KLASIFIKASI KEMATANGAN BUAH TOMAT BERBASIS CNN DENGAN ARSITEKTUR MOBILENETV2 DAN TRANSFER LEARNING

Mochamad Fathur Milzam, Ahmad Syukri Gozali, Sumanto Sumanto, Jefina Tri Kumalasari, Ghofar Taufiq, Ade Christian

Abstract


Classifying tomato ripeness is a crucial aspect of post-harvest processing and agricultural distribution. Manual ripeness determination is subjective and potentially inconsistencies. This study aims to classify tomato ripeness using a deep learning approach based on a Convolutional Neural Network (CNN) with the MobileNetV2 architecture and transfer learning techniques. The dataset, consisting of tomato images, was classified into three classes: unripe, ripe, and overripe. Preprocessing included image resizing and normalization before model training. The MobileNetV2 model was used as a feature extractor, while a fully connected layer was applied for classification. Test results showed that the model was able to classify tomato ripeness with good performance based on classification evaluation metrics. Overall, the proposed approach shows good potential for RGB image-based tomato ripeness classification.

Klasifikasi tingkat kematangan buah tomat merupakan aspek penting dalam proses pascapanen dan distribusi hasil pertanian. Penentuan kematangan secara manual masih bersifat subjektif dan berpotensi menimbulkan ketidakkonsistenan. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kematangan buah tomat menggunakan pendekatan deep learning berbasis Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2 dan teknik transfer learning. Dataset berupa citra buah tomat diklasifikasikan ke dalam tiga kelas, yaitu tidak matang, matang, dan terlalu matang. Tahapan preprocessing meliputi proses resizing dan normalisasi citra sebelum dilakukan pelatihan model. Model MobileNetV2 digunakan sebagai feature extractor, sedangkan lapisan fully connected diterapkan untuk proses klasifikasi. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan tingkat kematangan buah tomat dengan performa yang baik berdasarkan metrik evaluasi klasifikasi. Secara keseluruhan, pendekatan yang diusulkan menunjukkan potensi yang baik untuk klasifikasi tingkat kematangan buah tomat berbasis citra RGB


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DOI: https://doi.org/10.56486/jeis.vol6no2.1113

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