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All Journal ELKHA : Jurnal Teknik Elektro Jurnal Teknologi dan Manajemen Informatika Indonesian Journal on Software Engineering (IJSE) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Engagement: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Paramita, Cinantya
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Interpretable Hybrid YOLOv8s-GWO Framework for Bounding-Box Viral Pneumonia Detection on Kaggle Chest X-ray Images Jalaluddin Amron, Azmi; Paramita, Cinantya; Šolić, Petar; Supratiknyo, Supratiknyo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5419

Abstract

Viral pneumonia continues to impose a substantial global health burden, making rapid and reliable radiographic detection essential for early clinical management. This study proposes a hybrid framework integrating the YOLOv8s detection model with the Grey Wolf Optimizer (GWO) to enhance hyperparameter tuning for Viral Pneumonia identification in chest X-ray images. A curated set of Normal and Viral Pneumonia samples was manually annotated and preprocessed before training. The optimization process involved multi-stage refinement of learning rate, momentum, weight decay, and loss-gain parameters to improve convergence stability and detection accuracy. The optimized YOLOv8s + GWO model demonstrated notable performance gains, achieving 0.965 recall, 0.983 mAP@50, and 0.827 mAP@50–95 on internal evaluations. External testing further validated its robustness, delivering 98.80% accuracy, 99.48% specificity, and 97.46% sensitivity. These results highlight not only enhanced clinical diagnostic reliability but also contributions to Informatics and Computer Science, demonstrating the effectiveness of metaheuristic-guided optimization in improving deep-learning model performance, generalization, and computational efficiency for AI-driven image detection tasks.
A Hybrid Deep Learning Architecture for Cost-Effective, Real-Time IV Infusion Anomaly Detection using IoT Sensors Brian Nafis, Muhammad; Paramita, Cinantya; Wright , Sasha-Gay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5440

Abstract

Intravenous (IV) infusion therapy is a critical medical procedure, yet manual monitoring increases the risk of complications such as air embolism and irregular infusion flow, particularly in resource-constrained environments. Although several automated infusion monitoring systems have been proposed, their high implementation cost limits practical adoption. This research develops a low-cost IoT-based infusion monitoring system capable of real-time anomaly detection using a multi-architecture machine learning approach. The proposed prototype integrates an ESP32 microcontroller with load cell (HX711) and optical (LM393) sensors to acquire time-series infusion data. Ten models from classical machine learning, deep learning, hybrid, and ensemble categories were evaluated using a dataset of 10,420 records under a unified experimental setup. The results show that XGBoost had a perfect recall (1.0000) and a strong PRAUC, while the LSTM Autoencoder had the highest F1-Score (0.9343) and precision (0.8934). The best overall performance came from hybrid and ensemble methods, with CNN–LSTM having an F1-Score of 0.89, a recall of 0.99, and a precision of 0.80. This means they would be great for clinics where being sensitive is very important. The research shows that using a low-cost IoT infrastructure with carefully chosen deep learning or ensemble models can help find problems in real time. A web dashboard explains how the technology operates and its capabilities. This study examines a cost-effective and easily scalable method to enhance infusion safety in hospitals with limited financial resources.
Co-Authors Abu Salam Adhitya Nugraha Adi Nugroho Ahmed, Foez Amalia Amalia Andreas Wilson Setiawan Astuti, Yani Parti Brian Nafis, Muhammad Catur Supriyanto Dwi Eko Waluyo Dwi Nurul Izzhati Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Febrian, Nanda Dwi Finoe, Albireo Musyaffa Guruh Fajar Shidik Hayu Wikan Kinasih Hussein, Jasim Nadheer Jalaluddin Amron, Azmi Junta Zeniarja Kencana, Lisdi Inu Manaf, Humam Wajdi Muljono Muljono Mulyanto, Edy Nohan, Rajendra Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Pergiwati, Dewi Pradana, Naufal Arsyaputra Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Frida Rodia Syamwil Saputra, Filmada Ocky Savicevic, Anamarija Jurcev Shier Nee Saw Simbolon, Calvin Samuel Sindhu Rakasiwi Šolić, Petar Sudibyo, Usman Supratiknyo, Supratiknyo Swanny Trikajanti Widyaatmadja Syamsiar, Syamsiar Tanjung, Mohamad Aprilian Tasleem, Faathir El Tjahyono, Rudi Triono, Justin Matthew Widi Utomo, Emanuel Pinesthi Wildan Akhya, Malfino Wildanil Ghozi Wright , Sasha-Gay ‘Aini, Fariida Qurrota