DOI: https://doi.org/10.14710/jati.20.2.125-137

DETEKSI CACAT DAN PENGUKURAN JARUM JAHIT MENGGUNAKAN COMPUTER VISION DAN MACHINE LEARNING: TINJAUAN PUSTAKA SISTEMATIS (SLR)

*Fennyka Rahmawati  -  Universitas Diponegoro, Indonesia

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Abstract

Masih ditemukan kerusakan jahitan akibat interaksi antara jarum dan kain mempengaruhi optimalisasi operasi. Mesin jahit industri memiliki kecepatan tinggi dapat menyebabkan jarum jahit patah selama proses penjahitan. Patahan jarum jahit yang masih tertinggal akan memperburuk kerusakan pada serat kain. Deteksi cacat mendapat perhatian lebih bertujuan untuk menjaga kualitas produk. Pemanfaatan teknologi seperti computer vision dan machine learning mempermudah proses deteksi lebih cepat dan akurat. Basis data Scopus digunakan untuk mengekstrak artikel, yang mana hasil informasi akan divisualisasi perangkat lunak VOSViewer. Penelitian ini memberi gambaran umum yang komprehensif dan analisis bibliometrik dari studi publikasi terkait deteksi cacat dan pengukuran di bidang tekstil dalam kurun waktu 10 tahun terakhir yang didapatkan 131 artikel pada pencarian 23 Desember 2024. Walaupun terdapat peningkatan yang signifikan, namun tidak ditemukan pada deteksi cacat khusus jarum jahit dalam tren melainkan banyak ditemukan deteksi cacat pada jahitan dan kain. Metode yang paling sering digunakan adalah transformasi hough, GLCM, morphology sebagai fitur ekstraksi. Sementara dalam klasifikasi kecacatan yang paling banyak digunakan adalah support vector machine (SVM) dan Artificial Neural Network (ANN). Tiongkok memimpin jumlah publikasi terbanyak. Textile Research journal merupakan jurnal paling produktif dalam bidang penelitian ini.

 

Abstract

[Defect Detection and Sewing Needle Measurement Using Computer Vision and Machine Learning: Systematic Literature Review (SLR)] Sewing damage is still found due to the interaction between the needle and the fabric affecting the optimization of operations. Industrial sewing machines have high speeds that can cause sewing needles to break during the sewing process. Broken sewing needles that are still left will worsen the damage to the fabric fibers. More focus has been placed on defect identification in order to preserve product quality. The detection procedure is facilitated more quickly and precisely by the use of technologies like computer vision and machine learning. After articles are extracted from the Scopus database, the information is shown using VOSViewer software. The 131 papers that the search on December 23, 2024, turned up for this study's thorough examination and bibliometric analysis of published works regarding flaw detection and measurement in the textile sector during the past ten years. There was a noticeable rise in sewing and fabric fault detections, even if the trend did not find any particular issues with sewing needles. Artificial neural networks (ANN) and support vector machines (SVM) are the most used methods for classifying faults. The country with the most publications is China. The most fruitful journal in this area of study is Textile Research.

Keywords: bibliometric analysis; defect detection; broken needle; extraction method; classification method

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Keywords: analisis bibliometrik; deteksi cacat; jarum patah; metode ekstraksi, metode klasifikasi

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