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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 Jurnal Kesehatan Komunitas Indonesia Jurnal Abdimas Maduma Sriwijaya Accounting Community Services
Winda Kurnia Sari
Universitas Sriwijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health 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.
User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method Ramadhani, Indira Nailah; Sari, Winda Kurnia; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5708

Abstract

This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.
Sentiment Analysis on Google Play Store Reviews to Measure User Perception of the Gojek Application Using CNN Anissa, Cahya Rahmi; Tania, Ken Ditha; Sari, Winda Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11084

Abstract

This study was conducted to analyze sentiment towards user reviews from the Google Play Store regarding the Gojek application. The analysis aims to measure user perceptions using a Convolutional Neural Network (CNN). This study aims to understand user views on the Gojek application. By understanding user perceptions, the information obtained can be utilized by the company's service team to improve the quality of the application for users. User perceptions are grouped into three labels: positive, neutral, and negative. To produce an effective model, this study uses three data sharing ratios simultaneously with the same parameters: 90:10, 80:20, and 70:30. Due to the large amount of data, random sampling is needed to balance the data and thus increase accuracy in the data processing process. Model evaluation was carried out using a confusion matrix, precision, recall, and F1-Score. The results obtained with the highest accuracy of 84.29%. This study successfully demonstrates that CNN is able to process user review data well.
Pelatihan Integrasi Artificial Intelligence dalam Perancangan Bahan Ajar Bahasa Inggris bagi Guru SMP Negeri 56 Palembang Primanita, Anggina; Rini, Dian Palupi; Sari, Winda Kurnia; Falah, Miftahul; Utari, Meylani; Yusliani, Novi; Yunita; Marieska, Mastura Diana; Fiftinova
JURNAL ABDIMAS MADUMA Vol. 5 No. 1 (2026): Januari, 2026
Publisher : English Lecturers and Teachers Association (ELTA)

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Abstract

Kegiatan pengabdian ini bertujuan meningkatkan kemampuan guru SMP Negeri 56 Palembang dalam menyusun bahan ajar Bahasa Inggris dengan memanfaatkan Artificial Intelligence, khususnya platform Quizgecko. Pelaksanaan kegiatan meliputi tahap persiapan, pelatihan, pendampingan, evaluasi pre-test dan post-test, serta tindak lanjut. Selama pelatihan, guru mempraktikkan pengunggahan materi, pembuatan soal otomatis, serta analisis hasil keluaran Quizgecko. Evaluasi menunjukkan adanya peningkatan yang jelas, dengan rata-rata nilai pre-test sebesar 74,73 meningkat menjadi 88,70 pada post-test, atau peningkatan 18,69%. Hasil tersebut mengindikasikan bahwa pemanfaatan AI mendukung efisiensi penyusunan bahan ajar serta membantu guru memperkaya strategi pembelajaran di kelas.  Program ini memberikan dasar penting bagi integrasi teknologi berkelanjutan dalam proses pembelajaran di sekolah Kata Kunci : Kecerdasan Buatan, Quizgecko, Bahan Ajar, Pelatihan Guru, Bahasa Inggris
The Influence of Experience-Centric IT Governance on Digital Ethics Perception in Social Commerce Gumay, Naretha Kawadha Pasemah; Afrina, Mira; Indah, Dwi Rosa; Sari, Winda Kurnia; Sartika, Widya
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5750

Abstract

Assistance in Designing Brand Guidelines to Develop a Professional Identity for Young Generation B. Azhar, Iman Saladin; Sari, Winda Kurnia; Exaudi, Kemahyanto; Prasetyo, Aditya Putra Perdana; Putra, Pacu; Firnando, Ricy
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 7, No 1 (2026): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v7i1.30-39

