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Akbar, Fawwaz Ali
Universitas Pembangunan Nasional Veteran Jawa Timur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation

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Journal : JEECS (Journal of Electrical Engineering and Computer Sciences)

 1 
Implementation of ADABOOST Algorithm on C50 for Improving the Performance of Liver Disease Classification Anwar Sanusi; Chrystia Aji Putra; Fawwaz Ali Akbar
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 2 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i2.1

Abstract

The liver is a vital organ that is important for humans because it plays a role in regulating hormone cycles, neutralizing toxins, and controlling the composition of blood. Liver disease is a common ailment worldwide. Often, this disease occurs without specific symptoms (asymptomatic). Therefore, liver disease is known as a "silent killer," and it is necessary to quickly and accurately diagnose and treat liver diseases. Data mining technology can be useful for rapidly detecting liver diseases from laboratory diagnosis results. One classification algorithm that can be used is the C50 algorithm. This algorithm is an improvement over the previous C45 algorithm, with several advantages such as efficient memory usage and more concise tree results. However, the C50 algorithm may experience overfitting on complex medical data, requiring the boosting process using AdaBoost. The AdaBoost algorithm can make the C50 algorithm more susceptible to overfitting. Another advantage of the AdaBoost algorithm is its ability to handle imbalanced datasets in terms of target labels. This research used 583 data from UCI machine learning with two target labels: liver and normal. The research results show that the C50 algorithm is capable of identification with an accuracy rate of 74.58%. Furthermore, the C50 algorithm's accuracy can be maximized by AdaBoost to reach 86.44%
Co-Authors -, Moslim Wahyu S B Ade Fathoni Prastya Akbar, Muhammad Fadel Akbar, Rizky Januar Aldito Restu Wintama Ana, Vika Rafi Anggraini Puspita Sari annisaa sri indrawanti annisaa sri indrawanti Anwar Sanusi Aprilyan, Verdiansyah Ayus Ardisty Palvelus Jumala Arhinza, Rayhan Saneval Arifa, Salsabila Awandi, Nadhif Mahardika Azzahra, Rahma Adisty Muffid Bhakti, Mulyani Satya Bimantara, Candra Kusuma Muhammad Budi Nugroho Budi Nugroho Burhanuddin Muhammad Zhirof Cahyas, Jerry Ramadhani Chrystia Aji Putra Dewanto, Fuad Kurnia Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fadhilasari, Annisa Fahturohman, Ridho Fajar Faisal Muttaqin Faisal Muttaqin Farhana, Hafi Ihza Fauzan, Daffa Athallah Felliani Kurniawati Fetty Tri Anggraeny Firdausi, Putri Aulia Citra Firmantara, Wahyu Firza Prima Aditiawan Fisena, Muhammad Reyhan Dwi Gabriel, Paskalis Reynaldy Elroy Ghifari, Farhat Ibad Al Ginting, Fitznigel Diamond Daniel Habibi, Nabil Henni Endah Wahanani Hutagaol, LeonHoss I Gede Susrama Mas Diyasa Ibad, Farhat Ida Ayu Putu Sri Widnyani Iriansah, Ogy Rachmad Kasyfillah, Muhammad Rohan Kasyfillah, Rohan Kusuma, Steven Nathan Lina Nurlaili, Afina Lugito, Lugito Michael Imanuel Prasetya M. Miftachul Anwar Made Hanindia Prami Swari Mahnedra, Zenryo Yudi Arnava Darva Mandyartha, Eka Prakarsa Martoni Martoni Maulana, Dimas Octa Maulana, Hendra Mayya, Kalfin Syah Kilau Mohamad Ilham Prasetyo Raharjo Muhammad Agung Shobirin Muhammad Reyhan Dwi Fisena Muttaqin, Faisal Nadia, Prasinta Hari Natalia Matondang Noor Imansyah Basoeki, Dandy Nugroho, Iliochiesa Hilmi Nur Fadillah Dwi Rahma Nurlaili, Afina Lina Paskalis Reynaldy Elroy Gabriel Prastya, Ade Fathoni Putra, Chrystia Aji Putri, Angie Nurshabrina Rabbani, Rafi Rahma, Nur Fadillah Dwi Ramadhana, Fitranda Ramadhani, Neo Retno Mumpuni Reza Aminullah Rifan, Triyono Rizky Parlika, Rizky Romadhoni, Firman Ronggo Alit Salsabila, Belia Putri Sandy Rizkyando Saputra, Bayu Rachmawan Saputra, Bayu Rahmawan Saputra, Ersa Valerian Siran, Timothy Ueldy Siti Rochimah Sri Indrawanti, Annisaa SUBAGIA, RONNY Subagio, Moh. Mario Sugiarto Sujatmoko, Amanda Widya Indah Susrama Mas Diyasa, I Gede Syahbana, Ahmad Nadhif Fikri Wardana, Anak Agung Ngurah Wisnu Wintama, Aldito Restu Yisti Vita Via Yudi, Zenryo Ziidan, Muhammad Fattah