Comparison of RAM Classification with Decision Tree Algorithms and KNN


Komparasi Klasifikasi RAM dengan Algoritma Decision Tree dan KNN


  • (1)  Solehudin Al Ayyubi            Universitas Pembangunan Nasional “Veteran” Jawa Timur  
            Indonesia

  • (2)  Indira Setia Amalia            Universitas Pembangunan Nasional “Veteran” Jawa Timur  
            Indonesia

  • (3) * Amalia Anjani Arifiyanti             Universitas Pembangunan Nasional “Veteran” Jawa Timur  
            Indonesia

    (*) Corresponding Author

Abstract

A laptop is a rather small personal computer consisting of a keyboard, screen, microprocessor and usually a rechargeable battery. In the current era of technology, when buying a laptop, we need to have thorough and detailed considerations regarding our needs in using a laptop, especially laptop RAM. By comparing Decision Tree and KNearest Neighbors algorithm, a more accurate algorithm was found to make a prediction in buying a laptop with suitable variable consideration. The result shows that Decision Tree algorithm is more accurate to be used in predicting the suitable laptop RAM. Decision Tree accuracy is 68%, this result is higher than KNN accuracy which is only 66%.

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Author Biographies

Solehudin Al Ayyubi, Universitas Pembangunan Nasional “Veteran” Jawa Timur

Program Studi Sistem Informasi, Fakultas Ilmu Komputer

Indira Setia Amalia, Universitas Pembangunan Nasional “Veteran” Jawa Timur

Program Studi Sistem Informasi, Fakultas Ilmu Komputer

Amalia Anjani Arifiyanti , Universitas Pembangunan Nasional “Veteran” Jawa Timur

Program Studi Sistem Informasi, Fakultas Ilmu Komputer

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Published
2022-06-30
 
How to Cite
[1]
S. A. Ayyubi, I. S. Amalia, and A. A. Arifiyanti, “Comparison of RAM Classification with Decision Tree Algorithms and KNN”, PELS, vol. 2, no. 2, Jun. 2022.