Implementation of Adaptive Neuro-Fuzzy Inference System and Image Processing for Design Applications Paper Age Prediction

Valeria Cynthia Dewi; Victor Amrizal; Fenty Eka Muzayyana Agustin

doi:10.59976/jurit.v1i1.6

Authors

Valeria Cynthia Dewi Universitas Islam Negeri Syarif Hidayatullah
Victor Amrizal Universitas Islam Negeri Syarif Hidayatullah
Fenty Eka Muzayyana Agustin Universitas Islam Negeri Syarif Hidayatullah

DOI:

https://doi.org/10.59976/jurit.v1i1.6

Abstract

The development of technology today is widely misused by some people who intend to forge paper on documents and books. One way to find out the authenticity of a paper is by knowing its age. The age of paper can be known in several ways: carbon dating, uranium dating, and potassium-argon dating. But these methods still have weaknesses, requiring sophisticated equipment at a high cost, long processes to get results and limited access. To solve this problem, researchers made an application that can identify the age range of a sheet of paper with a faster process, low cost and does not have to be used by laboratory employees alone. The application is a Paper Age Prediction Application made desktop-based, using the MATLAB programming language with the Anfis Sugeno (TSK) Gaussian membership function method. Image processing by taking the average values of C, M, Y, and K from 70 images used as a database and will be trained with ANFIS. The research method uses interviews, observations, and literature studies—the prototype application development method. The test results showed an application success rate in identifying 60 data that had been trained by 100% against 40 that had not been trained by 42.5%.

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References

K. Elbaz, “Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm,” Appl. Sci., vol. 9, no. 4, 2019, doi: 10.3390/app9040780.

S. Maroufpoor, “Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm,” J. Hydrol., vol. 575, pp. 544–556, 2019, doi: 10.1016/j.jhydrol.2019.05.045.

N. Nabipour, “Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system,” Eng. Appl. Comput. Fluid Mech., vol. 14, no. 1, pp. 491–506, 2020, doi: 10.1080/19942060.2020.1722241.

H. Moayedi, “Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide,” Geomatics, Nat. Hazards Risk, vol. 10, no. 1, pp. 1879–1911, 2019, doi: 10.1080/19475705.2019.1650126.

A. Azad, “Modeling river water quality parameters using modified adaptive neuro fuzzy inference system,” Water Sci. Eng., vol. 12, no. 1, pp. 45–54, 2019, doi: 10.1016/j.wse.2018.11.001.

A. Jaafari, “Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility,” Catena, vol. 175, pp. 430–445, 2019, doi: 10.1016/j.catena.2018.12.033.

Z. Alameer, “Forecasting Copper Prices Using Hybrid Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms,” Nat. Resour. Res., vol. 28, no. 4, pp. 1385–1401, 2019, doi: 10.1007/s11053-019-09473-w.

O. Ghorbanzadeh, “A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping,” Journal of Spatial Science, vol. 65, no. 3. pp. 401–418, 2020. doi: 10.1080/14498596.2018.1505564.

M. Hesami, “Application of adaptive neuro-fuzzy inference system-non-dominated sorting genetic algorithm-II (ANFIS-NSGAII) for modeling and optimizing somatic embryogenesis of chrysanthemum,” Front. Plant Sci., vol. 10, 2019, doi: 10.3389/fpls.2019.00869.

B. Mohammadi, “Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series,” Hydrol. Sci. J., vol. 65, no. 10, pp. 1738–1751, 2020, doi: 10.1080/02626667.2020.1758703.

A. Alsabaa, “Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system,” Sensors (Switzerland), vol. 20, no. 6, 2020, doi: 10.3390/s20061669.

D. K. Roy, “Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones,” J. Hydrol., vol. 591, 2020, doi: 10.1016/j.jhydrol.2020.125509.

M. Dehghani, “Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system,” Energies, vol. 12, no. 2, 2019, doi: 10.3390/en12020289.

S. V. R. Termeh, “Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping,” Hydrogeol. J., vol. 27, no. 7, pp. 2511–2534, 2019, doi: 10.1007/s10040-019-02017-9.

