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan&#13;
University, Bogor, West Java, 16143, Indonesia&#13;
Department of Mathematical Sciences, Faculty ofScience,&#13;
Abstract&#13;
Abstract This study aims to develop an artificial intelligence (AI)-based application that &#13;
helps users search for information about medicinal plants through voice, image, and text &#13;
inputs. Indonesia is known to have more than 30,000 plant species, of which about 9,600 &#13;
have potential medicinal properties. However, much of the traditional knowledge &#13;
surrounding these plants remains undigitized and difficult to access. The AI Speech-to-Text &#13;
(STT) technology was selected because it can convert human speech into text that can be &#13;
processed automatically for plant information retrieval. The system integrates STT and &#13;
image recognition services through an Application Programming Interface (API), &#13;
developed using JavaScript and the React framework. The System Development Life Cycle &#13;
(SDLC) Waterfall model was employed, covering planning, design, implementation, &#13;
testing, deployment, and maintenance phases. Testing results show that the system &#13;
accurately converts speech into text and provides quick and relevant information on &#13;
medicinal plants. The study demonstrates that implementing AI STT can improve the &#13;
efficiency and accessibility of medicinal plant information while supporting the &#13;
preservation of Indonesia’s ethnobotanical heritage.&#13;
Keywords: Artificial Intelligence, Speech-to-Text, Medicinal Plants, SDLC, Information &#13;
Retrieval.</mods:abstract><mods:classification authority="lcc">Ilmu Komputer</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2025-12-08</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas Pakuan;Fakultas Matematika dan Pengetahuan Alam</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_11229"><mets:rightsMD ID="rights_eprint_11229_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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