Evaluasi Kinerja MobileNetV2 dalam Klasifikasi Sayuran Daun Hijau Lokal Mojo Kabupaten Kediri Berbasis Citra Digital
DOI:
https://doi.org/10.29407/w526b584Abstract
Penelitian ini bertujuan untuk mengevaluasi kinerja arsitektur MobileNetV2 dalam klasifikasi sayuran daun hijau lokal berbasis citra digital. Identifikasi jenis daun secara manual masih memiliki keterbatasan karena adanya kemiripan karakteristik visual antar kelas sehingga diperlukan pendekatan klasifikasi otomatis yang lebih cepat dan konsisten. Penelitian ini menggunakan pendekatan transfer learning dengan model MobileNetV2 berbobot awal ImageNet. Dataset terdiri dari delapan kelas, yaitu daun beluntas, daun kelor, daun kenikir, daun pakis, daun pepaya, daun sembukan, daun singkong, dan daun umbi jalar yang dibagi menjadi data training, validation, dan testing. Tahap preprocessing dilakukan melalui resize citra menjadi 224 × 224 piksel, normalisasi, dan augmentasi data. Untuk membantu mengurangi overfitting, diterapkan BatchNormalization, Dropout, serta callback berupa EarlyStopping, ReduceLROnPlateau, dan ModelCheckpoint. Hasil evaluasi menunjukkan bahwa model memperoleh nilai accuracy sebesar 91%, precision sebesar 91%, recall sebesar 91%, dan f1-score sebesar 90%. Hasil tersebut menunjukkan bahwa MobileNetV2 mampu melakukan klasifikasi sayuran daun hijau lokal dengan performa yang baik dan stabil.
Keywords:
citra digital, klasifikasi, MobileNetV2, sayuran daun hijau lokal, transfer learning##plugins.themes.default.displayStats.downloads##
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