C. Xie, “Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms,” J. Rock Mech. Geotech. Eng., vol. 13, no. 6, pp. 1452–1465, 2021, doi: 10.1016/j.jrmge.2021.07.005.

M. Sabah, “Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field,” J. Pet. Sci. Eng., vol. 177, pp. 236–249, 2019, doi: 10.1016/j.petrol.2019.02.045.

V. K. Vishnoi, “Plant disease detection using computational intelligence and image processing,” Journal of Plant Diseases and Protection, vol. 128, no. 1. pp. 19–53, 2021. doi: 10.1007/s41348-020-00368-0.

K. Ding, “Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems,” Int. J. Comput. Vis., vol. 129, no. 4, pp. 1258–1281, 2021, doi: 10.1007/s11263-020-01419-7.

A. Azad, “Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling,” J. Hydrol., vol. 571, pp. 214–224, 2019, doi: 10.1016/j.jhydrol.2019.01.062.

M. Rejeesh, “Interest point based face recognition using adaptive neuro fuzzy inference system,” Multimed. Tools Appl., vol. 78, no. 16, pp. 22691–22710, 2019, doi: 10.1007/s11042-019-7577-5.

S. Hadi, “Synergistic degradation of 4-chlorophenol by persulfate and oxalic acid mixture with heterogeneous Fenton like system for wastewater treatment: Adaptive neuro-fuzzy inference systems modeling,” J. Environ. Manage., vol. 268, 2020, doi: 10.1016/j.jenvman.2020.110678.

F. Prado, “Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework,” Energy, vol. 197, 2020, doi: 10.1016/j.energy.2020.117159.

M. S. Zaghloul, “Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors,” J. Environ. Chem. Eng., vol. 8, no. 3, 2020, doi: 10.1016/j.jece.2020.103742.

H. I. Hussain, “An investigation of an adaptive neuro-fuzzy inference system to predict the relationship among energy intensity, globalization, and financial development in major ASEAN economies,” Energies, vol. 13, no. 4, 2020, doi: 10.3390/en13040850.

A. Nabavi-Pelesaraei, “Comprehensive model of energy, environmental impacts and economic in rice milling factories by coupling adaptive neuro-fuzzy inference system and life cycle assessment,” J. Clean. Prod., vol. 217, pp. 742–756, 2019, doi: 10.1016/j.jclepro.2019.01.228.

Y. J. Wong, “Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) pee,” Environ. Monit. Assess., vol. 192, no. 7, 2020, doi: 10.1007/s10661-020-08268-4.

Y. Wang, “Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm,” J. Environ. Manage., vol. 247, pp. 712–729, 2019, doi: 10.1016/j.jenvman.2019.06.102.

M. Mehrabi, “Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques,” Sensors (Switzerland), vol. 20, no. 6, 2020, doi: 10.3390/s20061723.

P. Sihag, “Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS),” ISH J. Hydraul. Eng., vol. 25, no. 2, pp. 132–142, 2019, doi: 10.1080/09715010.2017.1381861.

T. R. Cucu, “Implementasi Adaptive Neural Fuzzy Inference System (ANFIS) untuk Menguji Kualitas Printed Circuit Board (PCB),” Techno Xplore J. Ilmu Komput. dan Teknol. Inf., vol. 5, no. 2, pp. 79–90, 2020.

G. Suwanto and R. I. Adam, “Identifikasi Citra Digital Jenis Beras Menggunakan Metode Anfis dan Sobel,” J. Inform. Polinema, vol. 7, no. 2, pp. 123–128, 2021.

A. R. Firdaus, M. Lutfi, and M. F. Amrulloh, “Klasifikasi Jenis Tanaman Kelengkeng Berdasarkan Ciri Tekstur Daun Menggunakan Metode Adaptive Neuro Fuzzy Inference System (AFIS),” Explor. IT! J. Keilmuan dan Apl. Tek. Inform., vol. 14, no. 1, pp. 29–38, 2022.