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14 results on '"Andrian, R."'

1. Computer vision in image detection case study of tree damage type using convolutional neural network (CNN) algorithm.

Author

Nopriyanto, Z., Andrian, R., and Safe'i, R.

Subjects
*CONVOLUTIONAL neural networks, *IMAGE processing, *FOREST monitoring, *FOREST health, *FEATURE extraction
Abstract

So far, the identification of 16 types of tree damage is still following the guidelines stated in the Forest Health Monitoring method. The types of tree damage can be recognized by human vision, as well as by computers. Computer vision allows computers to identify things that humans can remember. In this case, the study can be realized with computer vision to make work easier. This study aimed to identify 16 types of damage in Forest Health Monitoring using image data or photos with computer vision. The stages of this research are image acquisition (image acquisition), image processing (image preprocessing), and feature extraction (feature extraction). The results showed that computer vision could identify images in JPG/JPEG (Joint Photographic Experts) format, assisted by the Convolutional Neural Network (CNN) algorithm. The percentage value of success using the CNN algorithm reaches 99.06%, with an error detection of 0.94%. However, several classes were incorrectly identified, indicating that the dataset needs improvement. Thus, it can be concluded that identifying 16 types of damage using image data or photos with computer vision has been successfully carried out. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Confirmatory Method for Sulfamethazine Residues in Cattle and Swine Tissues, Using Gas Chromatography-Chemical Ionization Mass Spectrometry

Author

Arthur J Manuel, Manfred O Poeppel, Steven J. Stout, William A Steller, and Andrian R Dacunha

Subjects
Chemical ionization, Residue (complex analysis), Chromatography, Chemistry, Confirmatory technique, Amine gas treating, General Chemistry, Gas chromatography, Mass spectrometry, Ion
Abstract

A gas chromatographic (GC) method has been reported for the determination of sulfamethazine residues in cattle and swine tissues. The extracts from this procedure were found to be directly amenable to examination by gas chromatography–mass spectrometry (GC-MS), allowing positive confirmation of an apparent residue of sulfamethazine. Chemical ionization mass spectrometry (CIMS) was chosen as the MS technique because it generated an ion indicative of intact sulfamethazine and fragment ions indicative of the amine functionality and sulfanil moiety. Positive ion (PI) chemical ionization mass spectrometry was adequate by itself for a confirmatory technique. Negative ion (NI) chemical ionization mass spectrometry alone could not be used for the confirmatory analysis of sulfamethazine, but it did offer a means to check the quantitative data from the positive ion analyses and provided complementary confirmatory data. Satisfactory recoveries were obtained for sulfamethazine in swine and cattle tissues at the tolerance level of 0.1 ppm. Apparent sulfamethazine residues in control tissues were less than 0.01 ppm.

Published
1984

14. Web Scraping and Naïve Bayes Classification for Job Search Engine

Author

Slamet, C, Andrian, R, Maylawati, D S, Darmalaksana, W, and Ramdhani, M A

Abstract

Many organisations (government of non-government) use websites to share information of new recruitment for the workers. This information overflows on thousands of sites with various attributes and criteria. However, this availability forms a complex puzzle in the selection process and lead to inefficient runtime. This study proposes a simple method for job searching simplification through a construction and collaboration of web scraping technique and classification using Naive Bayes on search engine. This study is resulting an effective and efficient application for users to seek a potential job that fit in with their interests.

Published
2018

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