Article Per Year (5 Year)
p-Index From 2021 - 2026
0.339
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mario Ardhany
National Research and Innovation Agency
Computer Science & IT

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

Found 1 Documents
Search

 1 
Adulterated beef detection with redundant gas sensor using optimized convolutional neural network Ardani Cesario Zuhri; Agus Widodo; Mario Ardhany; Danny Mokhammad Gandana; Galang Ilman Islami; Galuh Prihantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26490

Abstract

Various types of research have been developed to detect beef adulteration, but the accuracy and reliability of these results still require improvement. This study proposes designing a highly precise redundant electronic nose system using an optimized convolutional neural network (CNN) method to detect adulterated beef mixed with pork. As baselines, other classifiers are also utilized, namely the decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). Several data preprocessing methods are employed to increase prediction accuracy, namely feature selection, principal component analysis (PCA), and time series smoothing. The weight of each data sample was 100 g with 15 classes of pork and beef mixing ratios of 0%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% pork. With the single-layer sensor configuration, the average CNN classification success rates were 97.15%, 96.29%, and 99.64% for layers 1, 2, and 3, respectively. In addition, from the combination of the three layers, a prediction results of 99.72% was obtained. Thus, a redundant gas sensor array configuration can improve the classification results. In addition, the relatively high accuracy of the optimized CNN provides a convincing alternative for identifying possible beef adulteration.
Co-Authors Agus Widodo Ardani Cesario Zuhri Danny Mokhammad Gandana Galang Ilman Islami Galuh Prihantoro