Evaluasi Performa Sistem Deteksi Cacat Kemasan Pouch Berbasis Machine Vision pada Lini Produksi UMKM
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Inspeksi kualitas kemasan secara manual memiliki keterbatasan dalam hal subjektivitas dan konsistensi, khususnya pada volume produksi yang besar. Penelitian ini mengembangkan sistem deteksi cacat otomatis berbasis visi mesin untuk membedakan kemasan pouch normal dan kemasan pouch cacat. Pendekatan yang diusulkan mengekstraksi fitur bentuk dan tekstur dari citra kemasan untuk melatih tiga algoritma pembelajaran mesin, yaitu Random Forest, Support Vector Machine (SVM), dan Logistic Regression. Evaluasi model menunjukkan bahwa algoritma Random Forest mencapai tingkat akurasi tertinggi sebesar 100%, diikuti oleh regresi logistik sebesar 99%, dan SVM sebesar 97%. Analisis lebih lanjut mengungkap bahwa fitur rasio aspek, dengan kontribusi 35% dan tingkat kebulatan sebesar 19%, merupakan penentu utama dalam proses klasifikasi. Meskipun tingkat akurasi sangat tinggi, uji kewajaran hasil mendiagnosis adanya indikasi kebocoran data akibat ketidakkonsistenan dalam kondisi pengambilan citra. Kesimpulan dari studi ini menunjukkan bahwa sistem memiliki potensi besar untuk otomasi industri, namun implementasi operasional membutuhkan standardisasi yang ketat dalam akuisisi citra untuk menghindari shortcut learning pada model pembelajaran mesin.
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