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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA Applied Information System and Management MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic International Journal of Advances in Data and Information Systems Madaniya Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Makara Journal of Science Journal of Data Science and Software Engineering Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science Jurnal Komputasi
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : telematika

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Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory Amalia, Raisa; Faisal, Mohammad Reza; Indriani, Fatma; Budiman, Irwan; Mazdadi, Muhammad Itqan; Abadi, Friska; Mafazy, Muhammad Meftah
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2774

Abstract

The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.
Comparative Analysis of Distance Metrics in KNN and SMOTE Algorithms for Software Defect Prediction Khusnul Rahmi Maulidha; Mohammad Reza Faisal; Setyo Wahyu Saputro; Friska Abadi; Dodon Turianto Nugrahadi; Puput Dani Prasetyo Adi; Hariyady Hariyady
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3008

Abstract

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.
Automatic Analysis of Natural Disaster Messages on Social Media Using IndoBERT and Multilingual BERT Yasmin Dwi Safitri; Mohammad Reza Faisal; Dwi Kartini; Triando Hamonangan Saragih; Friska Abadi; Adam Mukharil Bachtiar
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 Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adam Mukharil Bachtiar Adawiyah, Laila Adi, Puput Dani Prasetyo Adinda Ayu Puspita Ramadhani Admi Syarif Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Amalia, Raisa Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Anggi Mardiyono Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Bedy Purnama budiman, irwan Deni Sutaji Dewi Sri Susanti Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Elvina Nur Hana Emma Andini Fadhillah, Muhammad Alif Fatma Indriani Fatma Indriani Fatma Indriani Fitra Ahya Mubarok Fitriani, Karlina Elreine Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Fuad Muhajirin Farid Ghinaya, Helma Hafiz Ilhami Hanafi, Muhammad Bashir Hanif Rahardian Hariyady Hariyady Herteno, Rudi Herteno, Rudy Hertono, Rudy Imam Tahyudin Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Kartika, Najla Putri Kenji Satou Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Khusnul Rahmi Maulidha Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Lumbanraja, Favorisen R Mafazy, Muhammad Meftah Mahmud Mahmud Maisarah Maisarah, Maisarah Mauldy Laya Mbeledogu, Njideka Nkemdilim Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fattah Edric Camilo Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Helmi Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Khalid Maulana Muhammad Mada Muhammad Mursyidan Amini Muhammad Nabil Muyassar Rahman Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Naufal Said Ngo, Luu Duc Noordyanti, Erna Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Priyatama, Muhammad Abdhi Puput Dani Prasetyo Adi Puput Dani Prasetyo Adi Purnajaya, Akhmad Rezki Putri Nabella Raditya, Virgi Atha Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Rahmina Ulfah Aflaha Ramadhan, As'ary Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reisa Siva Nandika Reza Rendian Septiawan Riadi, Agus Teguh Riadi, Putri Agustina Rinaldi Riza Susanto Banner Riza, Yusi Rizal, Muhammad Nur Rizian, Rizailo Akfa Rizki, M. Alfi Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Septyan Eka Prastya Setyo Wahyu Saputro Siti Aisyah Solechah Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Syamsiar, Syamsiar Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Uthami, Mariza Vina Damayanti, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Warsuta, Bambang Wildan Panji Tresna Winda Agustina Yasmin Dwi Safitri Yeni Rahkmawati Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi