Applying Features Based on Word Embedding Techniques to 1D CNN for Natural Disaster Messages Classification

Faisal, Mohammad Reza; Budiman, Irwan; Abadi, Friska; Nugrahadi, Dodon Turianto; Haekal, Muhammad; Sutedja, Indrajani

doi:10.1109/ic2ie56416.2022.9970188

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The initial phase entails gathering datasets, as expounded upon in the preceding section. Subsequently, the text is subjected to preprocessing in order to achieve normalization through the utilization of tokenization, stemming, and stopword elimination techniques [13], [14], [15]. Tokenization is a procedure executed to fragment sentences into segments of lexemes, punctuation, and other significant articulations in agreement with the stipulations of the utilized linguistic system.…”

Section: Research Implementationmentioning

confidence: 99%

Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media

Budiman,

Faisal,

Faridhah

et al. 2024

J. Comput. Sci. Inst.

Self Cite

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Messages shared on social media platforms like X are automatically categorized into two groups: those who self-report COVID-19 status and those who do not. However, it is essential to note that these messages cannot be a reliable monitoring tool for tracking the spread of the COVID-19 pandemic. The classification of social media messages can be achieved through the application of classification algorithms. Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. However, CNN has limitations in understanding global context, while LSTM focuses more on understanding word-by-word sequences. Apart from that, both require a lot of data to learn. Currently, an algorithm is being developed for text classification that can cover the shortcomings of the previous algorithm, namely Bidirectional Encoder Representations from Transformers (BERT). Currently, there are many variants of BERT development. The primary objective of this study was to compare the effectiveness of two classification models, namely BERT and IndoBERT, in identifying self-report messages of COVID-19 status. Both BERT and IndoBERT models were evaluated using raw and preprocessed text data from X. The study's findings revealed that the IndoBERT model exhibited superior performance, achieving an accuracy rate of 94%, whereas the BERT model achieved a performance rate of 82%.

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Section: Research Implementationmentioning

confidence: 99%

Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media

Budiman,

Faisal,

Faridhah

et al. 2024

J. Comput. Sci. Inst.

Self Cite

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“…Tey have demonstrated an outstanding performance in many research felds such as image processing [4], natural language processing [5], speech recognition [6], computer vision [7], human activity recognition [8][9][10][11][12], and cyber security felds [13,14]. In recent years, 1D convolutional neural networks (1D CNNs) in particular have shown an impressive performance for text classifcation [15,16]. A considerable amount of research has also been conducted for web security based on deep learning-based approaches [17,18].…”

Section: Limits Of Prior Atrsmentioning

confidence: 99%

MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

Moarref,

Sandıkkaya

2023

Security and Communication Networks

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The explosive growth of web-based technology has led to an increase in sophisticated and complex attacks that target web applications. To protect against this growing threat, a reliable web attack detection methodology is essential. This research aims to provide a method that can detect web attacks accurately. A character-level multichannel multilayer dilated convolutional neural network (MC-MLDCNN) is proposed to identify web attacks accurately. The model receives the full text of HTTP requests as inputs. Character-level embedding is applied to embed HTTP requests to the model. Therefore, feature extraction is carried out automatically by the model, and no additional effort is required. This approach significantly simplifies the preprocessing phase. The methodology consists of multichannel dilated convolutional neural network blocks with various kernel sizes. Each channel involves several layers with exponentially increasing dilation sizes. Through the integration of multichannel and multilayer dilated convolutional neural networks, the model can efficiently capture the temporal relation and dependence of character granularity of HTTP requests at different scales and levels. As a result, the structure enables the model to easily capture dependencies over extended and long sequences of HTTP requests and consequently identify attacks accurately. The outcomes of the experiments carried out on the CSIC 2010 dataset show that the proposed model outperforms several state-of-the-art deep learning-based models in the literature and some traditional deep learning models by identifying web attacks with a precision score of 99.65%, a recall score of 98.80%, an F1 score of 99.22%, and an accuracy score of 99.36%. A useful web attack detection system must be able to balance accurate attack identification with minimizing false positives (identifying normal requests as attacks). The success of the model in recognizing normal requests is further evaluated to guarantee increased security without sacrificing web applications’ usability and availability.

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“…Pola konsumsi remaja yang tidak sehat dapat meningkatkan risiko terjadinya diabetes dikemudian hari (Widyastuti et al, 2023). Penelitian lain menyebutkan bahwa terdapat sebanyak 40 remaja yang menderita hipertensi (Faisal et al, 2022). Kejadian hipertensi pada remaja tersebut dapat diakibatkan karena ketidakseimbangan asupan gizi dan gaya hidup tidak sehat, seperti konsumsi makanan berisiko tinggi, rendahnya aktivitas fisik, dan kebiasaan merokok (Kusparlina, 2022;Faisal et al, 2022).…”

Section: A Pendahuluanunclassified

Edukasi Gizi Seimbang sebagai Upaya Pencegahan ?Penyakit Degeneratif pada Remaja SMP

Solikhah,

Tumewu,

Weiha

et al. 2023

JPKM

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Penyakit degeneratif tidak hanya terjadi pada lansia dan dewasa saja, melainkan juga pada remaja jika pola makannya tidak sesuai dengan pedoman gizi seimbang. Pedoman gizi seimbang merupakan kumpulan makanan dan minuman dengan kualitas dan kuantitas yang sesuai dengan kebutuhan harian tubuh dengan tetap memperhatikan prinsip keberagaman makanan, aktivitas fisik, perilaku hidup bersih, dan pemantauan berat badan secara teratur. Pengabdian masyarakat ini bertujuan untuk meningkatkan pengetahuan siswa/siswi tentang gizi seimbang. Metode yang digunakan adalah ceramah, Focus Groub Discussion (FGD), dan tanya jawab. Instrumen yang digunakan adalah kuesioner pengetahuan yang diberikan sebelum dan sesudah edukasi. Hasil yang diperoleh adalah adanya peningkatan pengetahuan siswa/siswi terkait gizi seimbang dari nilai rata-rata 59% menjadi 85%. Kesimpulan dari kegiatan ini adalah edukasi mampu meningkatkan pengetahuan gizi siswa/siswi sehingga tenaga gizi di puskesmas setempat disarankan untuk turut memberikan edukasi tentang pedoman gizi seimbang pada siswa/siswi agar pencegahan penyakit degeneratif dapat dilakukan sejak dini.

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Applying Features Based on Word Embedding Techniques to 1D CNN for Natural Disaster Messages Classification

Cited by 8 publications

References 17 publications

Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media

Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media

MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

Edukasi Gizi Seimbang sebagai Upaya Pencegahan ?Penyakit Degeneratif pada Remaja SMP

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