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GEOGRAPHIC INFORMATION SYSTEM APPLICATION FOR TRADITIONAL MARKET MAPPING IN PADANG CITY BASED ON ANDROID Melladia, Melladia; Afriansyah, Fadila
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3656

Abstract

This study aims to develop an Android-based Geographic Information System (GIS) application for mapping traditional markets in Padang City. The main issue addressed is the lack of accessible spatial information regarding market locations for the general public. The research employed field observations, interviews with the Trade Office and market vendors, and literature study. The system development followed the waterfall model, comprising requirement analysis, system design, implementation, testing, and maintenance. The application was built using Android Studio, LeafletJS, and QGIS. The results show that the application successfully presents market location information in an interactive map format, displays detailed market data, and facilitates administrative data management. This application is expected to assist users in locating traditional markets and support local government efforts in spatial data management more efficiently.Keyword: Georaphic Information System, Padang City, QGIS, Leaflet, Android
Sistem Pendeteksi Bahasa Isyarat SIBI Menggunakan LSTM Berbasis OpenCV dan MediaPip Sarjianto, Ahmad; Melladia, Melladia
JURNAL ILMIAH INFORMATIKA Vol 14 No 01 (2026): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v14i01.10681

Abstract

This study aims to develop a Sign Language Translation System which is specifically for the Indonesian Sign Language System (SIBI) based on artificial intelligence (AI) and computer vision which aims to help communication between deaf/mute people and the general public. using the Long Short-Term Memory (LSTM) method, taking important data from Sign Language hand movements and combined with OpenCV and MediaPip. This system is designed with a web-based interface that will display translations in text form in real-time. The testing was conducted on a dataset consisting of SIBI alphabets and basic words, with the highest accuracy reaching 0.85 or 85% for basic words, and 0.45 or 45% for alphabet recognition.In conclusion, this research produced a system capable of automatically translating sign language by utilizing web technology for the interface, and OpenCV, MediaPip, and Long Short-Term Memory (LSTM) for the translation process.This system has great potential to reduce communication barriers between the general public and individuals with hearing or speech impairments, although further development is required to improve its accuracy.