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All Journal Jupiter Syntax Jurnal Informatika Jurnal Algoritma Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Abdimas Universal Yumary: Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Bina Bangsa Jurnal Algoritma Jurnal Sistem Informasi dan Teknologi (SINTEK) IJOEM: Indonesian Journal of Elearning and Multimedia Carmin: Journal of Community Service LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Jurnal Rekayasa Perangkat Lunak
Ratnasari Ratnasari
Departemen Rekayasa Perangkat Lunak, Fakultas Teknologi Dan Informatika Universitas Aisyah Pringsewu, Lampung - Indonesia
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Komparasi Penerapan Adaboost Pada K-NN Dan Decision Tree Untuk Prediksi Penyakit Hati Mukaromah, Hafsah; Ratnasari, Ratnasari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

The liver is a vital human organ that plays a crucial role in detoxification, cholesterol regulation, and various metabolic activities within the body. Impairment of liver function can lead to several diseases such as hepatitis, liver cancer, cirrhosis, and other liver-related conditions. In Indonesia, approximately 0.6% of the population is identified as having hepatitis, despite the implementation of the HB 0–4 immunization program by the Ministry of Health. Liver disease is a common public health issue, with WHO data reporting an annual death toll of 1.2 million people due to liver-related illnesses in Southeast Asia and Africa. The importance of early detection of liver disease symptoms highlights the need for a predictive system capable of accurately identifying individuals at risk. This study employs a machine learning approach using K-Nearest Neighbor (K-NN) and Decision Tree classification algorithms, enhanced by the application of the Adaboost ensemble learning technique to optimize their performance. Evaluation results show that Adaboost improves the accuracy of the K-NN algorithm to 95.77% and the accuracy of the Decision Tree to 100%. Although the improvement in K-NN is quite significant, Adaboost does not have a substantial impact on the accuracy of the Decision Tree. This research indicates that the Adaboost method is effective in enhancing the classification performance for liver disease, particularly when applied to the K-NN algorithm.
Co-Authors Agustinus Eko Setiawan Ahlun Nazar, Ahmad Ahmad Jurnaidi Wahidin Alfina Alfina Andika, Tahta Herdian Aras, Irsan Ardian, Odi Arifin, Yulyani Bintoro, Panji Dinda Rahmadanty Dwi Yana Ayu Andini Eko Setiawan, Agustinus Fahlul Rizki Fandi Ahmad Ferly Ardhy Fitra Endi Fernanda, Fitra Endi Fitri Hardiyanti, Fitri Gustianing, Defy Habib, Cahya Isti Fada, Nurul Muhammad, Adamu Abubakar Mukaromah, Hafsah Nur Aminudin Ockhy Jey Fhiter Wassalam Pasaribu, Alfredo Prayoga, Sahrul Rendy Yudha Pratama Salman Alfarisi Salimu Setiawan, Agustinus Eko Silvyana, Dety Sunarmi Syaputra, Andhika Tahta Herdian Andika Ubaidillah, Ilham Ulfa Isni Kurnia Wahyuni, Nuci Widiandana, Panggah Zulkifli