Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

(1) Prasetya Widiharso Mail (Universitas Negeri Malang, Indonesia)
(2) Wahyu Tri Handoko Mail (Universitas Negeri Malang, Indonesia)
(3) * Aji Prasetya Wibawa Mail (Universitas Negeri Malang, Indonesia)
(4) Anik Nur Handayani Mail (Universitas Negeri Malang, Indonesia)
(5) Ming Foey Teng Mail (American University of Sharjah, United Arab Emirates)
*corresponding author

Abstract


Measuring the quality of river water used as industrial wastewater disposal is needed to maintain water quality from pollution. The chemical industry produces hazardous waste containing toxic materials and heavy metals. At specific concentrations, industrial waste can result in bacteriological contamination and excessive nutrient load (eutrophication). Using the Convolutional Neural Network (CNN), the method for measuring water quality processes remote sensing images taken via an RGB camera on an Unmanned Aerial Vehicle (UAV). The parameter measured is the change in the color of the river water image caused by the chemical reaction of the heavy metal content of industrial waste disposal. The test results of the Convolutional Neural Network (CNN) method in 2.01s/step obtained the value of training loss mode 17.86%, training accuracy 90.62%, validation loss 23.43%, validation accuracy 83.33%.

Keywords


Wastewater, River Water Check, Industrial Pollution, CNN

   

DOI

https://doi.org/10.31763/sitech.v2i2.638
      

Article metrics

10.31763/sitech.v2i2.638 Abstract views : 471

   

Cite

   

References


S. Yudo, “Kondisi pencemaran logam berat di perairan sungai dki jakarta,” J. Air Indones., vol. 2, no. 1, pp. 1–15, 2018, doi: 10.29122/jai.v2i1.2275.

S. Mariyam, S. Romdon, and E. Kosasih, “Teknik Pengukuran Oksigen Terlarut,” BULETIN TEKNIK LITKAYASA Sumber Daya dan Penangkapan, vol. 2, no. 1. p. 45, 2017, doi: 10.15578/btl.2.1.2004.45-47.

I. G. F. Frasiska, I. N. Budiastra, and P. Rahardjo, “Sistem pesawat tanpa awak menggunakan kamera thermal untuk membantu pencarian korban bencana alam,” vol. 7, no. 4, pp. 100–107, 2020.

H. S. Saroinsong, V. C. Poekoel, and P. D. . Manembu, “Rancang bangun wahana pesawat tanpa awak (fixed wing) berbasis ardupilot,” J. Tek. Elektro dan Komput., vol. 7, no. 1, pp. 73–84, 2018, doi: 10.35793/jtek.7.1.2018.19195.

A. Aqthobilrobbany, A. N. Handayani, D. Lestari, Muladi, R. A. Asmara, and O. Fukuda, “Hsv based robot boat navigation system,” CENIM 2020 - Proceeding Int. Conf. Comput. Eng. Network, Intell. Multimed. 2020, pp. 269–273, 2020, doi: 10.1109/CENIM51130.2020.9297915.

S. Desmanto, Irwan, and R. Angreni, “Penerapan alogaritma k-means clustering untuk pengelompokan citra digital dengan ekstraksi fitur warna rgb,” J. Inform. dan Apl., vol. 1, no. x, pp. 1–9, 2015.

A. Oktaviani and Yarjohan, “Perbandingan resolusi spasial, temporal dan radiometrik serta keandalannya,” J. Enggano, vol. 1, no. 2, pp. 74–79, 2016, doi: 10.31186/jenggano.1.2.74-79.

N. Suwargana, “Resolusi spasial, temporal dan spektral pada citra satelit landsat, spot dan ikonos,” Lemb. Penerbangan Antariksa Nas., vol. 1, 2013.

D. O. Pugas, M. Somantri, and K. I. Satoto, “Pencarian rute terpendek menggunakan alogaritma djikstra dan astar (a*) pada sig berbasis web untuk pemetaan pariwisata kota sawahlunto,” vol. 13, no. 1, pp. 27–32, 2011.

F. B. Simamora, B. Sasmito, and Hani’ah, “Kajian metode segmentasi untuk identifikasi tutupan lahan dan luas bidang tanah menggunakan citra pada google earth (studi kasus:kecamatan tembalang, semarang),” J. Geod. Undip, vol. 4, no. 4, pp. 43–51, 2015.

