Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Authors

  • Rahmad Syah Universitas Medan Area
  • Al-Khowarizmi Al-Khowarizmi Universitas Medan Area

DOI:

https://doi.org/10.26555/jiteki.v7i1.20508

Keywords:

Optimization, MAPE, Detection rate, SECoS, Classification

Abstract

Digital image processing in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using MAPE for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of ANN is very suitable for optimizing classification using the Simple Evolving Connectionist System Method and as the result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.

References

A. V. Iatsyshyn et al., “Formation of the scientist image in modern conditions of digital society transformation,†In Journal of Physics: Conference Series, vol. 1840, no. 1, pp. 012039, 2021, IOP Publishing. https://doi.org/10.1088/1742-6596/1840/1/012039

R. Sa’ari, N. A. Rahman, Z. M. Yusof, S.K. Ngien, S.A. Kamaruddin, and M.A. Hezmi, “Application of digital image processing technique in monitoring LNAPL migration in double porosity soil column,†Jurnal Teknologi, vol. 72, no. 3, pp. 23-29, 2015. https://doi.org/10.11113/jt.v72.4018

A.M. Alawad, A. Rahman, F. Diyana, O.O. Khalifa, and N.A. Malek, “Fuzzy Logic based Edge Detection Method for Image Processing,†International Journal of Electrical & Computer Engineering, vol. 8, no. 3, pp. 2088-8708, 2018. https://doi.org/10.11591/ijece.v8i3.pp1863-1869

A. Khmag, S. Ghoul, S.A.R. Al-Haddad, and N. Kamarudin, “Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal,†Telkomnika, vol. 16, no. 2, pp.915-924, 2018. https://doi.org/10.12928/telkomnika.v16i2.9060

M. Nixon and A. Aguado, “Feature extraction and image processing for computer vision,†Academic Press, 2019. https://doi.org/10.1016/B978-0-12-814976-8.00003-8

H.A. Wibawa and P.S. Sasongko, “Detection of Ship using Image Processing and Neural Network,†Telkomnika, vol. 16, no. 1, pp.259-264, 2018. https://doi.org/10.12928/telkomnika.v16i1.7357

A. R. Lubis, S. Prayudani, M. Lubis, and A. Al-Khowarizmi, “Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method, “ Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 3, 2018. https://doi.org/10.11591/ijeecs.v12.i3.pp1273-1281

M. Pojić, A. Mišan, and B. Tiwari, “Eco-innovative technologies for extraction of proteins for human consumption from renewable protein sources of plant origin,†Trends in Food Science & Technology, vol. 75, pp. 93-104, 2018. https://doi.org/10.1016/j.tifs.2018.03.010

C. Hartmann and M. Siegrist, “Consumer perception and behaviour regarding sustainable protein consumption: A systematic review,†Trends in Food Science & Technology, vol. 61, pp. 11-25, 2017. https://doi.org/10.1016/j.tifs.2016.12.006

R. Rahmadianto, E. Mulyanto, and T. Sutojo, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbor untuk Mendeteksi Kualitas Telur Ayam,†Jurnal VOI (Voice of Informatics), vol. 8, no. 1, 2019.

A. Muzami, O.D. Nurhayati, and K.T. Martono, “Aplikasi Identifikasi Citra Telur Ayam Omega-3 Dengan Metode Segmentasi Region of Interest Berbasis Android,†Jurnal Teknologi dan Sistem Komputer, vol. 4, no. 2, pp.380-388, 2016. https://doi.org/10.14710/jtsiskom.4.2.2016.380-388

H. Jiang, S. C. Yoon, H. Zhuang, W. Wang, K.C. Lawrence, and Y. Yang, “Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging,†Meat science, vol. 139, pp. 82-90, 2018. https://doi.org/10.1016/j.meatsci.2018.01.013

M. S. A. M. Ali, A.H. Jahidin, M. N. Taib, and N. M. Tahir, “EEG sub-band spectral centroid frequency and amplitude ratio features: A comparative study in learning style classification,†Jurnal Teknologi, vol. 78, no. 2, 2016. https://doi.org/10.11113/jt.v78.4100

