Optimasi Linear Sampling dan Information Gain pada Algoritma Decision Tree untuk Diagnosis Penyakit Diabetes

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Abdul Azis Abdillah

Abstract

Diabetes which is assigned to be in the top 10 list of diseases that cause death in the last 10 years has increased. What was observed was that this increase occurred in developing countries with middle to lower social status. In Indonesia, diabetes is included in the top 10 diseases with a large number of sufferers. And more than that, diabetes becomes a comorbid that causes complications in Covid 19 patients. Then to detect diabetes more quickly and accurately, it is necessary to make research that can produce a better level of accuracy in order to detect diabetes. By using public dataset taken from the UCI repository consisting of 520 records, obtained from Diabetes Sylhet Hospital, Bangladesh. In this research, classification will be carried out using the Decision Tree algorithm with optimization of Linear Sampling and Information Gain. After calculating using these methods and calculating the accuracy, the results obtained are 99.04% accuracy with a comparison with previous research which only used a Random Forest of 97.04%.

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How to Cite
Abdillah, A. A. (2021). Optimasi Linear Sampling dan Information Gain pada Algoritma Decision Tree untuk Diagnosis Penyakit Diabetes. MULTINETICS, 7(1), 21–29. Retrieved from https://jurnal.pnj.ac.id/index.php/multinetics/article/view/3681

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