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5. Combination of BERT and Hybrid CNN-LSTM Models for Indonesia Dengue Tweets Classification.

Author

Anggraeni, Wiwik, Kusuma, Moch Farrel Arrizal, Riksakomara, Edwin, Wibowo, Radityo P., Pujiadi, and Sumpeno, Surya

Subjects
LANGUAGE models, DENGUE, ARBOVIRUS diseases, CONVOLUTIONAL neural networks, SUPERVISED learning, FENITROTHION
Abstract

In the era of social media and online communication, the surveillance and classification of disease-related information in real-time is crucial. Twitter data on dengue-related are rarely used for classification, especially textbased classification in Indonesian. Even though, the classification of dengue-related news tweets is capable of being utilized for a variety of purposes. This study presents a novel approach to classifying dengue fever-related tweets in the Indonesian context, utilizing the potential of advanced language models and hybrid neural networks. The method proposed incorporates the advantages of two deep learning architectures: Bidirectional encoder representations from transformers indonesian-based (Indo-BERT) and a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model which is still rarely used in this context. Indo-BERT, a pre-trained language model, extracts complex semantic and contextual information from text, enabling a deeper comprehension of dengue-related tweets. The Hybrid CNN-LSTM model processes textual data in tandem, extracting features via convolutional layers while maintaining temporal dependencies. To assure this model's effectiveness in the Indonesian context, a collection of dengue fever-related tweets in Indonesian was created and labeled for supervised learning. Experiments were conducted in multiple scenarios, and the results demonstrated that the combination of the Indo-BERT and Hybrid CNN-LSTM models had superior performance in classifying tweets about dengue illness into five labels, namely infected, awareness, informative, news, and others. The best models deliver an accuracy of 0.91, an F1-score of 0.90, a precision of 0.91, and a recall of 0.89. The hybrid model's efficacy surpasses that of previous approaches with an average difference in accuracy of 0.25, precision of 0.27, and recall of 0.26. It is anticipated that the Health Service can use the results of this classification to improve the dengue surveillance system with the initial data obtained from this classification. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Pembuatan Media Pembelajaran Digital Interaktif untuk Materi Pembelajaran Bahasa Arab (Nahwu dan Shorof) untuk Santri Milenial

Author

Muklason, Ahmad, primary, Riksakomara, Edwin, additional, Mahananto, Faizal, additional, Djunaidy, Arif, additional, Vinarti, Retno Aulia, additional, Anggraeni, Wiwik, additional, Nurita, Raras Tyas, additional, Utamima, Amalia, additional, Basara, Naufal Rafiawan, additional, Nuriman, Muhammad Arif, additional, Jayanty, Amalinda, additional, Khofifah, Nur, additional, Laila, Nur, additional, Mahatta, Hanif Mitsal, additional, Ananda, M. Fiqri, additional, Wibowo, Tristanto Rachmadi, additional, Hisyam, Muhammad, additional, and Kamil, Mochamad Zidan Aqila, additional

Published
2023
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7. Coding for Kids: Pengenalan Pemrograman untuk Anak Sekolah Dasar sebagai Literasi Digital Baru di Industri 4.0

Author

Muklason, Ahmad, primary, Riksakomara, Edwin, additional, Mahananto, Faizal, additional, Djunaidy, Arif, additional, Vinarti, Retno Aulia, additional, Anggraeni, Wiwik, additional, Nurita, Raras Tyas, additional, Utamima, Amalia, additional, Fauzia, Rahma, additional, Theresia, Laura Wilhelmina, additional, Fikri, Muhammad Ali, additional, Propitadewa, Hardhika, additional, Habibah, Jihaan Harvi, additional, Prasetyo, Jeremy Diaz, additional, Permatasari, Septica Tiara Indah, additional, Risnina, Nur Nindya, additional, Tsaniyah, Nazriyah Deni, additional, and Maulana, Muhammad Daffa, additional

Published
2023
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8. SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN RUTE BERSEPEDA

Author

Luthfiansyah, Rifqi, Abdurahman, Naufal, Muhammad, Hamzah, Putri, Pramesti, Brameswara, M. Rayhan, and Riksakomara, Edwin

Abstract

Bersepeda sudah menjadi olahraga yang cukup populer di Indonesia, bahkan sudah menjadi hobi sebagian orang di Jabodetabek. Beberapa pertimbangan sebelum bersepeda antara lain jarak tujuan, target, waktu, tempat istirahat. Oleh karena itu, penelitian ini merancang sistem untuk menentukan jalur bersepeda menggunakan metode Simple Additive Weighting (SAW), kelebihan metode ini adalah sudah support dan banyak diimplementasikan dalam GIS dan mampu menampilkan hasil dari pembobotan dan perhitungan berdasarkan kriteria dengan sangat mudah dan efisien. Kriteria yang digunakan yaitu berdasarkan penelitian sebelumnya dan pembobotan yang dilakukan berdasarkan asumsi dari peneliti yang sama. Hasil dari implementasi metode SAW untuk menentukan rute bersepeda berupa rancangan sistem yang terdiri dari database, arsitektur sistem, dan desain antar muka pengguna. Kata Kunci : Rute, Sepeda, Simple Additive Weighting

Published
2022
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