PERBANDINGAN ALGORITMA DECISION TREE C4.5 DAN RANDOM FOREST PADA PREDIKSI RATING APLIKASI APPSTORE

RIDWAN, MUHAMMAD and Dewi, Novi Rustiana and Resti, Yulia (2023) PERBANDINGAN ALGORITMA DECISION TREE C4.5 DAN RANDOM FOREST PADA PREDIKSI RATING APLIKASI APPSTORE. Undergraduate thesis, Sriwijaya University.

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Abstract

Technology company Apple Inc develops and distributes an operating system called iOS. To find apps on iOS, users need to download apps from a service called the Appstore. Users can provide feedback to rate the application in the form of a rating. The better the rating, the more interested the user will be in the application and will download the application, so analysis is needed. The data used in this study is secondary data obtained from Kaggle.com. the data consists of 7197 observations and 16 variables. The results of this study indicate the accuracy value of the Decision Tree algorithm using quartile discretized data has an accuracy of 76.98%. precision of 74.48% and recall of 76.98%. The value of the accuracy of the Decision Tree algorithm on arrival discretized frequency distribution has an accuracy of 74.80%, a precision of 70.20% and a recall of 74.80%. While the accuracy value of the Random Forest algorithm on quartile discretized data has an accuracy of 77.93%, a precision of 76.02% and a recall of 77.93%. The accuracy value of the Random Forest algorithm on data discretized by the frequency distribution has an accuracy of 75.41%, a precision of 68.80% and a recall of 75.41%. In predicting the Appstore rating, the Random Forest algorithm is better than the Decision Tree algorithm.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Appstore, Decision Tree, Random Forest
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.M67 Experimental design. Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Muhammad Ridwan
Date Deposited:	11 Apr 2023 04:57
Last Modified:	11 Apr 2023 04:57
URI:	http://repository.unsri.ac.id/id/eprint/94588

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