Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Dody Pintarko
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
DEVELOPMENT OF AN AIoT-BASED COFFEE BEAN CLASSIFICATION AND SORTING SYSTEM USING A VISION TRANSFORMER Dody Pintarko; Basuki Rahmat; Faisal Muttaqin
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 5 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manual coffee bean sorting is highly prone to subjectivity, inconsistency, and low operational efficiency. This study aims to develop an automated classification and sorting system based on the Artificial Intelligence of Things (AIoT). The method integrates a Vision Transformer (ViT) model, TensorFlow Lite, Firebase, and an ESP32 microcontroller within a Mobile–Cloud–Edge Computing architecture. The ViT model was trained on four coffee roast levels to perform real-time inference on Android devices linked to physical sorting actuators. Experimental results showed that the ViT model achieved a 96.87% classification accuracy, while the automated physical sorting mechanism achieved 95.83% accuracy with an average response time of 462 ms. In conclusion, the integration of Vision Transformer and AIoT provides a fast and reliable post-harvest automation solution tailored for smart agricultural applications.
Co-Authors Basuki Rahmat Faisal Muttaqin