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All Journal JOIV : International Journal on Informatics Visualization
Yufis Azhar
Universitas Muhammadiyah Malang, Malang, Indonesia
Computer Science & IT

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Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor Agus Eko Minarno; Muhammad Yusril Hasanuddin; Yufis Azhar
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1299

Abstract

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
Classification of Malaria Cell Image using Inception-V3 Architecture Agus Eko Minarno; Laofin Aripa; Yufis Azhar; Yuda Munarko
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1301

Abstract

Malaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively.
Co-Authors Agus Eko Minarno Laofin Aripa Muhammad Yusril Hasanuddin Yuda Munarko