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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saragih, Triando Hamonangan
Universitas Lambung Mangkurat
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Journal : Telematika

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Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine Mafazy, Muhammad Meftah; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Saragih, Triando Hamonangan
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2569

Abstract

A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.
Automatic Analysis of Natural Disaster Messages on Social Media Using IndoBERT and Multilingual BERT Safitri, Yasmin Dwi; Faisal, Mohammad Reza; Kartini, Dwi; Saragih, Triando Hamonangan; Abadi, Friska; Bachtiar, Adam Mukharil
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3140

Abstract

Information about natural disasters disseminated through social media can serve as an important data source for mitigation processes and early warning systems. Social media platforms, such as X (formerly known as Twitter), have become primary channels for conveying real-time information, especially during disaster emergencies. With the large amount of unstructured disaster-related text that must be processed, the main challenge is accurately filtering and classifying messages into three categories: eyewitness, non-eyewitness, and don’t know. This research aims to compare the performance of four BERT-based natural language processing models, namely IndoBERT, IndoBERT with Masked Language Modeling (MLM), Multilingual BERT, and Multilingual BERT with MLM, in classifying Indonesian-language disaster messages. The dataset used in this study was obtained from previous research and publicly available data on GitHub, consisting of annotated messages related to floods, earthquakes, and forest fires. The method applied is a deep learning approach using the hold-out technique with an 80:20 ratio for training and testing data, and the same ratio applied to split the training data into training and validation subsets, with stratification to maintain balanced class proportions. In addition, variations in batch size were explored to evaluate their effect on model performance stability. The results show that the IndoBERT model achieved the highest performance on the flood and earthquake datasets, with accuracies of 80.67% and 81.50%, respectively. Meanwhile, IndoBERT with MLM pre-training recorded the highest accuracy on the forest fire dataset, 88.33%. Overall, IndoBERT demonstrated the most consistent and superior performance across datasets compared to the other models. These findings indicate that IndoBERT has strong capabilities in understanding Indonesian disaster-related text, and the results can be used as a foundation for developing automatic classification systems to support real-time disaster monitoring and early warning applications
Co-Authors AA Sudharmawan, AA Abadi, Friska Abdul Latief Abadi Abdullayev, Vugar Achmad Rizal Adawiyah, Laila Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Aida, Nor Ajwa Helisa Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Alfita Rakhmandasari Amelia Aditya Santika Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Athavale, Vijay Anant Athavale, Vijay Annant Bachtiar, Adam Mukharil Bachtiar, Adam Mukharil Difa Fitria Dina Arifah Diny Melsye Nurul Fajri Diny Melsye Nurul Fajri Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Erlianita, Noor Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fayyadh, Muhammad Naufaldi Febrian, Muhamad Michael Friska Abadi Haekal, Muhammad Haekal, Muhammad Hafizah, Rini Hermiati, Arya Syifa Huynh, Phuoc-Hai Ichwan Dwi Nugraha Indriani, Fatma Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Jumadi Mabe Parenreng Keswani, Ryan Rhiveldi Lilies Handayani M. Khairul Rezki Mafazy, Muhammad Meftah Mariana Dewi Muhamad Fawwaz Akbar Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Darmadi Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Ikhwan Rizki Muhammad Itqan Mazdadi Muhammad Mursyidan Amini Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Noryasminda Nugraha, Muhammad Amir Nurcahyati, Ica Nurlatifah Amini Okta Muthia Sari Purwoko, Agus Putra, Aditya Maulana Perdana Radityo Adi Nugroho Rahmat Ramadhani Rahmat Ramadhani Rahmatullah, Satrio Wibowo Ramadhani, Rahmat Ratna Septia Devi Regina Reza Faisal, Mohammad Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsha Farahdiba Saputro, Setyo Wahyu Siena, Laifansan Siti Aisyah Solechah Siti Napi'ah Suci Permata Sari Sulastri Norindah Sari Tajali, Ahmad Totok Wianto Vivi Nur Wijayaningrum Wahyu Caesarendra Wayan Firdaus Mahmudy Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yusuf Priyo Anggodo Zamzam, Yra Fatria