I. W. Suartika, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi citra menggunakan convolutional neural network (cnn) pada caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.

N. K. Qudsi, R. A. Asmara, and A. R. Syulistyo, “Identifikasi citra tulisan tangan digital menggunakan convolutional neural network (cnn),” pp. 48–53.

A. Krizhevsky and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” pp. 1–9.

C. Szegedy et al., “Going deeper with convolutions.”

V. Sangeetha and K. J. R. Prasad, “Deep residual learning for image recognition,” Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun, vol. 45, no. 8, pp. 1951–1954, 2006, doi: 10.1002/chin.200650130.

R. Rokhana, J. Priambodo, T. Karlita, I. M. G. Sunarya, and E. M. Yuniarno, “Convolutional neural network untuk pendeteksian patah tulang femur pada citra ultrasonik b – mode,” vol. 8, no. 1, 2019.

W. Setiawan, “Perbandingan arsitektur convolutional neural network untuk klasifikasi fundus,” vol. 7, no. 2, pp. 49–54, 2019.

B. M. H. Hidayat and R. E. Putra, “Penerapan cnn dengan filter gabor sebagai feature extractor untuk content-based image retrieval,” vol. 01, pp. 16–25, 2019.

A. P. Wibawa, W. A. Yudha Pratama, A. N. Handayani, and A. Ghosh, “Convolutional neural network (cnn) to determine the character of wayang kulit,” Int. J. Vis. Perform. Arts, vol. 3, no. 1, pp. 1–8, 2021, doi: 10.31763/viperarts.v3i1.373.

E. N. Arrofiqoh and Harintaka, “Implementasi metode convolutional neural network untuk klasifikasi tanaman pada citra resolusi tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.

C. Suardi, A. N. Handayani, R. A. Asmara, A. P. Wibawa, L. N. Hayati, and H. Azis, “Design of sign language recognition using e-cnn,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 166–170, 2021, doi: 10.1109/EIConCIT50028.2021.9431877.

K. Chaiyasarn, W. Khan, L. Ali, M. Sharma, D. Brackenbury, and M. DeJong, “Crack detection in masonry structures using convolutional neural networks and support vector machines,” ISARC 2018 - 35th Int. Symp. Autom. Robot. Constr. Int. AEC/FM Hackathon Futur. Build. Things, 2018, doi: 10.22260/isarc2018/0016.

Yohannes, D. Udjulawa, and Febbiola, “Klasifikasi lukisan karya van gogh menggunakan convolutional neural network- support vector machine,” vol. 7, no. April, pp. 192–205, 2021.

Y. Peng et al., “CNN-SVM: a classification method for fruit fL image with the complex background,” IET Cyber-Physical Syst. Theory Appl., vol. 5, no. 2, pp. 1–5, 2020, doi: 10.1049/iet-cps.2019.0069.

Felix, S. Faisal, T. F. Butarbutar, and P. Sirait, “Implementasi cnn dan svm untuk identifikasi penyakit tomat via daun,” vol. 20, no. 2, pp. 117–134, 2019.

A. Rahim, K. Kusrini, and E. T. Luthfi, “Convolutional neural network untuk klasifikasi penggunaan masker,” Inspir. J. Teknol. Inf. dan Komun., vol. 10, no. 2, p. 109, 2020, doi: 10.35585/inspir.v10i2.2569.

Y. Harjoseputro, “Convolutional neural network (cnn) untuk pengklasifikasian aksara jawa,” Buana Inform., p. 23, 2018.

D. Fitriati, “Perbandingan kinerja cnn lenet 5 dan extreme learning machine pada pengenalan citra tulisan tangan angka,” vol. 2, no. 1, 2016.

K. Aryasa and W. Musu, “Sistem pakar otomatisasi baku mutu limbah pertambangan nikel menggunakan alogaritma supervised machine,” vol. 2, no. 1, pp. 42–53, 2016.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Prasetya Widiharso, Wahyu Tri Handoko, Aji Prasetya Wibawa, Anik Nur Handayani, Ming Foey Teng

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : andri@ascee.org, andri.pranolo.id@ieee.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0

View My Stats