PERANCANGAN SISTEM PREDIKSI PENYAKIT PARU-PARU (TORAX) BERBASIS WEBSITE
In identifying pneumonia, medical images such as X-rays are needed to make it easier to recognize the characteristics of the disease. Although the condition of lung inflammation can be seen easily through X-rays, the quality of the resulting images is not always good, and they tend to be vague and h...
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2023-01-10.
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| 100 | 1 | 0 | |a Muh. Ahyan Saputra, . |e author |
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| 260 | |c 2023-01-10. | ||
| 500 | |a http://repository.upnvj.ac.id/22886/1/ABSTRAK.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/22886/5/BAB%20III.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/22886/10/LAMPIRAN.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/22886/12/HASIL%20PLAGIARISME.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/22886/11/ARTIKEL%20KI.pdf | ||
| 520 | |a In identifying pneumonia, medical images such as X-rays are needed to make it easier to recognize the characteristics of the disease. Although the condition of lung inflammation can be seen easily through X-rays, the quality of the resulting images is not always good, and they tend to be vague and have similarities between other types of lung diseases. To reduce errors in diagnosing lung diseases, several previous studies have developed a system to identify pneumonia, covid-19, Tuberculosis, infiltration, atelectasis and pleural effusion. The system developed uses machine learning technology using Convolutional Neural Network (CNN). Similarly, this research focuses on identifying lung pneumonia, covid-19, Tuberculosis, infiltration, atelectasis and pleural effusion with the Website-based CNN algorithm. The system development method used is the waterfall method. The expected final result is that the system can identify normal lungs and pneumonia lungs with an accuracy rate of more than 98%. | ||
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