Abstract

This community service programme aims to support the youth participants of Rumah Cahaya Indonesia (RCI) Palembang in designing a comprehensive Brand Guideline to strengthen their professional identity. The main problem identified is the inconsistency of visual identity and the limited understanding of branding principles among the participants. To address this issue, the programme implements workshops and technical mentoring sessions focusing on fundamental branding concepts, the structure and components of a Brand Guideline, and the importance of maintaining consistent visual communication. The activities include preparation, development of learning materials, workshop implementation, hands-on practice, and evaluation. Participants are trained to use Adobe Illustrator and Adobe After Effects to produce visual and motion-based brand assets. The programme received positive feedback, indicating an improvement in participants’ understanding and skills in branding. The results show that this initiative contributes to enhancing digital creative literacy and supports young people in developing a more structured and professional brand identity.
Community Health Workers’ Digital Competencies in Using Digital Technologies and Artificial Intelligence Sabrina Intan Zoraya; Abdillah Adipatria Budi Azhar; Winda Kurnia Sari; Iman Saladin Budi Azhar; Rizma Adlia Syakurah
Jurnal Kesehatan Komunitas Indonesia Vol 6 No 1: April 2026
Publisher : Al-Hijrah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58545/jkki.v6i1.685

Abstract

Background: Strengthening community health workers’ (CHWs) digital competencies is critical to ensuring that digital health transformation translates into improved community-level services. Aims: This systematic review aimed to synthesize evidence on the effectiveness of training, empowerment, or capacity-building interventions in enhancing CHWs’ competencies in using digital technologies and artificial intelligence (AI). Methods: Following PRISMA guidelines, articles published between 2016 and 2026 were identified from four databases. Quantitative studies and community-based implementation reports assessing improvements in digital knowledge and/or skills were included. Results: Of 885 records screened, 30 met eligibility criteria. Interventions encompassed mobile health applications, web-based information systems, digital data management tools, and AI-assisted screening platforms. Most studies reported significant gains in knowledge scores, digital data entry and reporting skills, electronic form management, digital surveillance, and AI-assisted interpretation. Improvements in data completeness, timeliness, and perceived reporting accuracy were also documented. However, sustainability challenges emerged, including limited internet infrastructure, unequal access to devices, heterogeneous baseline digital literacy, reliance on external mentoring, short-term evaluations, and incomplete integration with routine health information systems. Conclusion: Overall, structured digital training interventions consistently enhance CHWs’ competencies and support the strengthening of primary health care. Sustainable impact, however, requires institutional embedding, standardized tiered training, infrastructure investment, and governance mechanisms to prevent digital initiatives from remaining fragmented pilot projects.
Co-Authors Abdillah Adipatria Budi Azhar Aditya Putra Perdana Prasetyo Ahmad Rifai Anissa, Cahya Rahmi Aspirani Utari Aulia, Cantika Ayuningtiyas, Pratiwi B Azhar, Iman Saladin 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 Dwi Rosa Indah Ery Erman Fiftinova Firmansyah, M. Daffa Florensia, Yesinta Gumay, Naretha Kawadha Gumay, Naretha Kawadha Pasemah Hardini Novianti Iman Saladin B. Azhar Iman Saladin Budi 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 Mastura Diana Marieska Maulana, Rahmat Meylani Utari Miftahul Falah Mira Afrina Muhammad Afif Muhammad Hidayat Nadrota Acta, Muhammad Fakhri Novi Yusliani Pacu Putra Pangestu, Ridho Primanita, Anggina Putri, Aulia Najibah Putri, Naila Raihana Rachmamtullah, Muhammad Naufal Rahman, M. Fadhil Ramadhani, Indira Nailah Rendy Isdwanta Reza Firsandaya Malik Reza Firsandaya Malik Ricy Firnando Risyahputri, Aliyananda Rizma Adlia Syakurah Robani, M Tsabita Sabrina Intan Zoraya Sahira, Mutia Salsabila, Adella Salsabila, Shofi Saputra, Marco Sari, Tri Kurnia Sartika, Widya Siti Nurmaini Yamani, Zaqqi YUNITA