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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Technologia: Jurnal Ilmiah Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Paradigma Jurnal Penelitian dan Pengabdian Masyarakat Jotika Indonesian Community Service Journal of Computer Science (IndoComs)
Rizka Dahlia
Universitas Bina Sarana Informatika
Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Medicine & Pharmacology Neuroscience Transportation Other

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Journal : Jurnal Riset Informatika

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Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.522

Abstract

Smallpox syndrome, also known as monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are both used in the data analysis process. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. In comparison to using conventional machine learning algorithms, the adagrad optimizer with learning rate 0.01 and 0.2 dropout has a higher value. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores when compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.217

Abstract

Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are used in data analysis. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Co-Authors Ali Mustopa, Ali Arief Rama Sena Arina Selawati Badariatul Lailiah Cucu Ika Agustyaningrum Fachrurozi, Ahmad Firdaus, Muhammad Rifqi Fitriana, Lady Agustin Frieyadie Hendri Mahmud Nawawi Lady Agustin Fitriana Lady Agustin Fitriana Lisnawanty Muhammad Ifan Rifani Ihsan Muhammad Rifqi Firdaus Nanik Wuryani Omar Pahlevi Rabiatus Saadah Rezki, Muhammad Sa'adah, Rabiatus Saadah, Rabiatus Siti Fauziah Siti Nurdiani Sri Hadianti Syarah Seimahuira Syarah Seimahuira Wahyu Nugraha Windu Gata