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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal of Robotics and Control (JRC) Journal of Soft Computing Exploration International Journal of Robotics and Control Systems Signal and Image Processing Letters
Fathurrahman, Haris Imam Karim
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application Fadlil, Abdul; Fathurrahman, Haris Imam Karim; Lin, Yu-Hao; Kamilah, Farhah; Sunardi, Sunardi
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.18879

Abstract

Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window.
Understanding of Convolutional Neural Network (CNN): A Review Purwono, Purwono; Ma'arif, Alfian; Rahmaniar, Wahyu; Fathurrahman, Haris Imam Karim; Frisky, Aufaclav Zatu Kusuma; Haq, Qazi Mazhar ul
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.888

Abstract

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.
Air quality monitoring using multi node slave IoT Rahani, Faisal Fajri; Fathurrahman, Haris Imam Karim
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.292

Abstract

Jakarta is the city with the second poorest air quality in the world. IQAir data show that Jakarta's air quality is 159. In addition, the concentration of air particles in Jakarta is 14.2 times higher than the annual guidelines of the World Health Organization (WHO). According to the WHO, exposure to air pollution causes around 7 million premature deaths and millions of years of lost health time each year. Air pollution also stunts children's growth, impairs lung function, etc. Therefore, we need a system that can be used to combine air quality to determine how dangerous a place is with air quality. Knowing air quality, certain policies or actions being taken to overcome this danger. This research aims to build and test a prototype air quality monitoring system using multi-node slaves with the Internet of Things. The prototype development process was carried out by adapting the architectural framework of the air quality monitoring system with the Internet of Things. The testing of prototype results is carried out to sound sensor values and functional success. The results of the test show that the system can run well according to the design made. The DSM501A sensor device functions to detect particles of a size larger than one micrometer, which usually include cigarette smoke, house dust, ticks, spores, pollen, and mildew, and works well so that the controller can read the surrounding air conditions well.
Light sensor optimization based on finger blood estimation and IoT-integrated Fathurrahman, Haris Imam Karim; Robi'in, Bambang; Saputro, Sigit Suryo; Sudaryanti, Sudaryanti
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.298

Abstract

Diabetes mellitus is a prevalent disease in society. This condition results from various causes, such as lifestyle choices or genetic predisposition. To prevent diabetes mellitus, blood glucose levels must be monitored periodically, and dietary consumption must be managed. Blood glucose monitoring still uses the incision or minimally invasive approach. This approach poses a risk of infection and damage. This study devised a method to optimize a light sensor to measure blood glucose levels. This approach uses sensor optimization and an integrated Internet of Things (IoT) technology. The research findings demonstrate that the use of the optimization strategy leads to increased consistency in sensor values, which may then be transmitted wirelessly through the IoT network. The research results demonstrate that using the optimization strategy leads to increased consistency in sensor values, which may then be wirelessly transmitted through the IoT network.
The Implementation of Occupational Health and Safety Using Zerosicks in Indonesia Vocational Education Ismara, Ketut Ima; Kurniawan, Arie Wibowo; Kasjono, Heru Subaris; Zamtinah, Zamtinah; Mustaqim, Bima; Fathurrahman, Haris Imam Karim; Adnanda, Azka
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 1 (2024): Mei 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i1.60586

Abstract

This study aims to determine (1) the implementation of occupational health and safety (OHS) using the Zerosicks method in vocational education and (2) the significant relationship between each aspect of the countenance stake evaluation model. The model consists of 3 stages: input (Antecedent), process (Transaction), and output. Then, the population in this study involved 727 students with a sample size of 258.03, and this research was conducted in April-June 2021 at the Prambanan Muhammadiyah Vocational High School. Furthermore, the data analysis techniques used were quantitative descriptive and SEM (Structural Equation Modeling). The study findings reveal that (1) the antecedent stage, encompassing environment, hazard, risk, knowledge, and standardization indicators, is evaluated as excellent by teachers and predominantly good by students. In the Transaction stage, focusing on risk management control, solution, implementation, and culture indicators, teachers rate it as good to perfect, while students generally rate it as very good to good. Moreover, the output stage, comprising control and OHS behavior awareness indicators, is deemed outstanding by both teachers and students. (2) The indicators of the model are valid and achievable following the results of the confirmatory factor analysis, and the structure of the theoretical model fulfils the Goodness of Fit criteria for empirical data because it has six criteria. Furthermore, an antecedent has a significant effect on Transaction and OHS Output, while a Transaction has a significant effect on OHS Output.
Automatic Tourism Waste Selection Using Image Digital and Artificial Intelligence (AI) Bakir, Muh Janwar; Fathurrahman, Haris Imam Karim
Signal and Image Processing Letters Vol 5, No 2 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i2.94

Abstract

Garbage is leftovers or discarded items that are no longer used and are no longer used by their owners. Waste is generally divided into two, namely organic and inorganic waste. Both of these wastes have benefits for us, but they also have an impact on the environment. Organic waste is waste that comes from the remains of living creatures (nature) such as animals, humans, plants that are experiencing decay or weathering. This waste is classified as environmentally friendly waste because it can be broken down by bacteria naturally and quickly. The research object studied in this research is camera detection on a waste detection tool using a camera which aims to detect types of tourism waste, where in this research I will conduct research on the detection of organic and non-organic tourism waste. The waste problem in Indonesia is caused by an increase in waste produced by the community, a lack of rubbish disposal sites (TPS), the spread of insects and rats due to rubbish, as well as environmental pollution through land, water and air pollution. So it is hoped that this tool will be able to reduce the waste problem in Indonesia, especially in the tourism environment. In this study, an average value of 0.83% was obtained, where the results were in accordance with the initial target when starting training and carrying out detection. This makes it possible to move the servo more accurately because the detection results have a high value. From the test results above, an accuracy of 90% was obtained, and the results of the servo movement were in accordance with the detection results, where if the results were organic waste detection, the servo would rotate 90 degrees and if the detection results were non-organic, the servo would not move or remain in the 0 degree position. There was no error in servo accuracy, but the error in detection was 10% from 20 samples which resulted in the servo moving in the direction of the servo movement in the error detection direction.
Agricultural Mechatronics: Orange Sorting System Using Image Segmentation Fathurrahman, Haris Imam Karim; Aulia, Imam Haris
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.142

Abstract

Sorting oranges after harvest is a critical step. It requires separating ripe fruit from unripe. Traditionally, this is done by hand. This method is inefficient and subjective. It is not suitable for modern agriculture. This study creates an automated system to solve this problem. The system uses mechatronics and image processing. Its core uses the HSV color space for image analysis. This method is effective for assessing the peel's color, which indicates maturity. The mechatronic system performs the physical sorting using a servo motor. It includes a conveyor belt, a digital camera, a processing unit, and an actuator. This research was tested on 30 sample oranges. The results show 90% accuracy in mechatronics sorting. This proves the system is a reliable and effective tool for quality control.
Co-Authors Abdul Fadlil Adnanda, Azka Alfian Ma’arif Aulia, Imam Haris Bakir, Muh Janwar Faisal Fajri Rahani Frisky, Aufaclav Zatu Kusuma Haq, Qazi Mazhar ul Heru Subaris Kasjono, Heru Subaris Kamilah, Farhah Ketut Ima Ismara Kurniawan, Ari Wibowo Lin, Yu-Hao Mustaqim, Bima Purwono Purwono, Purwono Rahmaniar, Wahyu Robi'in, Bambang Saputro, Sigit Suryo Sudaryanti Sudaryanti Sunardi Sunardi Zamtinah Zamtinah