Analisis Performa CNN Arsitektur MobileNet Untuk Pengenalan Penyakit Pada Tanaman Bawang Merah

Authors

  • Hadi Rizky Dwi Via Prahesta Prahesta Universitas Nusantara PGRI Kediri Indonesia
  • Danar Putra Pamungkas Universitas Nusantara PGRI Kediri Indonesia
  • Patmi Kasih Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/fn6mgw27

Abstract

Penyakit pada tanaman bawang merah, seperti busuk bawah, jamur daun, dan serangan ulat, dapat menyebabkan penurunan kualitas dan kuantitas hasil panen. Deteksi dini penyakit sangat penting untuk mencegah kerugian lebih lanjut. Penelitian ini mengembangkan sistem klasifikasi otomatis berbasis citra menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNetV1. Dataset terdiri dari 250 citra daun bawang merah yang dikategorikan ke dalam empat kelas: normal, busuk bawah, jamur daun, dan ulat. Proses pelatihan menggunakan teknik augmentasi citra dan callbacks EarlyStopping serta ModelCheckpoint untuk mencegah overfitting. Model berhenti pada epoch ke-16 dan mencapai akurasi 92,00%, presisi 92,31%, recall 92,00%, serta F1-score 91,90%. Hasil ini menunjukkan bahwa arsitektur MobileNetV1 efektif dalam mengklasifikasikan penyakit pada tanaman bawang merah secara otomatis.

Keywords:

Bawang merah, CNN, Klasifikasi, MobileNetV1, penyakit tanaman

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Published

2025-07-10

Issue

Vol. 9 No. 1 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

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Copyright (c) 2025 Hadi Rizky Dwi Via Prahesta Prahesta, Danar Putra Pamungkas, Patmi Kasih

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How to Cite

Analisis Performa CNN Arsitektur MobileNet Untuk Pengenalan Penyakit Pada Tanaman Bawang Merah. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(1), 406-412. https://doi.org/10.29407/fn6mgw27