Analisis Citra Digital Roti Tawar untuk Identifikasi Kontaminasi Jamur Menggunakan Convolutional Neural Network
DOI:
https://doi.org/10.22441/jitkom.v10i1.006Kata Kunci:
CNN, Deep, Deteksi Jamur, Keamanan Pangan, Klasifikasi Citra, Python, Roti TawarAbstrak
Kontaminasi jamur (Aspergillus sp) pada produk makanan seperti roti merupakan masalah serius dalam industri pangan karena dapat membahayakan kesehatan konsumen dan menurunkan kualitas produk. Penelitian ini bertujuan mengembangkan model klasifikasi citra berbasis Convolutional Neural Network (CNN) untuk mendeteksi kontaminasi jamur secara otomatis pada roti tawar. Dataset yang digunakan terdiri dari 2000 gambar, yang terbagi secara seimbang antara gambar roti berjamur dan tidak berjamur. Pra-pemrosesan dilakukan dengan mengubah citra ke format grayscale dan menyesuaikan resolusi ke 128x128 piksel. Model CNN dibangun menggunakan Python dengan framework Keras dan TensorFlow. Hasil pengujian menunjukkan bahwa model CNN mampu mengklasifikasikan gambar dengan akurasi 47%, presisi 47%, recall 47% dan f1-score 47%. Nilai akurasi, presisi, recall, dan f1-score yang seragam (47%) pada kedua kelas menunjukkan bahwa model gagal membedakan fitur penting dari citra roti berjamur dan tidak berjamur.
Kata Kunci—CNN; deep learning; deteksi jamur; keamanan pangan; klasifikasi citra; python; roti tawarReferensi
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