Authors :
Abi Mestu Yudansha; M Arief Soeleman; Affandy
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/ydrfnjcz
Scribd :
https://tinyurl.com/5cm7y62c
DOI :
https://doi.org/10.5281/zenodo.10784755
Abstract :
Higher education institutions such as
universities play a central role in the steps where
comprehensive research and development activities take
place within a highly competitive environment. The
academic achievements of students become a crucial
element within the structure of these higher education
institutions. This is because one of the key indicators of
university quality is an exceptional track record of
academic achievements. Universitas Dian Nuswantoro
(UDINUS), a private educational institution with an A
accreditation rating, is located in Semarang, Indonesia.
One of the faculties that holds a significant role at
UDINUS is the Faculty of Computer Science, which
stands out with the highest number of students,
particularly in the Bachelor's program of Computer
Science (S1), which records a comprehensive and
outstanding student count compared to other study
programs. Therefore, it is appropriate to focus research
on the data regarding the graduation rates of students
from the Computer Science S1 program. In this study,
the author applies Data Mining, a method involving
manipulation of large-scale data. The primary mission
of this research is to address the question of how the
implementation of Deep Learning using an optimized
Convolutional Neural Network (CNN) through Genetic
Algorithm can be utilized to predict student graduation.
Consequently, the outcomes can serve as references to
expedite student graduation. The study demonstrates
that the feature extraction values using CNN and the
hyperparameters using Genetic Algorithm show an
overall increase in accuracy when using K-Nearest
Neighbor (K-NN) for all values of n: 3, 4, 5, and 6
(Proven that feature extraction from tabular data
represented as images and processed with the CNN
algorithm using the most suitable parameters is
successful)
Keywords :
Component Udinus, Convolutional Neural Network (CNN), Genetic Algorithm, K- Nearest Neighbor (KNN).
Higher education institutions such as
universities play a central role in the steps where
comprehensive research and development activities take
place within a highly competitive environment. The
academic achievements of students become a crucial
element within the structure of these higher education
institutions. This is because one of the key indicators of
university quality is an exceptional track record of
academic achievements. Universitas Dian Nuswantoro
(UDINUS), a private educational institution with an A
accreditation rating, is located in Semarang, Indonesia.
One of the faculties that holds a significant role at
UDINUS is the Faculty of Computer Science, which
stands out with the highest number of students,
particularly in the Bachelor's program of Computer
Science (S1), which records a comprehensive and
outstanding student count compared to other study
programs. Therefore, it is appropriate to focus research
on the data regarding the graduation rates of students
from the Computer Science S1 program. In this study,
the author applies Data Mining, a method involving
manipulation of large-scale data. The primary mission
of this research is to address the question of how the
implementation of Deep Learning using an optimized
Convolutional Neural Network (CNN) through Genetic
Algorithm can be utilized to predict student graduation.
Consequently, the outcomes can serve as references to
expedite student graduation. The study demonstrates
that the feature extraction values using CNN and the
hyperparameters using Genetic Algorithm show an
overall increase in accuracy when using K-Nearest
Neighbor (K-NN) for all values of n: 3, 4, 5, and 6
(Proven that feature extraction from tabular data
represented as images and processed with the CNN
algorithm using the most suitable parameters is
successful)
Keywords :
Component Udinus, Convolutional Neural Network (CNN), Genetic Algorithm, K- Nearest Neighbor (KNN).
