Analisis Citra Pada Potret Botol Minuman Bekas Menggunakan Algoritma Convolutional Neural Network
DOI:
https://doi.org/10.22441/jte.2024.v15i3.003Keywords:
Botol minuman bekas, Convolutional Neural Network (CNN), Ensemble method, Klasifikasi citra, MobileNetV2Abstract
Hadirnya kehidupan manusia yang menggunakan berbagai jenis produk berpotensi menghasilkan beraneka ragam sampah. Dalam situs resmi Sistem Informasi Pengelolaan Sampah Nasional (SIPSN), urutan kedua pada produksi sampah nasional tahun 2021 adalah sampah plastik. Sampah botol minuman bekas berbahan dasar plastik membutuhkan waktu yang lama agar dapat terurai. Di antara solusinya ialah dengan melakukan daur ulang sampah. Sebagian pengelola daur ulang masih menggunakan teknik penyortiran sampah secara manual, sehingga dibutuhkan sistem klasifikasi agar mempermudah penyortiran sampah botol minuman bekas. Pada penelitian ini, dilakukan perancangan sistem dengan algoritma Convolutional Neural Network (CNN), di mana terdapat beberapa model CNN tunggal dengan base model dari MobileNetV2 dan dimodifikasi pada bagian head model atau fully-connected layer, sehingga menghasilkan arsitektur MobileNetV2 dengan tiga macam head model, yaitu Head Model 1, Head Model 2, dan Head Model 3. Selain itu, diusulkan metode ensemble yang diterapkan pada seluruh model CNN tunggal dengan mengambil nilai bobot (weight) yang diperoleh setelah pelatihan, kemudian dilakukan proses bobot rata-rata (average weights) untuk meningkatkan performa pengklasifikasian gambar. Bentuk model baru ini dikenal dengan Ensemble Convolutional Neural Network (E-CNN). Berdasarkan hasil pengujian yang dilakukan, model CNN mampu mengklasifikasikan citra pada potret botol minuman bekas secara akurat dan efektif. Hal ini ditunjukkan pada nilai classification report pengujian, yaitu penggunaan model CNN tunggal berupa arsitektur MobileNetV2 dengan Head Model 1, Head Model 2, dan Head Model 3 memiliki nilai accuracy berturut-turut sebesar 91%, 89%, dan 91%. Selain itu, setelah diterapkan metode ensemble dan menjadi model E-CNN, maka didapatkan nilai accuracy pengujian sebesar 98%, di mana terjadi peningkatan nilai accuracy sebesar 7% hingga 9%.Downloads
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