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Journal : Journal of Computer System and Informatics (JoSYC)

Analisa Sentimen Informasi Hoaks Pasca Pandemi Covid-19 dengan Text Mining Akhmad Rezki Purnajaya; Yonky Pernando
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3358

Abstract

In this era of globalization, the delivery of information can be conveyed by any information media, this can also cause the spread of hoax information. This is very detrimental for recipients of information, especially information that is very important to receive and information related to the Covid or Corona virus that has hit the world and Indonesia. Based on data from the Gugus Tugas Percepatan Penanganan Covid-19, in the post-pandemic era 227 hoax information were still found that were identified. Therefore, in this study the authors conducted hoax information research, especially regarding the Covid-19 virus in the post-pandemic era by using the Text Mining method to find out information patterns that often appear in the post-Covid-19 pandemic. This paper will also show some results regarding the association of several words with the main word in hoax information in the post-Covid-19 pandemic era in the form of Term of Frequency, Word Cloud, Fruchterman Reingold Layout, and Circle Layout. The results of this study indicate that in the post-Covid-19 pandemic era, hoax information is frequently associated with ‘vaccines’ and ‘covid’, with 113 and 111 occurrences respectively. This is due to public skepticism regarding the safety and effectiveness of the Covid-19 vaccine, leading to the dissemination of hoaxes aimed at discouraging vaccination. Other commonly mentioned words in post-pandemic hoax information include ‘omicron’, ‘vaccinated’, ‘variant’, ‘pfizer’, ‘mRNA’, ‘virus’, and ‘vaccination’, albeit with lower frequencies
Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels Simanjuntak, Nurchaya; Saragih, Raymond Erz; Pernando, Yonky
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5123

Abstract

In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the potential of employing YOLOv8, a cutting-edge deep learning model, to automate Ambarella fruit ripeness classification. This work focuses on the YOLOv8n, YOLOv8s, and YOLOv8m, lightweight models within the YOLOv8 family. Our results are promising: all three models achieved 100% accuracy on the training set, with YOLOv8s demonstrating the lowest loss at 0.00286. The web application was utilised to deploy the trained models, allowing users to upload images of Ambarella fruit and run the model for inference.