Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization
Dublin Core
Title
Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization
Subject
histogram of orientation gradients; principal component analysis; k-nearest neighbor; binary particle swarm
optimization
optimization
Description
Various cultivars of date fruit distributed throughout exhibit diverse complexity and unique attributes, including color, flavor,
shape, and texture. These distinctive characteristics and appearance occasionally may lack variability in date fruits, as various
kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying
multiple types of date fruit, a classification model was developed to categorize date fruit based on their visual appearances
and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The
system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments,
HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train
a K-Nearest Neighbor (KNN) classifier. Considering the parameters applied in developing the proposed classification model
is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle
Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the
optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a
considerable improvement of 12% over barebone KNN.
shape, and texture. These distinctive characteristics and appearance occasionally may lack variability in date fruits, as various
kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying
multiple types of date fruit, a classification model was developed to categorize date fruit based on their visual appearances
and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The
system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments,
HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train
a K-Nearest Neighbor (KNN) classifier. Considering the parameters applied in developing the proposed classification model
is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle
Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the
optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a
considerable improvement of 12% over barebone KNN.
Creator
Wikky Fawwaz Al Maki, Khaidir Mauladan, Indra Bayu Muktyas
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
Englis
Type
Text
Files
Collection
Citation
Wikky Fawwaz Al Maki, Khaidir Mauladan, Indra Bayu Muktyas, “Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10126.