Comparative Analysis of Linear Discriminant Algorithms and Support Vector Machine in Palm Fruit Image Disease Classification


  • Finis Hermanto Laia Magister of Computer Science, Potensi Utama University
  • Hartono Magister of Computer Science, Potensi Utama University
  • Zakarias Situmorang Magister of Computer Science, Potensi Utama University



Image Classification, Linear Discriminant Analysis, Support Vector Machine, Cross Validation, Palm Fruit


Artificial Intelligence (AI) is a branch of computer
vision that is used for object detection and image classification
using algorithms. Approaches to comparing object characteristics
in image processing can be divided into High Dimensional Feature
and Low Dimensional Feature approaches. Support Vector
Machine (SVM) is an accurate High Dimensional Feature method,
while Linear Discriminant Analysis (LDA) is a powerful Low
Dimensional Feature method. Some studies combine SVM with
LDA to reduce complexity and improve performance. In the
classification of palm fruit image diseases, the comparison
between SVM (High Dimensional Feature) and LDA (Low
Dimensional Feature) can be done with variations in dataset size
and the percentage of training and testing data in the research is
50:50, 60:40 and 70:30. Performance measurement is based on
accuracy, precision recall and f1-score values. The algorithm for
testing the validity of the accuracy results is k=5 Cross Validation.
The average test result on linear dicriminant analysis, the highest
prediction, namely 0.57, was obtained in the 1st iteration.
Meanwhile, the lowest average value was obtained in the 5th
iteration, namely 0.44. Then the predicted value for system testing
is 0.52. Meanwhile, the calculation of the support vector machine
testing results has the highest average prediction, namely 62.63%,
obtained in the 2nd iteration. The lowest average accuracy value
was obtained in the 4th iteration, namely 62.36%. Then the
prediction value for the testing data system was 62.57, obtained
from the average results of each iteration. This study aims to
compare the performance of the LDA and SVM algorithms in
classifying healthy and diseased oil palm fruit diseases with
variations in dataset size and percentage of training and testing
data. From the results of research conducted, SVM has an
accuracy of 72.02% at the 3rd percentage variation. Meanwhile,
LDA is 69.04% with the same percentage variation. SVM shows
that it is more effective in classifying images of sick palm fruit or
healthy fruit compared to LDA.