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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jiko (Jurnal Informatika dan komputer)
Yuita Arum Sari
Faculty of Computer Science, Brawijaya University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Convolutional Neural Network untuk Klasifikasi Citra Makanan Khas Indonesia Muhammad Dandi Darojat; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

There are various types of special food in Indonesia that are still difficult to identify domestically and internationally. If many people know the special Indonesian food, then Indonesia will be increasingly recognized as well. In line with this, state revenue may increase as well. But in some cases, special food in Indonesia is still challenging to identify, especially for foreign tourists. This paper proposes an image classification system for Indonesian special food images using the Convolutional Neural Network algorithm (CNN) which is supported by several other methods and algorithms. Based on the experiments conducted eight times on 26 models, the best model was obtained with a test accuracy value of 0.6 and an evaluation accuracy of 0.91. This shows that the CNN is relatively good to be applied to the classification of special Indonesian food images.
SPERM ABNORMALITY CLASSIFICATION USING MULTI-PURPOSE IMAGE EMBEDDING AND CLASSICAL MACHINE LEARNING Sigit Adinugroho; Yuita Arum Sari; Wijaya Kurniawan; Achmad Arwan
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 3 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8938

Abstract

Since sperm cells have big impact for human welfare in terms of reproduction, there are many studies have been done. In this case, we are attracted to enrich the method in determining the morphological properties of them using machine learning. Most study about it is done using 2-steps action that are feature extraction which is continued by classification. In our work, we aimed to lower the complexity by using image embedding as a general-purpose feature extractor that requires no training. For feature extraction using image, it is found that RGB has better performance compared to grayscale if we want to use Support Vector Machine (SVM). Meanwhile, when a comparation is done between SVM, random forest, Multi-Layer Perceptron (MLP), Naïve Bayes, and k-Nearest Neighbour (kNN) for classification process, MLP shows the best performance among them which is around 85%. Moreover, our proposed method has low complexity indicated by the training time around one and a quarter minute s for the most accurate method, compared to hours of training time in similar methods.
Co-Authors Achmad Arwan Muhammad Dandi Darojat Randy Cahya Wihandika Sigit Adinugroho Wijaya Kurniawan