Klasifikasi Citra Jamur Menggunakan SVM dengan PCA Berbasis Ekstraksi Fitur Hibrida - I Putu Andika Arsana Putra,; I Gusti Agung Gede Arya Kadyanan, (Jurnal Nasional Teknologi Informasi dan Aplikasinya, 2026)
The general public still faces significant difficulty in differentiating between poisonous and non-poisonous mushrooms due to their high visual similarity. This has led to numerous poisoning incidents due to consumption of poisonous mushrooms. Between 2010 and 2020, there were 76 reported cases of poisoning involving 550 victims, 9 of whom died. To address this issue, a classification model was developed to differentiate between poisonous and non-poisonous mushrooms using Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms based on hybrid feature extraction. The dataset for this study was obtained from Kaggle. The model built using PCA saw an increase in the model training time to 3 minutes 32 seconds from the initial 16 minutes 4 seconds without using PCA. Hyperparameter tuning was performed to find the best combination of parameters, resulting in RBF kernel, C value of 10, and gamma set to scale. The model was evaluated using a confusion matrix to determine accuracy and class-specific metrics. The model performed well, achieving 85% accuracy on the test data.
Keywords: Mushrooms; Support Vector Machine; Principal Component Analysis; Classification; Machine Learning