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All Journal Science in Information Technology Letters Signal and Image Processing Letters
Widiharso, Prasetya
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks Widiharso, Prasetya; Handoko, Wahyu Tri; Wibawa, Aji Prasetya; Handayani, Anik Nur; Teng, Ming Foey
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.638

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%.
Letter Detection: An Empirical Comparative Study of Different ML Classifier and Feature Extraction Wibawa, Aji Prasetya; Putri, Nastiti Susetyo Fanany; Widiharso, Prasetya
Signal and Image Processing Letters Vol 5, No 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i1.45

Abstract

Work and communication activities are inextricably linked. Letters are an example of a communication medium that is still widely utilized. When it comes to significant job, however, simply an official letter is required. Official and private letters must be distinguished and classified. Different feature extraction methods, such as the count-vectorizer and TF-IDF vectorizer, are employed to transmit the detection of this official and personal letter. To categorize letters by type, various machine learning (ML) techniques are employed. Nave Bayes, Support vector machine, and AdaBoost are the algorithms. The accuracy measurements used in this study include accuracy scores, F1-mean, recall, and precision. The best working algorithm is Naïve Bayes for two vectorizer methods used, with an accuracy value of 98%.
Co-Authors Aji Prasetya Wibawa Anik Nur Handayani Ming Foey Teng, Ming Foey Putri, Nastiti Susetyo Fanany Wahyu Tri Handoko