Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Asrul Syawal
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Sistem Pakar Deteksi Kenakalan Remaja di Sekolah Menggunakan Modified K-Nearest Neighbor (MKNN) Asrul Syawal; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.656 KB)

Abstract

Juvenile delinquency is one of the problems faced by parents in guiding children. Juvenile delinquency has often happened in the world of primary education in schools, this is a concern for parents, besides parents, teachers as mentors in schools actively participate in guiding students in schools to avoid juvenile delinquency and to behave positively and positively in the school environment and outside the school environment . To avoid delinquency in school prevention is done by detecting delinquency that might occur in children based on factors or behavioral symptoms that are often carried out by children. In this study a system was created to detect juvenile delinquency especially in schools to prevent delinquency that might occur using the Modified K-Nearest Neighbor (MKNN) method. MKNN method is a developmental method of the KNN method, which distinguishes the value of validity in training data to produce better values. The results of this test used as much as 60 training data, and the accuracy of the variation in training data obtained the lowest level of accuracy when the value of training data variation was 30% the accuracy was 76.78% and the highest level of accuracy when the training data variation value was 90% the accuracy was 100%. In this test the average system accuracy was obtained at a maximum of 86.7%.
Co-Authors Arief Andy Soebroto