The kidney plays a vital role in filtering impurities from the blood, allowing nutrients and oxygen to circulate efficiently. However, like other organs, kidney function declines with age and is affected by factors such as smoking, a sedentary lifestyle, poor nutrition, and excessive medication use. This progressive decline must be monitored to prevent it from advancing to chronic kidney disease (CKD). If poorly managed, CKD can lead to renal failure, requiring dialysis or renal replacement therapy (RRT) [1].The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods.&nbsp;In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for man patient.

Pendeteksi Fungsi Ginjal Versi 2

Good Health and Well-being

This equipment is the development of the kidney function detector version 1.

Anthroscales &amp; Tumbuh Hebat is a growth monitoring scale and application designed to help prevent stunting in Indonesia. This tool and app are intended for use by parents and health cadres, featuring a portable, modular screening device with an intuitive interface that is user-friendly for those with limited technical expertise. The scale can be used until the child reaches 3 years of age, with growth data being automatically summarized and processed. The application allows users to easily access and review the child's growth status, history, and graphs directly from the home page of the Tumbuh Hebat app.&nbsp;Anthroscales &amp; Tumbuh Hebat is expected to enhance child growth monitoring by integrating IoT-based technology. This integration enables real-time data collection and analysis, ensuring timely intervention and improving healthcare at Posyandu. The system processes and summarizes growth information automatically, reducing human error and providing clear insights into a child's developmental status, history, and trends.

Anthroscales & Tumbuh Hebat 

Anthroscales & Tumbuh Hebat is a tool for child growth monitoring

The Automatic Peristaltic Condition identification Detector is designed to detect Peristaltic&nbsp;conditions through the simple stethoscope detector. This innovative device swiftly analyzes the frequency of the peristaltic movement from a patient's stomach and provides insights into their peristaltic health. The key to this remarkable technology lies in the automatic detection of peristaltic condition in the patient's stomach, which can indicate digestive health, particularly in cases of digestive. The device measures the frequency of the peristaltic movement. The system analyze the frequency of the&nbsp;peristaltic movement using machine learning techniques to predict the condition of the digestive accurately.

Automatic peristaltic condition identification based on intelligent embedded stethoscope system

The Automatic Peristaltic Condition identification Detector is designed to detect Peristaltic  conditions through the simple stethoscope detector

This device is capable of detecting kidney disease through exhaled breath. The air exhaled from the mouth will be analyzed by the device quickly to indicate five levels of kidney conditions. It is non-invasive and does not require a blood sample, making it suitable for patients with needle phobia. The device is also internet-based, allowing the stored data to be accessed by doctors or medical professionals wherever they are.Patients with kidney failure can emit bad breath with a certain level of ammonia content. The interpretation of ammonia levels in patients with renal failure was identified in this work.&nbsp;The ppb (parts per billion) of ammonia converted into eGFR (estimated Glomerular Filtration Rate) which will determined, the severity of kidney failure was divided into 5 categories, namely normal (Stage 1), mild (stage 2), moderate (stage 3), severe (stage 4) and failure (stage 5). The values of eGFR features are used as input for the machine learning technique to predict the level of kidney failure. AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 89.9% and 95.65% for training and testing accuracy, respectfully.

Non-Invasive Kidney Disease Detector

This device is capable of detecting kidney disease through exhaled breath. It is non-invasive and does not require a blood sample, making it suitable for patients with needle phobia. 

Ir. Yosica Mariana, ST., M.T., IPU, ASEAN Eng.

I possess a robust background in architectural engineering and interior design, focusing on research in circular and sustainable design, virtual reality applications, heritage conservation, and sustainable energy use. My expertise encompasses the development of intelligent product models through reverse engineering to drive design evolution, alongside integrating architectural technology innovations within a circular economy framework. Throughout my career, I have bridged academia, research, and community engagement, demonstrating a steadfast commitment to fostering sustainable design methodologies and contributing to developing resilient, forward-thinking solutions in the built environment.

Ir. Yosica Mariana, ST., M.T., IPU, ASEAN Eng.

Faculty Member Industrial Engineering

Join Us Now!