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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) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 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 Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA 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 Madaniya Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Makara Journal of Science Journal of Data Science and Software Engineering Jurnal INFOTEL 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
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 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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Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants Fitriani, Karlina Elreine; Faisal, Mohammad Reza; Mazdadi, Muhammad Itqan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Prastya, Septyan Eka
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/t158qq37

Abstract

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
Analysis of the Effect of Feature Extraction on Sentiment Analysis using BiLSTM: Monkeypox Case Study on X/Twitter Noryasminda; Saragih, Triando Hamonangan; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.73

Abstract

The monkeypox outbreak has again become a global concern due to its widespread spread in various countries. Information related to the disease is widely shared through social media, especially Twitter which is a major source of public opinion. However, the complexity of language and the diverse viewpoints of users often pose challenges in accurately analyzing sentiment. Therefore, sentiment analysis of tweets about monkeypox is important to understand public perception and its impact on the dissemination of health information. This research contributes to identifying the most effective word embedding-based feature extraction method for sentiment analysis of health issues on social media. The purpose of this study is to compare the performance of word embedding methods namely Word2Vec, GloVe, and FastText in sentiment analysis of tweets about monkeypox using the BiLSTM model. Data totaling 1511 tweets were collected through a crawling process using the Twitter API. After the data is collected, manual labeling is done into three sentiment categories, namely positive, negative, and neutral. Furthermore, the data is processed through a preprocessing stage which includes data cleaning, case folding, tokenization, stopword removal, and stemming. The evaluation results show that FastText with BiLSTM produces the highest accuracy of 90%, followed by Word2Vec at 89%, and GloVe at 87%. FastText proved to be more effective in reducing classification errors, especially in distinguishing between negative and positive sentiments due to its ability to capture subword information and broader context. These findings suggest that the use of FastText can improve the accuracy of sentiment analysis, especially on health issues that develop on social media, so that it can support data-driven decision making by relevant parties in handling information dissemination. 
Optimasi Recursive Feature Elimination menggunakan Shapley Additive Explanations dalam Prediksi Cacat Software dengan klasifikasi LightGBM Hartati, Hartati; Herteno, Rudy; Faisal, Mohammad Reza; Indriani, Fatma; Abadi, Friska
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1159

Abstract

Software defect refers to issues where the software does not function properly. The mistakes in the software development process are the reasons for software defects. Software defect prediction is performed to ensure the software is defect-free. Machine learning classification is used to classify defects in software. To improve the classification model, it is necessary to select the best features from the dataset. Recursive Feature Elimination (RFE) is a feature selection method. Shapley Additive Explanations (SHAP) is a method that can optimize feature selection algorithms to produce better results. In this research, the popular boosting algorithm LightGBM will be selected as a classifier to predict software defects. Meanwhile, RFE-SHAP will be used for feature selection to identify the best subset of features. The results and discussion show that RFE-SHAP feature selection slightly outperforms RFE, with average AUC values of 0.864 and 0.858, respectively. Moreover, RFE-SHAP produces more significant results in feature selection compared to RFE. The RFE feature selection T-Test results are Pvalue = 0.039 < α = 0.05 and tcount = 3.011 > ttable = 2.776. On the contrary, the RFE-SHAP feature selection T-Test results are Pvalue = 0.000 < α = 0.05 and tcount = 11.91 > ttable = 2.776.
Co-Authors Abd. Gafur Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Admi Syarif Aflaha, Rahmina Ulfah Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Andi Farmadi Andi Farmadi Angga Maulana Akbar Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Dewi Sri Susanti Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Fitriani, Karlina Elreine Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Fuad Muhajirin Farid Ghinaya, Helma Halim, Kevin Yudhaprawira Hanif Rahardian Hartati Hartati Herteno, Rudi Herteno, Rudy Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Mahmud Mahmud Maisarah Maisarah, Maisarah Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini 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 Nafiz, Muhammad Fauzan Ngo, Luu Duc Noordyanti, Erna Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Purnajaya, Akhmad Rezki Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reza Rendian Septiawan Riadi, Agus Teguh Riadi, Putri Agustina Rinaldi Riza Susanto Banner Riza, Yusi Rizal, Muhammad Nur Rizki, M. Alfi Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rossyking, Favorisen Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Uthami, Mariza Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Wildan Panji Tresna Winda Agustina Yeni Rahkmawati Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi