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.

The face recognition system is a rapidly developing computer vision technology capable of accurately identifying faces. Additionally, advancements in SBCs (Single Board Computers) have significantly improved, enabling fast computational processing. The integration of a face recognition system on SBCs facilitates the creation of embedded face recognition systems. In this study, Raspberry Pi was selected as the platform to implement the face recognition system, equipped with a camera module to capture images and a GPS module to support mobility. With the addition of GPS, it is possible to develop an embedded and mobile face recognition system that can contribute to enhancing infrastructure security and sustainability.In addition to detecting target faces, the system also has the capability to send location coordinates to user devices (e.g., operators) via the Telegram application (Telegram Bot), which can be highly beneficial in improving security in areas such as public monitoring or workplace environments. Testing results from this study indicate that the system successfully detects target faces and can transmit location coordinates (relative to the system's position) through the Telegram application.&nbsp;

Raspberry Pi-Based Embedded Face Recognition

Industry, Innovation and Infrastructure

This product combines portable face recognition and GPS location tracking in a single Raspberry Pi-based device

Have you ever followed the signal lights of buses on cross-island routes in Java and Sumatra? It turns out that the meaning of the left and right signals can differ when you’re following these buses. In one area, the same signal might invite you to overtake together, while in another area, it could be warning you against overtaking.Ever been frustrated by a driver in front of you suddenly signaling left and then stopping? And then, when you look, you see that a mother and her baby are in an emergency situation. This tool can also be used for such scenarios. The tool features an LED display installed in the rear window of the vehicle, operable via a remote or a mobile app.In addition to the default settings on the remote, this tool can also be operated through AI-Based voice commands via the mobile app, allowing you to directly convey messages to the driver behind you. This product received best project award from BINUS ASO School of Engineering 2023 - 2024.

DriveWave - AI-Based Communication Display for Automobile

Ever wonder the major cause of road accidents? it is miss-communication and interpretation between drivers. This tool helps reduce accidents from driver miscommunication!

The Automatic lung diseases identification Detector is designed to detect lung diseases;&nbsp;Bronchiectasis, Bronchiolitis, Chronic Inflammatory Lung Disease (COPD), Penumonia,Upper Respiratory Track Infection (URTI), Lower respiratory tract infection (LRTI),&nbsp;Asthma, and Healthy&nbsp;conditions through the simple sound stethoscope recognition.This innovative device swiftly analyzes the sound&nbsp;from the lung captured by stethoscope from the&nbsp;patients chess&nbsp;and provides insights into their lung health.&nbsp;The key to this remarkable technology lies in the automatic detection of lung condition&nbsp;in the patient's chess, which can indicate lung&nbsp;health, particularly in cases of some of the lung diseases.The device measures the analysis frequency of the lung sound. The system analyze the frequency of the&nbsp;lung sound capturing by stethoscope using machine learning techniques to predict the condition of the lung accurately.&nbsp;the device operates via the internet, allowing doctors and medical staff to access stored patient data from anywhere they are located. This connectivity enhances the scope and efficiency of healthcare delivery, bridging geographical gaps and offering medical professionals an invaluable tool for remote monitoring.&nbsp;This tool is equipped with Internet of Medical Thing (IoMT)&nbsp;that can send data via the internet website or android that can be monitored by policy makers.

Automatic lung diseases identification based on intelligent embedded stethoscope system 

The Automatic lung diseases identification Detector is designed to detect lung conditions through the simple sound stethoscope recognition.

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. 

Winda Astuti, S.T., M.Sc., Ph.D

As a holder of a PhD in Mechatronics, a Master's degree in Electrical and Computer Systems, and a Bachelor's degree in Electrical Engineering, I have developed a keen interest in various fields, including signal processing, machine learning, instrumentation, control systems, biomedical engineering, robotics, intelligent systems, and automation. My PhD dissertation, titled "Earthquake Prediction using a Hybrid Technique," focuses on predicting earthquakes based on the Earth's electric field. This research involves applying signal processing and machine learning methodologies to forecast upcoming earthquakes. In my Master's thesis, titled "Speaker Identification using Support Vector Machines," I utilized signal processing techniques to extract speech signals, and then employed machine learning algorithms to identify individuals based on these extracted features. During my undergraduate studies, I undertook a final year project to develop a weather station capable of measuring five different parameters: humidity, temperature, wind speed, wind direction, and rainfall. This weather station aims to be utilized for future weather forecasting purposes. I am passionate about applying my knowledge and engineering skills to develop innovative, user-friendly, and portable solutions for various domains, particularly in the fields of biomedical engineering, industry, and addressing challenges related to life and natural disasters.

Winda Astuti, S.T., M.Sc., Ph.D

Faculty Member Computer Engineering

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