Deteksi Cacat Otomatis Komponen Industri Menggunakan Faster R-Cnn Berbasis  Pembelajaran Mendalam

Anggit Suryopratomo; M. Syafaruddin Mahaputra; Jayadi

doi:10.59061/jentik.v4i3.1533

Authors

Anggit Suryopratomo Universitas Ma'soem
M. Syafaruddin Mahaputra Universitas Ma'soem
Jayadi Universitas Ma'soem

DOI:

https://doi.org/10.59061/jentik.v4i3.1533

Keywords:

Deteksi Cacat, Faster R-CNN, Deep Learning, Komponen Industri, Kontrol Kualitas

Abstract

Abstrak. Inspeksi cacat pada komponen industri menjadi tahapan kritis yang secara langsung menentukan kualitas produk akhir. Metode manual masih memiliki keterbatasan dalam hal konsistensi dan efisiensi, sementara pendekatan konvensional berbasis machine vision menghadapi kendala pada kompleksitas tekstur serta variasi pencahayaan. Penelitian ini membangun sistem deteksi cacat otomatis menggunakan Faster R-CNN dengan backbone ResNet-101 dan Feature Pyramid Network (FPN). Dataset yang digunakan terdiri dari 2.500 citra komponen industri dengan empat kelas: goresan, retakan, inklusi, dan normal. Data augmentation diterapkan untuk meningkatkan robustness model terhadap variasi kondisi operasional. Evaluasi pada data pengujian independen menghasilkan mAP@0,5 sebesar 96,8%, presisi 97,2%, recall 96,1%, dan F1-score 96,6%. Kecepatan inference mencapai 0,15 detik per citra, yang menunjukkan kelayakan untuk implementasi inspeksi near-real-time. Temuan ini memperkuat bahwa Faster R-CNN merupakan solusi efektif untuk otomasi inspeksi kualitas komponen industri.

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