PERBANDINGAN ALGORITMA DECISION TREE, RANDOM FOREST DAN NAIVE BAYES PADA PREDIKSI PENILAIAN KEPUASAN PENUMPANG MASKAPAI PESAWAT MENGGUNAKAN DATASET KAGGLE

Abstract

Machine learning focuses on building systems to learn and improve performance based on the data they have. Each machine learning algorithm has a different performance. In this study, the focus is on measuring the performance of three classification machine learning algorithms, namely the decision tree algorithm, the random forest algorithm and the naive Bayes algorithm. Using airplane passenger satisfaction data from the kaggle site, in this study a classification will be carried out to predict passenger satisfaction ratings. The confusion matrix method is used to measure accuracy performance. The measurement results in a random forest algorithm having the highest accuracy of 95%, a decision tree algorithm of 93% and naive bayes algorithm has the lowest accuracy of 82%.