The hybrid design of supervised learning algorithm for design and development in classifications a defect in clay tiles

Authors

  • Murman Dwi Prasetio School of Industrial and System Engineering, Telkom University, Bandung http://orcid.org/0000-0001-7507-5005
  • Rais Yufli Xavier School of Industrial and System Engineering, Telkom University, Bandung
  • Haris Rachmat School of Industrial and System Engineering, Telkom University, Bandung
  • Wiyono Wiyono School of Industrial and System Engineering, Telkom University, Bandung
  • Denny Sukma Eka Atmaja School of Industrial and System Engineering, Telkom University, Bandung

DOI:

https://doi.org/10.12928/ijio.v2i2.4449

Keywords:

Sentiment Analysis, Support Vector Machine, Naïve Bayes Classifier.

Abstract

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards.  One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight.  It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods.  Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

References

Aprilla, S., Furqon, M. T., & Fauzi, M. A. (2018). Klasifikasi Penyakit Skizofrenia dan Episode Depresi Pada Gangguan Kejiwaan Dengan Menggunakan Metode Support Vector Machine ( SVM ). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(11), 5611–5618.

Atmaja, D. S. E., & Herliansyah, M. K. (2015). Optimasi Proses Pengukuran Dimensi Dan Defect Ubin Keramik Menggunakan Pengolahan Citra Digital Dan Full Factorial Design. Jurnal Teknosains, 4(2), 179–191. https://doi.org/10.22146/teknosains.7972.

Bilghifary, M., Rachmat, H., & Sjafrizal, T. (2015). Perancangan User Requirements Specification ( URS ) Sistem Otomasi Terintegrasi Pada Stasiun Exturning, Drilling- Chamfering, Dan Threading Di Pt . Abc Design of User Requirements Specification ( URS ) Integrated Automation System in Exturning, Drillin. 2(2), 3923–3957.

Bong, H. Q., Truong, Q. B., Nguyen, H. C., & Nguyen, M. T. (2019). Vision-based Inspection System for Leather Surface Defect Detection and Classification. NICS 2018 - Proceedings of 2018 5th NAFOSTED Conference on Information and Computer Science, 300–304. https://doi.org/10.1109/NICS.2018.8606836.

BPS. (2019). Perkembangan Indeks Produksi Industri Triwulanan Industri Mikro dan Kecil 2017-2019. Badan Pusat Statistik. https://www.bps.go.id/publication/2019/12/16/4bbaf229c500ad2439aa73f3/perkembangan-indeks-produksi-triwulanan-industri-mikro-dan-kecil-2017-2019.html.

Helyudanto, D., Nhita, F., & Rohmawati, A. A. (2019). Prediksi Penyebaran Demam Berdarah di Kabupaten Bandung dengan Metode Hybrid Autoregressive Integrated Moving Average ( ARIMA ) dengan Support Vector Machine ( SVM ).

Jawahar, M., Babu, N. K. C., & Vani, K. (2015). Leather texture classification using wavelet feature extraction technique. 2014 IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2014, 6–9. https://doi.org/10.1109/ICCIC.2014.7238475.

Jiang, F., & Yin, G. (2018). Bayesian Outdoor Defect Detection. 14(8), 1–9. http://arxiv.org/abs/1809.01000.

Junaidi, J., Koriatul, J., & Sutrisno. (2019). Model Aplikasi Purchasing System Untuk Monitoring Stok Dalam Mengurangi Tingkat. Sensi, 5(1), 86–98. http://ejournal.raharja.ac.id/index.php/sensi/article/download/745/565.

Kusumawati, A., & Fitriyeni, L. (2017). Pengendalian Kualitas Proses Pengemasan Gula Dengan Pendekatan Six Sigma. Jurnal Sistem Dan Manajemen Industri, 1(1), 43. https://doi.org/10.30656/jsmi.v1i1.173.

Li, Z., Zhang, J., Zhuang, T., & Wang, Q. (2018). Metal surface defect detection based on MATLAB. Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018, IAEAC, 2365–2371. https://doi.org/10.1109/IAEAC.2018.8577540.

Luo, X. C. (2014). A hybrid SVM-QPSO model-based ceramic tube surface defect detection algorithm. Proceedings - 2014 5th International Conference on Intelligent Systems Design and Engineering Applications, ISDEA 2014, 28–31. https://doi.org/10.1109/ISDEA.2014.15.

Oktaviani, I., & Budiman. (2019). Klasifikasi Jenis Batuan Pasir Sedimen Melalui Pengolahan Citra Digital Menggunakan Metode Local Binary Pattern ( LBP ) Dan Support Vector Machine ( SVM ).

Prasetio, M. D., Hayashida, T., Nishizaki, I., & Sekizaki, S. (2018). Deep belief network optimization in speech recognition. Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017, 2018-Janua, 138–143. https://doi.org/10.1109/SIET.2017.8304124.

Prasetio, M. D., & Xavierullah, R. Y. (2020). An Approaching Machine Learning Model: Tile Inspection Case Study. International Journal of Innovation in Enterprise System, 4(01), 12–22. https://doi.org/10.25124/ijies.v4i01.44.

Ragab, K., & Alsharay, N. (2017). Developing Parallel Cracks and Spots Ceramic Defect Detection and Classification Algorithm Using CUDA. Proceedings - 2017 IEEE 13th International Symposium on Autonomous Decentralized Systems, ISADS 2017, 255–261. https://doi.org/10.1109/ISADS.2017.14.

Shadika. (2017). Optimasi Klasifikasi Cacat Pada Kain Tenun Gorden Menggunakan Metode Image Processing Dan Metode Artificial Neural Network di Pt Buana Intan Gemilang.

Downloads

Additional Files

Published

2021-09-01

How to Cite

Prasetio, M. D., Xavier, R. Y., Rachmat, H., Wiyono, W., & Atmaja, D. S. E. (2021). The hybrid design of supervised learning algorithm for design and development in classifications a defect in clay tiles. International Journal of Industrial Optimization, 2(2), 141–150. https://doi.org/10.12928/ijio.v2i2.4449

Issue

Section

Articles