Peningkatan Akurasi Klasifikasi Kematangan Buah Pisang Menggunakan CNN dengan Attention Mechanism
Abstrak
Penyortiran buah otomatis berbasis deep learning pada perangkat edge menghadapi tantangan besar karena keterbatasan sumber daya komputasi. Arsitektur ringan seperti MobileNetV2 sangat efisien untuk inferensi, namun seringkali kurang sensitif dalam menangkap fitur fine-grained seperti gradasi warna dan fitur halus pada tugas klasifikasi tingkat kematangan. Penelitian ini mengusulkan strategi integrasi modul Squeeze-and-Excitation (SE) secara selektif pada MobileNetV2 untuk meningkatkan akurasi tanpa membebani parameter model secara signifikan. Metodologi penelitian melibatkan pengujian lima skenario: Baseline, All Block, serta integrasi selektif pada blok Early (1-5), Middle (6- 11), dan Deep (12-17) guna merekalibrasi fitur pada berbagai tingkatan hierarki. Hasil eksperimen pada Banana Ripeness Classification Dataset menunjukkan bahwa skenario Early memberikan performa paling optimal dengan akurasi validasi tertinggi sebesar 96,79% dan nilai F1-score sebesar 0,9679. Skenario ini terbukti sangat efisien karena mempertahankan jumlah parameter pada angka 2,88 M, serupa dengan model baseline. Sebaliknya, integrasi pada seluruh blok (All Block) justru menurunkan akurasi menjadi 95,19% dan meningkatkan parameter menjadi 2,91 M. Analisis confusion matrix mengonfirmasi bahwa model memiliki tingkat misklasifikasi yang sangat rendah pada kelas unripe. Penelitian ini menyimpulkan bahwa penempatan modul SE pada tahap awal jaringan merupakan strategi terbaik untuk menjaga efisiensi dan presisi klasifikasi pada perangkat edge.
Kata kunci: deep learning, edge computing, klasifikasi kematangan buah, mobilenetv2, squeeze-and- excitation.
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