Ekstraksi Fitur MobileNetV2 dan Broad Learning System untuk Klasifikasi Citra Makanan
DOI:
https://doi.org/10.54065/artificial.2014Keywords:
Broad Learning System, Ekstraksi Fitur, Klasifikasi Citra, Masakan Nusantara, Mobilenetv2Abstract
Klasifikasi citra masakan Nusantara merupakan salah satu permasalahan dalam pengolahan citra karena setiap jenis masakan memiliki karakteristik visual yang dapat saling menyerupai. Penelitian ini bertujuan mengembangkan model klasifikasi citra masakan Nusantara dengan mengintegrasikan MobileNetV2 untuk ekstraksi fitur dan Broad Learning System (BLS) untuk klasifikasi. Dataset terdiri atas 7.690 citra yang terbagi ke dalam 14 kelas, meliputi berbagai jenis masakan Nusantara dan kelas bukan_masakan_nusantara. Tahapan penelitian mencakup prapemrosesan citra melalui pengubahan ukuran menjadi 224 × 224 piksel dan normalisasi nilai piksel, ekstraksi fitur menggunakan MobileNetV2 dengan bobot pralatih ImageNet, standardisasi fitur, serta klasifikasi menggunakan BLS. Kinerja model dievaluasi menggunakan akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil pengujian terhadap 1.047 citra menghasilkan akurasi sebesar 89,02%, presisi tertimbang 89,14%, recall tertimbang 89,02%, dan F1-score tertimbang 88,97%. Evaluasi juga menunjukkan adanya variasi kinerja antarkelas serta kesalahan klasifikasi pada masakan yang memiliki kemiripan visual. Hasil ini menunjukkan bahwa kombinasi MobileNetV2 dan BLS dapat digunakan untuk klasifikasi citra masakan pada dataset yang diteliti. Pengembangan selanjutnya dapat difokuskan pada penggunaan dataset yang lebih beragam dan evaluasi komparatif untuk meningkatkan ketepatan serta kemampuan generalisasi model.
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