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All Journal Building of Informatics, Technology and Science Jurnal Bumigora Information Technology (BITe)
Dion Pratama Putra, Dion Pratama
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media Putra, Dion Pratama; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3084

Abstract

There are negative effects of the ease of obtaining information in today's society, one of which is the rise of hoaxes on the internet. As much as 92.40% of social media platforms such as Twitter are used to spread hoaxes. Launched on July 13, 2006, Twitter is a microblogging service where users can spread information at no cost to themselves or others. In this study, the authors will conduct hoax news detection on Twitter social media using the Long Short - Term Memory (LSTM) method and Gate Recurent Unit (GRU) and gloVe feature expansion. with a dataset of 25,234 data which produces accuracy results in TF-IDF on each model, namely 97.33% in LSTM and 96.75% in GRU, and an increase in accuracy of 0.22% in the tweet corpus on LSTM and an increase in accuracy of 0.15 in the BeritaTweet corpus on GRU.
Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method Putra, Dion Pratama; Wahyu Wiriasto, Giri; Paniran, Paniran
Jurnal Bumigora Information Technology (BITe) Vol 6 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4401

Abstract

Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows. Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application. Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification. Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures. Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.
Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method Putra, Dion Pratama; Wahyu Wiriasto, Giri; Paniran, Paniran
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4401

Abstract

Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows. Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application. Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification. Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures. Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.
Co-Authors Defiariany Defiariany Erwin Budi Setiawan Giri Wahyu Wiriasto Ilham Eka Putra Paniran Paniran