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All Journal ILKOM Jurnal Ilmiah Bulletin of Culinary Art and Hospitality
Ming Foey Teng
American University of Sharjah
Agriculture, Biological Sciences & Forestry Computer Science & IT Health Professions

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Asking a Chatbot for COVID-19 Food and Nutrition Fahmi Fahmi; Yusrandi Yusrandi; Aji P. Wibawa; Ming Foey Teng; Purnawansyah Purnawansyah
Bulletin of Culinary Art and Hospitality Vol. 1 No. 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.425 KB) | DOI: 10.17977/um069v1i22021p63-69

Abstract

Coronavirus 19 (COVID-19) is a disease caused by a new coronavirus called severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2; previously known as 2019-nCoV). On March 11, 2020, WHO declared COVID-19 a global pandemic, the first pandemic since H1N1 influenza was declared a pandemic in 2009. The disease has hit almost every country in the world and so far, no medicine nor antiviral was found. In some countries where the death rate is high, the number of patients continues to increase. On May 20, 2020, the number of patients exceeded 4.8 million and the toll is 318,000 (6.6 percent). Although the number of new patients in many countries declined after the access rights lockdown (lockdown), the second attack returned to the area where the first attack occurred. The COVID-19 case in Indonesia still shows an increasing trend even though various efforts have been made by the state and society. This research is intended to discuss us regarding the use of chatbots to provide guidance on food and nutrition during the COVID-19 pandemic. The results showed that a review of maintaining a healthy lifestyle by adhering to health protocol during the pandemic of COVID-19, shows that the higher the rating, the more effective it is to reduce the transmission of the corona virus in the future.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah Purnawansyah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Haviluddin Haviluddin; Cholisah Erman Hasihi; Ming Foey Teng; Herdianti Darwis
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
Co-Authors Aji P. Wibawa Aji Prasetya Wibawa Cholisah Erman Hasihi Fahmi Fahmi Haviluddin Haviluddin Herdianti Darwis Purnawansyah Purnawansyah Triyanna Widyaningtyas Yusrandi Yusrandi