Article Per Year (5 Year)
p-Index From 2021 - 2026
1.431
P-Index
This Author published in this journals
All Journal Trikonomika: Jurnal Ekonomi JAIS (Journal of Applied Intelligent System) Jurnal Ilmu Komputer dan Bisnis Jurnal Teknik Informatika (JUTIF) Tedc Journal of Economics, Accounting, Tax and Management Ramatekno
Gerinata Ginting
Fakultas Pascasarjana Universitas Pasundan, Bandung
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Veterinary

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JAIS (Journal of Applied Intelligent System)

 1 
Smart Waste Management and Recycling Based on IoT using Machine Learning Algorithm Ginting, Gerinata; Apnena, Riri Damayanti
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10766

Abstract

Smart waste management and recycling have become critical issues in urban planning and environmental sustainability due to the increasing volume of waste generated by modern societies. In this study, we investigated the performance of Support Vector Machine (SVM) and Neural Network (NN) methods in an Arduino-based waste sorting system. Our testing phase revealed exceptional performance, with SVM achieving an accuracy of 92% and NN achieving 96%, alongside perfect precision, recall, and F1-score metrics. The consistent True Positive (TP) results across all waste categories underscored the system's capability to accurately direct waste into correspondingcolored bins. These findings highlight the significance of automated waste management systems in promoting effective waste sorting practices and facilitating sustainable recycling efforts. Moreover, advancements in technology and machine learning algorithms offer promising prospects for further enhancing the efficiency and scalability of such systems, thereby contributing to a cleaner and healthier environment for future generations. Future research in smart waste management could focus on exploring additional machine learning algorithms, advanced sensor technologies, and Internet of Things integration. Investigating alternative algorithms beyond SVM and NN, adopting advanced sensors like hyperspectral imaging or lidar, and incorporating IoT devices for real-time monitoring could enhance waste sorting accuracy and scalability.
Co-Authors Apnena, Riri Damayanti Ari Sudrajat Hellena, Lovenia Islam, Hussain Md Mehedul Jaja Suteja Jefriasnyah, Wawan Kusumawati, Puspa Dewi Monica, Rizqy Dimas Octanza, Tahta Rizaldi Pitriani, Kania Pujilestari, Intan Rahayu, Ayu Hendrati Ramdani, Rizki Saleh, Agus