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13 July 2026
Published
22 September 2026
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Abstract

Untuk mencegah faktor subjektivitas dan potensi human error saat pengecekan kualitas produk casting manufacturing secara manual, penelitian ini bertujuan mengembangkan sistem Automated Visual Inspection (AVI) berbasis AI. Metode yang digunakan dengan pendekatan Deep learning berbasis Convolutional Neural Network (CNN) dengan membandingkan kinerja antara arsitektur AlexNet dan Development architecture. Proses evaluasi menggunakan 6.633 citra riil hasil preprocessing yang terbagi atas kelas defect dan non-defect untuk data training dan validasi. Hasil eksperimen membuktikan keunggulan architecture AlexNet yang mencatatkan akurasi hingga 99,25% dengan validation loss sebesar 0,02481, sementara architecture pengembangan hanya menghasilkan tingkat akurasi 89,07%. Hasil analisis confusion matrix juga memperlihatkan tingkat kestabilan AlexNet yang sangat tinggi dengan nilai presisi, recall, serta F1-score melebihi 0,99. Kesimpulan dari penelitian ini menegaskan bahwa architecture AlexNet memiliki efektivitas yang jauh lebih tinggi dalam mendeteksi cacat pada komponen pengecoran logam, menjadikannya opsi yang sangat relevan untuk diaplikasikan dalam sistem kendali mutu otomatis di industri.

Keywords

Automated Visual Inspection Deep Learning Convolutional Neural Network AlexNet Casting Manufacturing Automated Visual Inspection; Deep learning; Convolutional Neural Network; AlexNet; Casting Manufacturing

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Ariefa, I., Sendie Yuliarto Margen, Della Kumalaningrum, Komar Roni, & Hawa Rizka. (2026). Automated Visual Inspection Pada Produk Casting Manufacturing Menggunakan Deep Convolutional Neural Network. Jurnal Ilmiah Momentum, 22(2), 33–39. https://doi.org/10.36499/jim.v22i2.15624
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