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All Journal International Journal of Electrical and Computer Engineering Jurnal Gamma Transmisi: Jurnal Ilmiah Teknik Elektro Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri CYCLOTRON Multitek Indonesia : Jurnal Ilmiah Journal of Robotics and Control (JRC) Jurnal ABDINUS : Jurnal Pengabdian Nusantara Community Development Journal: Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Jurnal Nasional Teknik Elektro dan Teknologi Informasi SinarFe7 Equivalent: Jurnal Ilmiah Sosial Teknik
Zulfatman Zulfatman
Universitas Muhammadiyah Malang
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences

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Journal : JOIN (Jurnal Online Informatika)

 1 
Comparison of Machine Learning Classification Methods in Hepatitis C Virus Lailis Syafa’ah; Zulfatman Zulfatman; Ilham Pakaya; Merinda Lestandy
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.719

Abstract

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
Comparison of Machine Learning Classification Methods in Hepatitis C Virus Syafa’ah, Lailis; Zulfatman, Zulfatman; Pakaya, Ilham; Lestandy, Merinda
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.719

Abstract

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
Co-Authors A. F. Z. Abidin Ahzen Habibidin Muslim Ailin Rohmatul Fajria Akhyar Anadiansyah Al Ghozy, Naufal Ali, Nur Husnina Mohamad Alvian Widianto Amrul Faruq Anadiansyah, Akhyar Annas Alif Putra Arif, Denny Jumbo Ahmad Basolle, Azfar Waris Bella Retno Budhi Priyanto Cahyadi, Basri Nor Chai Mau Shern Chong Chee Soon Chong Chee Soon Delliar Khafid Nur Fahmi Delliar Khafid Nur Fahmi Dezar Septiantono Diding Suhardi Dirman Hanafi Dwi Nur Fajar Een Hutama Putra Effendy, Machmud Ermanu Azizul Hakim Fachmy Faizal Faizal, Fachmy Fajar, Dwi Nur Fajria, Ailin Rohmatul Fauziyah, Lailatul Fua'ad Rahmat, Mohd Ghani, Muhammad Fadli Gowdie Palmer Derai Haris Rahmana Putra Hazriq Izzuan Jaafar Humaidi, Haneef Nouval Alannibras Ilham Pakaya Jaafar, Hazriq Izzuan Jayanegara, M Mulyadi Khusnul Hidayat Komarudin Achmad Lailatul Fauziyah Lailis Syafa'ah Lailis Syafa’ah Leonardo Kamajaya M Mulyadi Jayanegara M. F. Rahmat M. Irfan Machmud Effendy Machmud Effendy Machmud Effendy Mardiyah, Nur Alif Mardiyah, Nur Alif Mardiyah, Nuralif Marhainis Othman, Siti Marzuki, Mohammad Mas Nurul Achmadiah Md Rozali, Sahazati Merinda Lestandy Merinda Lestandy Mohammad Marzuki Mohd Fua'ad Rahmat Muhamad Fadli Ghani Muhamad Fadli Ghani Muhammad Haziq Hakim Rosman Muhammad Ikhwanul Khair Muhammad Irfan Muhammad Nasar Muslim, Ahzen Habibidin Noor Cahyadi, Basri Norazizah Novendra Setyawan Nur Alif Mardiyah Nur Alif Mardiyah Nur Alif Mardiyah Nur Alif Mardiyah, Nur Alif Nuralif Mardiyah Nuralif Mardiyah Nuralif, Nuralif Nurhadi Nurhadi Nurhadi, Nurhadi Nurhadi, Nurhadi Nurkasan Nurkasan, Nurkasan Pratama, Rido Octa Putra, Annas Alif Putra, Een Hutama Retno, Bella Rido Octa Pratama Rozaimi Ghazali Rozaimi Ghazali Rozaimi Ghazali S. M. Rozali Sahazati Md Rozali Septiantono, Dezar Siti Marhainis Othman Siti Marhainis Othman Soon, Chong Chee Syafa'ah, Lailis Syafaah, Lailis Syafa’ah, Lailis Tahir, Abdul Wafi WIDIANTO Yahaya Md Sam Yahaya Md. Sam Zsa Zsa Septina Atsil