Penerapan Multi-Label Image Classification Menggunakan Metode Convolutional Neural Network (CNN) Untuk Sortir Botol Minuman
DOI:
https://doi.org/10.22441/jte.2022.v13i1.009Keywords:
Artificial Intelegance (AI), Multi-Label Image Classification, Convolutional Neural Network (CNN), Sortir botol minumanAbstract
Dalam industri Fast Moving Consumer Goods (FMCG) seperti air minum kemasan, quality control berperan penting untuk menjaga produk yang baik, oleh karena itu diperlukan metode yang cepat dan andal untuk menganalisis data lapangan dan memberikan informasi yang dapat membantu dalam menentukan kualitas produk. Selama proses produksi di pabrik, botol air minum sering kali memiliki beberapa cacat misalnya, tutup dan label yang tidak ada atau tidak terpasang dengan benar, serta air yang kurang terisi pada botol. Semua masalah tersebut dapat menurunkan kualitas produk yang dikirim ke konsumen. Untuk mengatasinya, inspeksi visual menjadi teknik wajib di lini produksi. Metode inspeksi visual tradisional sering kali memerlukan beberapa kamera dan perangkat untuk mendeteksi beberapa cacat pada produk, masing-masing digunakan untuk mendeteksi jenis masalah yang berbeda. Dalam penelitian ini, digunakan metode Multi-Label Image Classification dengan Convolutional Neural Network (CNN) sebagai algoritma untuk mendeteksi beberapa cacat pada botol air sekaligus. Terdapat enam class label yaitu isi kurang, isi kosong, tutup rusak, tutup tidak ada, label rusak, dan label tida ada. Masing-masing class label tersebut mewakili kemungkinan adanya cacat yang ada pada botol. Beberapa kombinasi feature learning layer dan fully connected layer digunakan untuk mengekstrak pola dan mengklasifikasikan gambar masukan. Untuk memfasilitasi penelitian ini, dikumpulan dataset gambar botol air dengan skala besar, dimana didalam datset tersebut merepresentasikan enam class label yang telah ditentukan. Saat diuji dengan dataset baru, model CNN mendapatkan hasil akurasi prediksi 98,526%, dan mendapat rata-rata akurasi sebesar 97,71% ketika diuji dengan 10-fold cross validation.Downloads
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