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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Global Medical and Health Communication SISFOTENIKA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Media Keperawatan Indonesia JURNAL TEKNOLOGI INFORMASI Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Teknik Informatika (JUTIF) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat International Journal of Informatics Engineering and Computing
Sri Hasta Mulyani
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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GENERATIVE ADVERSARIAL NETWORKS FOR ANTERIOR CRUCIATE LIGAMENT INJURY DETECTION Mulyani, Sri Hasta; Diqi, Mohammad; Salsabil, Husna Arwa
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1150

Abstract

This research explores the application of Generative Adversarial Networks (GANs) for detecting and classifying Anterior Cruciate Ligament (ACL) injuries using MRI images. The study utilized a dataset of 917 MRI images, each labeled as healthy, partially injured, or completely ruptured, to train the model. The performance of the GAN model was evaluated using a confusion matrix and a classification report, yielding an overall accuracy of 92%. The model demonstrated high proficiency in identifying healthy ACLs and partially injured ACLs but encountered some challenges in accurately identifying completely ruptured ACLs. Despite this, the results suggest that machine learning techniques, particularly GANs, have significant potential for enhancing the accuracy and efficiency of ACL injury detection. The ability of the model to distinguish between different degrees of injury could potentially aid in treatment planning. However, the study also underscores the need for further refinement of the model, particularly in improving its sensitivity in detecting severe ACL injuries. This research highlights the potential of machine learning in medical imaging and provides a solid foundation for future research in ACL injury detection and classification.
Monitoring System for Sugar Storage using DHT22, Ultrasonic, and Light Sensors Izzurohman, Moh.; Mulyani, Sri Hasta; Ordiyasa, I Wayan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/c3d6kr84

Abstract

This study develops an Internet of Things (IoT)-based monitoring system designed to maintain stable environmental conditions in palm sugar storage warehouses. The system integrates a NodeMCU ESP8266 microcontroller, a DHT22 temperature and humidity sensor, an OLED display, and a relay-controlled exhaust fan to monitor and regulate environmental parameters. Experimental evaluation was conducted using 30 measurement samples collected at 15-minute intervals in a simulated warehouse environment. The accuracy of the DHT22 sensor was assessed by comparing its readings with calibrated digital instruments. The results show that the average temperature measurement error was 0.3923°C, while the humidity error reached approximately 2.1%. The monitoring system successfully displayed real-time environmental conditions and automatically activated the exhaust fan when the temperature exceeded 30°C or the humidity surpassed 67.89%. Telegram notifications were delivered with an average latency of approximately 1–2 seconds after threshold detection, demonstrating near real-time system responsiveness. Overall, the proposed IoT-based monitoring system demonstrates reliable performance in monitoring and managing environmental conditions in palm sugar storage facilities. The integration of automated control, remote notification, and web-based data visualization provides a practical and cost-effective solution for warehouse monitoring.
Co-Authors Ariyanto Nugroho Catur Paryadi Fajar Y Zebua Giyawati Yulilania Okinarum Hamzah Hamzah Hamzah Husna Arwa Salsabil Izzurohman, Moh. Jerry Hardianto Maisarah Nurain Marselina Endah Hiswati Mohammad Diqi Okinarum, Giyawati Yulilania Ordiyasa, I Wayan Putra Wanda, Putra R.Nurhadi Wijaya Rizky Erwanto Rizky Erwanto Rodiyah Suwarto Trinidya, Fike Venny Vidayanti Vidayanti, Venny Wijaya, Nurhadi Wita Adelia