Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Journal of Computer Engineering, Electronics and Information Technology
Basyir Al Musthoqfirin Majid
Universitas Khairun Ternate
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Classification of Device Addiction to Students Using SAS-SV with K-Nearest Neighbor Algorithm Method Basyir Al Musthoqfirin Majid; Abdul Mubarak; Salkin Lutfi
Journal of Computer Engineering, Electronics and Information Technology Vol 1, No 1 (2022): COELITE: Volume 1, Issue 1, 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.564 KB) | DOI: 10.17509/coelite.v1i1.51616

Abstract

A gadget is a small electronic device with a particular purpose, often thought of as an innovation of new goods. Not only to help facilitate human activities, but gadgets are also a part of the lifestyle for modern citizens. With this innovative feature, the gadget has attracted users more and more, or in other words, users have become more addicted to the gadget. This study aims to investigate how addictive gadgets are to students at the Department of Informatic Engineering, Khairun University, Ternate, Indonesia using K-Nearest Neighbor (KNN) Algorithm. In KNN, there is a Training dataset where one set of data contains the class's value and a predictor that will be used as one of the requirements for determining a suitable grade per the predictor. In contrast, the Testing dataset contains the new data that will be classified based on the model made and the accuracy of classification in the data collection process. Questionnaires were made using Google forms, then distributed through the internal groups of the Informatics Engineering department of  Khairun University. A total of 78 questionnaires were successfully collected. The results showed that the testing accuracy with k = 3 is 86% and k = 5 is 80%. This show that KNN algorithm can be applied to measure the level of addiction to students.
Co-Authors Abdul Mubarak Salkin Lutfi