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All Journal Jupiter Aisyah Journal of Informatics and Electrical Engineering Jurnal Teknik Informatika (JUTIF) Carmin: Journal of Community Service Jurnal Rekayasa Perangkat Lunak
Mukaromah, Hafsah
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Komparasi Teknik Bagging Dan Adaboost Pada Decision Tree Dan Naive Bayes Untuk Prediksi Stroke Mukaromah, Hafsah; Wasilah
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 1 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10790777

Abstract

Stroke, also known as cerebrovascular accident (CVA), is a condition where there is a sudden disruption in brain function due to circulation problems, which can result in paralysis or even death of brain cells. There are two main types of stroke: ischemic, caused by blockage of blood vessels, and hemorrhagic, caused by bleeding into the brain. In Indonesia, stroke is the leading cause of death with an increasing incidence rate. Therefore, early prevention and treatment efforts are crucial in managing this condition. Data mining and machine learning have become important tools in predicting the risk of stroke. In this study, ensemble techniques, particularly bagging and adaboost, were applied to decision tree and naive bayes algorithms to improve accuracy in predicting stroke. The results showed that the use of ensemble techniques, especially adaboost, significantly improved the performance of the naive bayes algorithm, with an increase in accuracy of up to 7.42%. The combination of decision tree algorithm with bagging achieved the highest accuracy in predicting stroke, reaching 96.91%, followed by the combination of decision tree with adaboost and naive bayes with adaboost. These results indicate that ensemble techniques can significantly improve the performance of stroke prediction algorithms, with an emphasis on using adaboost for naive bayes algorithm and bagging for decision tree.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Anas Ikhsanudin Dita Septasari Dwi Feriyanto Febri Arianto Ikna Awaliyani Kiki Rizki Amelia Ningsiah Nur Aminudin Nurul Hidayat Prayoga, Sahrul Ratnasari Ratnasari Sri Lestari Wasilah