Sistem Klasifikasi Citra untuk Proses Inspeksi Kain Menggunakan Teachable Machine dan Raspberry Pi

  • Emmanuel Agung Nugroho Politeknik Enjinering Indorama
  • Diki Mulyadi
  • Nanang Roni wibowo
Keywords: Kata kunci: machine learning; image Processing; Raspberry PI; inference time; image classification.

Abstract

The textile industry has been growing rapidly, even the growth of textile products exceeds the growth of the national economy. The demand for textile products is not only for domestic consumption but also for export. In an effort to meet quality standards and maintain customer satisfaction, quality control of fabric production is very important, especially in controlling fabric production defects.  The types of defects that exist in fabrics are holes, stains, rare defects due to broken/lost threads, floating, color fading, broken patterns, double threads, thick threads (slubs), mixed ends, pin marks, and others. In this research, a system is designed that can detect production defects in fabrics using machine learning-based image processing methods using Raspberry Pi. The types of defects modeled are sparse defects and stain defects, or in factory terms often called slap defects.  The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an image classification result accuracy of 98.4%

References

Abou Baker, N., Zengeler, N., & Handmann, U. (2022). A Transfer Learning Evaluation of Deep Neural Networks for Image Classification. Machine Learning and Knowledge Extraction, 4(1), 22–41. https://doi.org/10.3390/make4010002
Abu, M. A., Indra, N. H., Rahman, A. H. A., Sapiee, N. A., & Ahmad, I. (2019). A study on image classification based on deep learning and tensorflow. International Journal of Engineering Research and Technology, 12(4), 563–569.
Alaskar, H., & Saba, T. (2021). Machine Learning and Deep Learning: A Comparative Review. June, 143–150. https://doi.org/10.1007/978-981-33-6307-6_15
Bandyopadhyay, H. (2022). What Is Computer Vision? [Basic Tasks & Techniques]. V7.
Chazar, C., & Rafsanjani, M. H. (2022). LPPM STMIK ROSMA / Prosiding Seminar Nasional : Inovasi & Adopsi Teknologi Penerapan Teachable Machine Pada Klasifikasi Machine Learning Untuk Identifikasi Bibit Tanaman.
Dewanti, Firma, D., & Pujotomo, D. (2017). Analisis Penyebab cacat Produk Kain dengan Menggunakan Metode FMEA. Jurnal Teknik Industri, 6(4), 1–7.
Dompeipen, T. A., & Sompie, S. R. U. . (2020). Penerapan computer vision untuk pendeteksian dan penghitung jumlah manusia. Jurnal Teknik Informatika, 15(4), 1–12.
Han, S.-H., Kim, K. W., Kim, S., & Youn, Y. C. (2018). Artificial Neural Network: Understanding the Basic Concepts without Mathematics. Dementia and Neurocognitive Disorders, 17(3), 83. https://doi.org/10.12779/dnd.2018.17.3.83
Kamavisdar, P., Saluja, S., & Agrawal, S. (2013). ). A survey on image classification approaches and techniques. Nternational Journal of Advanced Research in Computer and Communication Engineering. 2(1), 1005–1009.
Kementerian Perindustrian. (2019). Kemenperin: Industri Tekstil dan Pakaian Tumbuh Paling Tinggi. Kementrian Perindustrian.
Kementrian Perindustrian. (2021). Mendorong Kinerja Industri Tekstil dan Produk Tekstil di Tengah Pandemi. Buku Analisis Pembangunan Industri, 1–37.
Kristanto, A. Y., Rumita, R., & Sriyanto. (2016). Analisis Penyebab Cacat Kain dengan Menggunakan Metode Failure Mode and Effect Analysis (FMEA) dan Fault Tree Analysis (FTA). Industrial Engineering Online Journal, 5(1), 1–8.
Malahina, E. A. U., Hadjon, R. P., & Bisilisin, F. Y. (2022). Teachable Machine: Real-Time Attendance of Students Based on Open Source System. The IJICS (International Journal of Informatics and Computer Science), 6(3), 140. https://doi.org/10.30865/ijics.v6i3.4928
Maulana, I. (2021). Implementasi Raspberry Pi 4 Sebagai Server E-Learning. Jurnal Media Aplikom, 13(2), 53–67.
Pintelas, E., Liaskos, M., Livieris, I. E., Kotsiantis, S., & Pintelas, P. (2020). Explainable machine learning framework for image classification problems: Case study on glioma cancer prediction. Journal of Imaging, 6(6). https://doi.org/10.3390/JIMAGING6060037
Prasad, P. Y., Prasad, D., Malleswari, N., Shetty, M. N., & Gupta, N. (2022). Implementation of Machine Learning Based Google Teachable Machine in Early Childhood Education. International Journal of Early Childhood Special Education, 14(3), 4132–4138.
Pratama, Y., Lestari, U., & Hamzah, A. (2022). Pemanfaatan Aplikasi Teachable Machine Untuk Pengenalan Binatang Menggunakan Konsep Convolutional Neural Network (Cnn). 10(1), 10–20.
Purno, A., & Wibowo, W. (2016). Implementasi Teknik Computer Vision Dengan Metode Colored Markers Trajectory Secara Real Time. Jurnal Teknik Informatika, 8(1), 38–42.
Setiawan, F. B., Kusuma, H. W., Riyadi, S., & Pratomo, leonardo H. (2022). Penerapan PI Cam Menggunakan Program Berbasis Raspberry PI 4. CYCLOTRON : Jurnal Teknik Elektro, 5(2), 51–56.
Wijaya, I. D., Nurhasan, U., & Barata, M. A. (2017). Implementasi Raspberry Pi Untuk Rancang Bangun Sistem Keamanan Pintu Ruang Server Dengan Pengenalan Wajah Menggunakan Metode Triangle Face. Jurnal Informatika Polinema, 4(1), 9. https://doi.org/10.33795/jip.v4i1.138
Published
2024-05-31