Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Riska Agustia
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 
Aplikasi Data Mining menggunakan Algoritme Naive Bayes untuk Memprediksi Ketepatan Waktu Lulus Mahasiswa Riska Agustia; Ahmad Afif Supianto; Niken Hendrakusma Wardani
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 (507.107 KB)

Abstract

The graduation rate for each student is different, timely and not on time. What can be an obstacle is if there are many students who graduate not on time. Based on data in 2018 on the official website of the Faculty of Computer Science, Universitas Brawijaya, the average student admission of Information Systems every year is approximately 227 students, while for the average student graduating annually around 134 students. So based on these data, an application is needed that is able to help decision makers to predict earlier students who have the potential to pass on time so that further action can be given. The process of predicting the timeliness of graduating students will be done using the Naive Bayes algorithm. The system implementation will utilize the Laravel framework and Weka simple CLI. The output generated from the system is in the form of a dashboard visualization with a chart containing graduation information, a form to create a model, information from the model that has been made, and a form that can be used by the Head of the SI Department to predict the timeliness of graduating students. The results of the evaluation and validation of the Naive Bayes algorithm resulted in an accuracy value of 88.6076% and an AUC of 0.9558. The results of testing the system using black-box testing shows that the system is valid according to defined requirements. While for usability testing with the System Usability Scale produces a value of 67.5 which is classified as an Adjective rating Good.
Co-Authors Ahmad Afif Supianto Niken Hendrakusma Wardani