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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOISIE (Journal Of Information Systems And Informatics Engineering) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Teknologi Dan Sistem Informasi Bisnis Sriwijaya Journal of Informatics and Applications Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Sriwijaya Accounting Community Services
Winda Kurnia Sari
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Other

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A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Tutuko, Bambang; Nurmaini, Siti; Marcelino, Jordan; Isdwanta, Rendy; Khairunnisa, Cholidah Zuhroh
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp352-360

Abstract

Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
Sentiment-Based Knowledge Discovery pada Aplikasi iPusnas Menggunakan Metode Machine Learning dan Deep Learning Ayuningtiyas, Pratiwi; Tania, Ken Ditha; Sari, Winda Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10258

Abstract

iPusnas is a digital library application developed by the National Library of the Republic of Indonesia since 2016, with over 1.5 million users. Despite its potential to improve literacy, the application has only received a rating of 2.0. This study conducted sentiment analysis on 7.596 reviews obatained through web scraping using the Google Play Scraper Library. The data then underwent preprocessing steps including case folding, data cleaning, tokenization, stopword removal, and stemming. Reviews were automatically labeled based on the rating score, where scores of 1-3 were categorized as negative, with 5.174 entries, and scores 4-5 as positive, with 2.422 entries. The dataset was split in an 80:20 ratio, with 80% for training, and 20% for testing. The machine learning models tested were SVM, Random Forest, CNN, LSTM, and RNN. The evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. CNN and LSTM achieved the highest accuracy (82%), Random Forest and CNN achieved the highest precision (81%), RNN the highest recall (79%) and LSTM the highest F1-score (79%). McNemar test showed a significant difference between Random Forest and CNN, Random Forest and LSTM, and between RNN and LSTM, while CNN and LSTM, as well as CNN and RNN, showed no significant difference.
Co-Authors Aditya Putra Perdana Prasetyo Ahmad Rifai Aspirani Utari Aulia, Cantika Ayuningtiyas, Pratiwi B Azhar, Iman Saladin Bambang Tutuko Darmawahyuni, Annisa Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dinna Yunika Hardiyanti Ery Erman Firmansyah, M. Daffa Florensia, Yesinta Gumay, Naretha Kawadha Gumay, Naretha Kawadha Pasemah Hardini Novianti Iman Saladin B. Azhar Isdwanta, Rendy Jambak, Muhammad Ihsan Jordan Marcelino Kemahyanto Exaudi Ken Dhita Tania Ken Ditha Tania Khairunnisa, Cholidah Zuhroh Lakeisyah, Eka Therina Marcelino, Jordan Marshella, Siti Hariza Mas Ud, Khalid Al Maulana, Rahmat Mira Afrina Muhammad Afif Muhammad Hidayat Nadrota Acta, Muhammad Fakhri Novi Yusliani Pacu Putra Pangestu, Ridho Putri, Aulia Najibah Putri, Naila Raihana Rachmamtullah, Muhammad Naufal Rahman, M. Fadhil Rendy Isdwanta Reza Firsandaya Malik Reza Firsandaya Malik Risyahputri, Aliyananda Robani, M Tsabita Sahira, Mutia Salsabila, Adella Salsabila, Shofi Saputra, Marco Sari, Tri Kurnia Siti Nurmaini Yamani, Zaqqi