Implementasi Model MobileNetV2 pada Aplikasi Mobile untuk Deteksi Daun Melati, Kemuning, dan Mondokaki

Authors

  • Allvintantyo Fristya Gita Nanda Universitas Nusantara PGRI Kediri Indonesia
  • Ratih Kumalasari Niswatin Universitas Nusantara PGRI Kediri Indonesia
  • Danar Putra Pamungkas Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/p4sk1856

Abstract

Tanaman Mondokaki, Melati, dan Kemuning memiliki ciri morfologi daun yang sangat identik pada fase vegetatif, sehingga rentan memicu kesalahan identifikasi manual. Penelitian ini bertujuan mengembangkan aplikasi Android cerdas berbasis MobileNetV2 dengan teknik transfer learning untuk melakukan klasifikasi citra daun secara otomatis dan offline. Pemodelan dilakukan menggunakan 600 dataset citra alami yang dibagi sama rata antara 3 kelas, dengan prapemrosesan berupa interpolasi bilinear dan normalisasi piksel. Berkas model terbaik dikonversi menjadi format TensorFlow Lite dan diintegrasikan ke perangkat seluler. Hasil evaluasi model secara internal menggunakan confusion matrix mencatatkan tingkat akurasi sebesar 98,68%. Sementara itu, pengujian performa aplikasi di lapangan menggunakan 30 sampel citra baru menghasilkan akurasi operasional sebesar 93,33%. Aplikasi ini berhasil mengimplementasikan logika confidence threshold 80% untuk menyaring objek asing, memberikan solusi identifikasi yang cepat tanpa perlu harus menunggu tanaman mengalami pembungaan.

Keywords:

Aplikasi Android, Klasifikasi Citra, Daun Tanaman Hias, MobileNetV2, Transfer Learning

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Published

2026-07-20

Issue

Vol. 10 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

Section

Articles

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How to Cite

Implementasi Model MobileNetV2 pada Aplikasi Mobile untuk Deteksi Daun Melati, Kemuning, dan Mondokaki. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(1), 512-521. https://doi.org/10.29407/p4sk1856