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All Journal Jurnal Rekayasa Informasi
Wida Aziz Rahmat
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Computer Science & IT

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PERBANDINGAN ALGORITMA DECISION TREE, RANDOM FOREST DAN NAIVE BAYES PADA PREDIKSI PENILAIAN KEPUASAN PENUMPANG MASKAPAI PESAWAT MENGGUNAKAN DATASET KAGGLE Wida Aziz Rahmat; Siti Madinah Ladjamuddin; Dipa Teruna Awaludin
JURNAL REKAYASA INFORMASI Vol 12 No 2 (2023): JURNAL REKAYASA INFORMASI Vol 12 No 2 Tahun 2023
Publisher : PROGRAM STUDI SISTEM INFORMASI INSTITUT SAINS DAN TEKNOLOGI NASIONAL (ISTN)

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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%.
Co-Authors Dipa Teruna Awaludin Siti Madinah Ladjamuddin