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All Journal JAIS (Journal of Applied Intelligent System) JOURNAL OF HEALTH SCIENCE (JURNAL ILMU KESEHATAN) Journal of Soft Computing Exploration
Hyperastuty, Agoes Santika
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Computer Science & IT Health Professions Nursing

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Pneumonia Detection on X-rays Image using YOLOv8 Model Hyperastuty, Agoes Santika; Pradana, Dio Alif; Widayani, Aisyah; Putra, Fadli Dwi; Mukhammad, Yanuar
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10865

Abstract

Pneumonia is an acute inflammatory disease of lung tissue. It is usually caused by microorganisms such as bacteria, fungi and viruses. The young children are particularly vulnerable to this illness. Report in 2019 shows that pneumonia kills almost 2,000 children under the age of five every day worldwide and affects over 800,000 children under the age of five annually. Analyzing the chest X-ray results of the patient's body is one method of diagnosing pneumonia. Therefore, this research was done to deploy a deep learning to identify the healthy and pneumonia affected lungs from chest X-ray images in order to aid in the diagnosing process. This research was done by using 2000- chest X-ray dataset—of which 1500 pneumonia lung data and 500 normal lung data. The computer vision model YOLOv8 is used in this study. The accuracy results from the training process were 56.15% in the pneumonia class and 92.03% in the normal class. Wether in the testing process yielded an average value of 0.482 (48, 2%) for the pneumonia class and 0.675 (67,5%) for the normal class. From these results, there are promising possibilities for developing a pneumonia detection system using YOLO in the future.
Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Development of an IoT-based temperature and humidity prediction system for baby incubators using solar panels Mukromin, Radian Indra; Setiawan, Fachruddin; Pradana, Dio Alif; Hyperastuty, Agoes Santika; Mukhammad, Yanuar
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.497

Abstract

Baby incubators are crucial medical devices to maintain environmental stability for babies born prematurely or have health problems. This research aims to develop a prediction system for temperature and humidity variables in baby incubators by utilizing Internet of Things (IoT) technology and solar panels as the main energy source. Despite advancements in IoT-based incubator systems, existing solutions often rely on reactive approaches, making them less effective in preventing harmful environmental fluctuations. Addressing this gap, this study focuses on optimizing temperature and humidity predictions using artificial intelligence (AI) for proactive control. Using a DHT22 sensor to measure temperature and humidity, as well as a 1 Wp solar panel, the system is designed to operate autonomously in areas with limited access to electricity. The methods used include data collection, data processing to calculate correlation coefficients, and selection of linear regression models for the analysis of relationships between variables. The results showed that the linear regression model applied had a good temperature and humidity prediction with a Mean Squared Error (MSE) value of 0.45 for the training data and 7.32 for the test data.
Implementation of internet of things for leakage current monitoring system in medical equipment Pradana, Dio Alif; Mukhammad, Yanuar; Hyperastuty, Agoes Santika
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.536

Abstract

The rise in electricity consumption, especially in the health sector, has heightened concerns about electrical safety, particularly leakage current in medical equipment. The main objective of this research is to develop an IoT-based leakage current monitoring system specifically designed for low-voltage medical devices, aiming to enhance safety and prevent electrical hazards such as electric shocks and equipment damage. The system used two current sensors module (PZEMT-004T) to measure leakage at points near the voltage source and medical components. Data were processed by a microcontroller and transmitted to a web server for real-time monitoring via mobile devices. Testing on humidifiers and ECGs showed average accuracies of 90.11% and 92.49%, respectively, within a 10 mA range. However, the system could not detect currents below the 3 mA safety threshold because of the sensors reading limitation at 10 mA, indicating a need for sensor improvements. The IoT-based system enhances medical equipment safety, with future work focusing on better sensors and AI for predictive maintenance.
Analisis Uji Kesesuaian Pesawat Sinar X Radiografi Mobile Merk Drgem Topaz-40d Menggunakan X-Ray Multimeter PIRANHA Hyperastuty, Agoes Santika; Mukhammad, Yanuar; Sugeng, Sugeng
Journal Of Health Science (Jurnal Ilmu Kesehatan) Vol 6 No 1 (2021): JOURNAL OF HEALTH SCIENCE (JURNAL ILMU KESEHATAN)
Publisher : Fakultas Ilmu Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jik.v6i1.1287

Abstract

Sesuai dengan peraturan Badan Pengawas Tenaga Nuklir Republik Indonesia nomor 2 tahun 2018 bahwa pesawat sinar X ray yang belum mempunyai sertifikat uji kesesuaian dan pesawat sinar X yang akan melampaui masa uji berkala harus mempunyai sertifikat uji kesesuaian alat. Uji kesesuaian pesawat sinar X ray dilakukan oleh lembaga yang ditunjuk oleh Kepala Badan untuk melaksanakan Uji Kesesuaian dan menerbitkan sertifikat Uji Kesesuaian. Kami telah melakukan uji kesesuaian (Compliance Test) terhadap pesawat sinar-X radiografi mobile merk DRGEM Topaz-40D. Tujuannya adalah untuk memastikan pesawat sinar X dalam kondisi ANDAL,ANDAL dalam perbaikan atau tidak ANDAL. Jenis pengujian yang dilakukan diantaranya Uji kolimasi berkas cahaya, Uji generator dan tabung sinar X. Dari hasil penelitian untuk uji iluminasi didapatkan hasil 292,25 lux, uji selisih lapangan kolimasi dengan berkas sinar X 0,6 dan 0,7, sedangkan ketegaklurusan berkas sinar X diukur dengan multimeter sinar X adalah 1 º. Untuk uji generator dan tabung sinar X meliputi uji akurasi tegangan error menunjukkan 0,4 %, uji akurasi waktu penyinaran error menunjukkan 1,4%. Uji linieritas didapatkan CL=0,02 dengan pengambilan focus besar dan focus kecil. Uji reproduksibilitaas tegangan, waktu dan dosis mendapatkan keluaran radiasi 0,001 dengan waktu eksposi 0,000 dan tegangan puncak 0.001. Uji kualitas HVL dengan pengaturan tegangan 70 dan 80 kV yang dipasang secara permanen mendapatkan hasil 2,8 mmAl dan 3,2 mmAl.Uji kebocoran tabung dihitung mencapai 1 mGy/jam. Dari semua hasil pengukuran yang telah dilakukan hasil uji dalam rentang nilai lolos uji yang ditetapkan oleh BAPETEN. Sesuai PERBA BAPETEN No.2 Tahun 2018 bahwa pesawat X-ray radiografi mobile merk DRGEM Topaz-40D dinyatakan dalam kondisi ANDAL. Metode ini bisa dilakukan untuk uji kesesuaian pesawat sinar x ray radiografi mobile atau pesawat sinar x ray radiografi umum terpasang tetap. kunci—Uji kesesuaian, pesawat x ray radiografi mobile, ANDAL, Piranha
Co-Authors Christy Atika Sari Eko Hari Rachmawanto Erna Daniati Ery Mintorini Mukromin, Radian Indra Pradana, Dio Alif Putra, Fadli Dwi Setiawan, Fachruddin Setiawan, Fachruddin Ari Sugeng Sugeng Widayani, Aisyah Yanuar Mukhammad