Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Moneter : Jurnal Keuangan dan Perbankan
Sulistyowati, Tinuk
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance

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

Found 1 Documents
Search

 1 
VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases Sulistyowati, Tinuk; PURWANTO, Purwanto; Alzami, Farrikh; Pramunendar, Ricardus Anggi
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (692.215 KB) | DOI: 10.32832/moneter.v11i1.57

Abstract

Data in the real world, there are many conditions (situations) where the number of instances in one class is much less than the number of instances in other classes. This situation is a problem in unbalanced datasets (imbalance class). As a result, performance in classification will decrease in some data systems. In this study, it was identified that the apple leaf disease performance dataset used had a large enough data imbalance problem where the comparison between instances was 1:5, so an oversampling method was needed to solve the data imbalance problem. Methods that can be used include the Synthetic Minority Over Sampling Technique (SMOTE). In order to validate the effectiveness of the proposed model, two experimental scenarios were carried out: first, the VGG16 algorithm was directly applied to modeling without considering class imbalance by reducing the number of layers and kernels in each layer to achieve optimal results, second, over-sampling SMOTE to increase the number of balanced datasets. The results showed that using the confusion matrix the accuracy results for each method were obtained where VGG 16 scored 85.16%, VGG 16 with SMOTE scored 92.94%. The conclusion of this study is that SMOTE helps improve the accuracy of leaf disease detection in apples.
Co-Authors Alzami, Farrikh Purwanto Purwanto Ricardus Anggi Pramunendar