Komparasi Metode Artificial Neural Network dan Support Vector Machine untuk Identifikasi Gas Polimer Berbahaya Menggunakan Electronic Nose
DENAYA PRAMA SIDYA, Prof. Dr.techn. Ahmad Ashari, M.Kom.; Roghib Muhammad Hujja, S.Si., M.Sc.
2024 | Skripsi | ELEKTRONIKA DAN INSTRUMENTASI
Plastic is a polymer compound that can release hazardous gases when heated, such as carbon monoxide and formaldehyde, posing health and occupational risks, including fires. An electronic nose is used to detect these gases through a gas sensor array. It works by detecting the gas aroma emitted by plastic during heating, converting it into electrical signals, which are processed using machine learning algorithms like Artificial Neural Network (ANN) and Support Vector Machine (SVM). This study focuses on using an electronic nose to detect gases from heated plastic samples, including Polystyrene (PS), Polyvinyl Chloride (PVC), and Polyoxymethylene (POM). The system includes a sample unit, gas sensors, a microcontroller, and a data processing unit with a dashboard to display the results. The performance of ANN and SVM was compared to find the most effective method for detecting polymer gases and assess regularization techniques to reduce overfitting. Results showed that ANN performed better, achieving 99.4?curacy, 98.8% recall, 98.9% precision, and a 98.7?-score. SVM showed lower results with 90?curacy, 80% recall, 80.1% precision, and a 79.7?-score. The use of dropout techniques on ANN (0.5 and 0.2) effectively reduced overfitting, resulting in a 90.8% training score and a 98.5% validation score. For SVM, regularization with varying cost and gamma values and 5-fold cross-validation yielded an 82% training score and a 78.8% validation score. Therefore, ANN proved superior to SVM in detecting hazardous polymer gases under complex and non-linear data conditions.
Kata Kunci : Electronic Nose, Artificial Neural Network, Support Vector Machine, Overfitting, Klasifikasi