Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes

Mahendra Dwifebri Purbolaksono; Muhammad  Irvan Tantowi; Adnan  Imam Hidayat; Adiwijaya Adiwijaya

doi:10.29207/resti.v5i2.3008

Mahendra Dwifebri Purbolaksono Telkom University
Muhammad Irvan Tantowi Universitas Telkom
Adnan Imam Hidayat Universitas Telkom
Adiwijaya Adiwijaya Universitas Telkom

DOI: https://doi.org/10.29207/resti.v5i2.3008

Keywords: Diabetes, Machine Learning, Supervised Learning, Support Vector Machine, Modified Balanced Random Forest

Abstract

Diabetes (diabetes) was a metabolic disorder caused by high levels of sugar in the blood caused by disorders of the pancreas and insulin. According to data from the Ministry of Health of the Republic of Indonesia, Diabetes was the third-largest cause of death in Indonesia with a percentage of 6.7%. The high rate of death from diabetes encouraged this study, with the aim of early detection. This research used a Machine Learning approach to classify the data. In this paper, a comparison of Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) was discussed for classifying diabetes patient data. Both methods were chosen because it was proven in previous studies to get high accuracy, so that the two methods are compared to find the best classification model. Several preprocessing methods were used to prepare the data for the classification process. The entire combination of preprocessing steps will be carried out on the two classification methods to produce the same dataset. The evaluation was carried out using the Confusion Matrix method. Based on the experimental results in the process of testing the system being built, the maximum performance results were 87.94% using SVM and 97.8% using MBRF.

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