<![CDATA[Inaccurate pest identification can delay control measures and increase the risk of agricultural losses. This study evaluated MobileNetV2 for image-based pest classification and simulated the financial impact of prediction errors. The Dangerous Farm Insects Dataset, containing 1,591 images in 15 classes, was used in four scenarios: baseline, augmentation, baseline fine-tuning, and augmentation fine-tuning. The models were assessed using accuracy, precision, recall, macro F1-score, and confusion matrices. The results showed that the baseline achieved the best performance, with an accuracy of 0.750 and a macro F1-score of 0.744. Fine-tuning produced the same accuracy but a slightly lower macro F1-score, while the two augmentation scenarios achieved accuracies of only 0.528 and 0.536. Most errors involved visually similar classes. The financial simulation indicated that the baseline had the lowest relative risk, although the cost values remained illustrative parameters requiring field validation. Keywords: MobileNetV2; image classification; agricultural pests; data augmentation; financial impact Abstrak Identifikasi hama yang tidak tepat dapat menunda pengendalian dan meningkatkan risiko kerugian pertanian. Penelitian ini mengevaluasi MobileNetV2 untuk klasifikasi citra hama serta menyimulasikan dampak finansial kesalahan prediksi. Dangerous Farm Insects Dataset yang berisi 1.591 citra dalam 15 kelas digunakan pada empat skenario: baseline, augmentation, baseline fine-tuning, dan augmentation fine-tuning. Model dinilai menggunakan accuracy, precision, recall, macro F1-score, dan confusion matrix. Hasil pengujian menunjukkan bahwa baseline memberikan performa terbaik dengan accuracy 0,750 dan macro F1-score 0,744. Fine-tuning menghasilkan accuracy yang sama, tetapi macro F1-score sedikit lebih rendah, sedangkan dua skenario augmentation hanya mencapai accuracy 0,528 dan 0,536. Kesalahan terbanyak terjadi pada kelas yang serupa secara visual. Simulasi finansial menunjukkan bahwa baseline memiliki risiko relatif terendah, meskipun nilai biaya masih berupa parameter ilustratif dan memerlukan validasi lapangan. Kata kunci: MobileNetV2; klasifikasi citra; hama pertanian; data augmentation; dampak finansial]]>