R. Syah, M. K. M Nasution, E. B Nababan, S. Efendi, “Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas.,†TELKOMNIKA, vol. 19, no. 4, 2021. http://dx.doi.org/10.12928/telkomnika.v19i4.18872

S. Prayudani, A. Hizriadi, Y. Y. Lase, Y. Fatmi, and A. Al-Khowarizmi, “Analysis Accuracy of Forecasting Measurement Technique on Random K-Nearest Neighbor (RKNN) Using MAPE and MSE,†In Journal of Physics: Conference Series, vol. 1361, no. 1, pp. 012089), 2019. IOP Publishing. https://doi.org/10.1088/1742-6596/1361/1/012089

S. S. Kamaruddin, Y. Yusof, H. Husni, and M.H. Al Refai, “Text classification using modified multi class association rule,†Jurnal Teknologi, vol. 78, no. 8, 2016. https://doi.org/10.11113/jt.v78.9553

Al-Khowarizmi, R. Syah, M. K. M. Nasution, M. Elveny. Sensitivity of MAPE using Detection Rate for Big Data Forecasting Crude Palm Oil on k-Nearest Neighbor. Vol. 19, no. 4. International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 3, 2021. https://doi.org/10.11591/ijece.v11i3.pp2696-2703

B. Bozkurt, I. Germanakis, and Y. Stylianou, “A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection,†Computers in biology and medicine, vol. 100, pp.132-143, 2018. https://doi.org/10.1016/j.compbiomed.2018.06.026

N. L. Hairuddin, L. M. Yusuf, M. S. Othman, and H. A. Majid,†Improving Gender Classification with Feature Selection in Forensic Anthropology,†Jurnal Teknologi, vol. 78, no. 12, 2016. https://doi.org/10.11113/jt.v78.10143

A. Al-Khowarizmi and S. Suherman,†Classification of skin cancer images by applying simple evolving connectionist system,†IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 2, 2021.

A. R. Lubis, M. Lubis, A. Al-Khowarizmi and D. Listriani, “Big Data Forecasting Applied Nearest Neighbor Method,†In 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC), pp. 116-120, 2019. https://doi.org/10.1109/ICSECC.2019.8907010

R. Syah., M. Nasution, M. Elveny, and H. Arbie,†Optimization Model for Customer Behavior with Mars and KYC System,†Journal of Theoretical and Applied Information Technology, vol. 98, no. 13, 2020.

N. K Kasabov, “Artificial neural networks. evolving connectionist systems,†In Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, pp. 39-83, 2019, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_2

A. Al-Khowarizmi, O. S. Sitompul, S. Suherman, and E.B Nababan, “Measuring the Accuracy of Simple Evolving Connectionist System with Varying Distance Formulas,†In Journal of Physics: Conference Series, vol. 930, no. 1, 2017. IOP Publishing. https://doi.org/10.1088/1742-6596/930/1/012004

A. Al-Khowarizmi, I. R. Nasution, M. Lubis, and A.R. Lubis, “The effect of a SECoS in crude palm oil forecasting to improve business intelligence,†Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, 2020. https://doi.org/10.11591/eei.v9i4.2388

A. Al-Khowarizmi, “Modification of the SECoS Method uses the Distance Formula,†Thesis Computer Science, Universitas Sumatera Utara, 2017.

A. Y. Lam, Y. Li, D. L. Gregory, J. Prinz, J. O’Reilly, M. Manka, and R.N. Keswani, “Association between improved adenoma detection rate and interval colorectal cancer rates after a quality improvement program,†Gastrointestinal Endoscopy, 2020. https://doi.org/10.1016/j.gie.2020.02.016

A. R. Lubis, M. Lubis, and Al-Khowarizmi,†Optimization of distance formula in K-Nearest Neighbor method,†Bulletin of Electrical Engineering and Informatics, vol. 9, no. 1, pp. 326-338, 2020. https://doi.org/10.11591/eei.v9i1.1464

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Published

2021-04-23

How to Cite

[1]
R. Syah and A.-K. Al-Khowarizmi, “Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 7, no. 1, pp. 154–160, Apr. 2021.

Issue

Vol. 7 No. 1 (2021): April

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