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All Journal CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization ComTech: Computer, Mathematics and Engineering Applications
Johan Muliadi Kerta, Johan Muliadi
Information System Department, School of Information Systems, Bina Nusantara University Jln. K. H. Syahdan No. 9, Jakarta 11480, Indonesia
Computer Science & IT Engineering Mathematics

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Journal : JOIV : International Journal on Informatics Visualization

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Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods Rangkuti, Abdul Haris; Kerta, Johan Muliadi; Mogot, Roderik Yohanes; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2545

Abstract

Traditional food is essential in preserving cultural heritage and is a vital part of Indonesian cuisine. In this research, we implement a methodology to identify the traditional Indonesian food using machine learning algorithms supported by various segmentation methods. This research aims to provide an efficient and accurate approach to classifying traditional foods, which can contribute to promoting and preserving Indonesia's culinary heritage. To conduct this research, we conducted experiments on 34 types of conventional Indonesian food originating from various provinces in Indonesia. The analysis of food images involved several segmentation algorithms, including Sobel, Prewitt, Robert, Scharr, and Canny filters. After the segmentation process, we proceeded with feature extraction and classification using traditional machine learning algorithms such as the Random Forest algorithm, Decision Tree, and derivatives of the SVM algorithm. These algorithms aimed to recognize the 34 types of traditional food. After conducting several experiments, we found that Random Forest with Robert's segmentation method was the highest-performance algorithm. It produced extraordinarily accurate results on the test dataset, with an accuracy performance of 85.52%, recall of 84.63%, precision of 83.77%, and an f1 score of 82.49%. Additionally, the best-performing algorithms with execution time averaged less than 1 minute. Another experimental result showed that the Random Forest algorithm with the Canny operator achieved an accuracy of 81.51%, recall of 84.97%, precision of 86.8%, and an f1 score of 85.61% on the test dataset. Furthermore, the Random Forest algorithm with the Sobel operator achieved accuracy results of 78.4%, recall of 65.3%, precision of 62.3%, and an f1 score of 63.71%.  In the SVM algorithms derivative, the Sigmoid SVM combined with the Scharr operator achieved the highest performance in its category across all classification metrics. In conclusion, this research offers valuable insights into classifying traditional Indonesian dishes using traditional machine learning algorithms. Simultaneously, this research aims to promote the appropriate and effective preservation and recognition of traditional Indonesian food.
Improving Accuracy in Deep Learning-Based Mushroom Image Classification through Optimal Use of Classification Techniques Kerta, Johan Muliadi; Rangkuti, Abdul Haris; Lun Lau, Sian; Kurniawan, Albert; Gabriela, Melanie; Tandianto, Alicia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2820

Abstract

The primary purpose of this research is to address the existing knowledge gap surrounding various lesser-known types of edible mushrooms. A common understanding exists that mushrooms are edible and possess numerous health benefits. This research is intended to advance that understanding by deploying AI technology and deep learning models specifically designed to recognize and identify various fungi. During this research, we have developed a unique derivative of deep learning. This involved testing several Convolutional Neural Network (CNN) models aimed at automatically identifying and detecting different types of mushrooms and understanding the benefits associated with each type. The research methodology was divided into several stages: Collection of mushroom images, Preprocessing of images, Feature extraction, and Classification. The preprocessing involved adjustments such as scale, image rotation, and setting the brightness range. The goal of selecting and training the CNN model was to enhance the classification accuracy of mushroom images within each class. The data was divided into training, testing, and validation sets for the experimental stage. The purpose was to process image data from test and validation images based on the training images that have been processed. We evaluated the classification layer to be shorter, but it demonstrated excellent accuracy in assessing similarity performance. Based on several experiments conducted using different CNN models, DenseNet, MobileNetV2, and InceptionResNetV2 models achieved an accuracy of more than 90%, specifically 95%, 94%, and 92%, respectively. The most accurately recognized mushroom types include Snow, Dried Shitake, King Oyster, Straw, Button, and Truffle; some CNN models could identify these up to 100%. Overall, the models and algorithms used in this research successfully facilitated the identification and detection of various types of fungi. They were fast and displayed high accuracy performance. Hopefully, this research can be extended to process images of even more diverse types of mushrooms, particularly in terms of shape, color, and texture characteristics. This will enhance the depth and breadth of knowledge in this field and further advance our understanding of the beneficial properties of various mushrooms.
Co-Authors Adiprabowo, Panji Albert Kurniawan, Albert Andreas Kurniawan, Andreas Andrian, Ronny Angellia Debora Suryawan, Angellia Debora Athala, Varyl Hasbi Erny Erny, Erny Gabriela, Melanie Gunawan, Tonny Jufri, Wike Apryanto Kusmiyati, Eva Lun Lau, Sian Mogot, Roderik Yohanes Nugroho, Alexander Aryo Rahardjo, Sylvia Astri Wulandari Rangkuti, Abdul Haris Tandianto, Alicia Wennoris, David