eprintid: 11229
rev_number: 6
eprint_status: archive
userid: 46
dir: disk0/00/01/12/29
datestamp: 2026-10-10 04:27:09
lastmod: 2026-10-10 04:27:09
status_changed: 2026-10-10 04:27:09
type: thesis
metadata_visibility: show
creators_name: Zakaria, M. Adam
creators_NPM: 065121169
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Herfina, Herfina
contributors_name: Tus Sadiah, Halimah
corp_creators: Universitas Pakuan
corp_creators: Fakultas Matematika dan Ilmu Pnegetahuan Alam
corp_creators: Program Studi Ilmu Komputer
title: IMPLEMENTATION OF AI SPEECH-TO-TEXT 
IN MEDICINAL PLANT SEARCH 
APPLICATIONS
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
abstract: 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.
date: 2025-12-08
date_type: published
institution: Universitas Pakuan
department: Fakultas Matematika dan Pengetahuan Alam
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Zakaria, M. Adam  (2025) IMPLEMENTATION OF AI SPEECH-TO-TEXT IN MEDICINAL PLANT SEARCH APPLICATIONS.  Skripsi thesis, Universitas Pakuan.