Combination of TOPSIS Method with Attribute Weighting of Information Gain in Decision-Making - Ahmad Rozy; Erna Budhiarti Nababan; Syahril Efendi (InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, 2021)
In this research, a combination of the Technique for Order Preference by Similarity lto Ideal Solution (TOPSIS) lalgorithm was carried out with the attribute weighting of the Information Gain method to obtain better decision support results. The data processed in this study is the Indian Liver Patient Dataset (ILPD) dataset obtained lfrom UCI Machine Learning Repository which has 583 instances, 11 attributes and 1 class label. The class label is a text type that consists of two values, namely a liver patient and a non-liver patient. The experimental results show that TOPSIS’ running time and information gain combination algorithm is 1.13 seconds. The result of the accuracy value obtained with a final threshold value greater than 0.5 is 91.25%.In this research, a combination of lthe lTechnique lfor lOrder lPreference lby Similarity lto lIdeal lSolution (TOPSIS) lalgorithm was carried out with the attribute weighting of the Information Gain method to obtain better decision support results. The data processed in this study is the Indian lLiver lPatient lDataset (ILPD) dataset obtained lfrom UCI lMachine Learning lRepository which has 583 instances, 11 attributes and 1 class label. The class label is a text type that consists of two values, namely a liver patient and a non-liver patient. The experimental results showthat TOPSIS’ running time and information gain combination algorithm is 1.13 seconds. The result of the accuracy value obtained with a final threshold value greater than 0.5 is 91.25%