SYAHIDA NURHIDAYARNIS, -
(2024)
KLASIFIKASI BERBASIS NEURAL NETWORK UNTUK
PENENTUAN PRIORITAS ATRIBUT STRES MAHASISWA
MENGGUNAKAN ANALYTIC HIERARCHY PROCESS.
KLASIFIKASI BERBASIS NEURAL NETWORK UNTUK PENENTUAN PRIORITAS ATRIBUT STRES MAHASISWA MENGGUNAKAN ANALYTIC HIERARCHY PROCESS.
Abstract
Abstract— Universities in Indonesia require final-year
students to complete a thesis as a graduation requirement.
However, many students face challenges in choosing a research
topic, which can trigger stress and hinder timely graduation.
The aim of this study is to identify the impact of stress caused
by procrastination on students' timely graduation. In this
research, the Analytic Hierarchy Process (AHP) method was
used to determine the most influential procrastination
attributes, while the Backpropagation Neural Network (BPNN)
was employed to predict timely graduation. The results
indicate that not working on the thesis is the most significant
factor affecting student stress. Stress due to procrastination
has a significant impact on graduation predictions, with an
RMSE value of 0.1428 and an accuracy rate of 97.9%. These
findings can assist educational institutions in designing more
effective interventions to reduce stress caused by
procrastination, thereby increasing the likelihood of timely
graduation for students.
Actions (login required)
 |
View Item |
