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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Alqaaf, Muhammad
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Support vector machine method for classifying severity of Alzheimer's based on hippocampus object using magnetic resonance imaging modalities Supriyanti, Retno; Riyanto, Arif Pujo; Ramadhani, Yogi; Aliim, Muhammad Syaiful; Akbar, Muhammad Irham; Widodo, Haris Budi; Alqaaf, Muhammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6322-6331

Abstract

Alzheimer's disease is a degenerative brain condition that causes progressive decline in several aspects. Starting from memory, cognitive or thinking abilities, speaking abilities, and behavior. Currently, Alzheimer's diagnosis uses some methods, such as blood tests, scanning with computerized tomography scan (CT scan), or magnetic resonance imaging (MRI). As a reference for determining the level of severity, doctors usually use clinical dementia rating (CDR). CDR is a numerical scale used to measure the severity of dementia symptoms. The doctor will manually compare the patient's condition with those stated on the CDR. This condition will take quite a long time, and sometimes human error will occur. As technology and science develop, doctors can assist in manually detecting Alzheimer's using classification algorithms. Many methods can be used to classify, including the CDR support vector machine (SVM) method. Unfortunately, this method is usually only used to classify two classes. This technology allows the classification process to be carried out automatically and quickly. On the other hand, when using CDR to classify Alzheimer's severity, there are several scales, not just two classes. So, in this research, we modified the use of SVM to classify three levels of severity, namely scale 0 for normal, scale 1 for mild conditions, and scale 2 for moderate conditions. The experiments we carried out provided an accuracy of 90.9%.
The effect of features combination on coloscopy images of cervical cancer using the support vector machine method Supriyanti, Retno; Aryanto, Andreas S.; Akbar, Mohammad Irham; Sutrisna, Eman; Alqaaf, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2614-2622

Abstract

Cervical cancer is cancer that grows in cells in the cervix. This cancer generally develops slowly and only shows symptoms when it has entered an advanced stage. Therefore, it is crucial to detect cervical cancer early before serious complications arise. One way to detect cervical cancer early is to use colposcopy, which is to look closely at the condition of the cervix to find changes in cells in the cervix that have the potential to become cancer. However, this method requires the expertise of an obstetrician. This research proposes the use of image processing techniques to create automatic early detection of cervical cancer based on coloscopy images. In this paper, we will discuss image selection using an approach in the form of comparing the weights of feature vectors and then using a data distribution threshold, features that are not too influential can be eliminated. Image classification uses the Support Vector Machine (SVM) method, which makes it possible to distinguish normal images from abnormal images. Classification with feature selection and merging results can improve the consistency of SVM model performance evenly across all four SVM kernels.
Design of mobile application for communication and user interface of ESP32 potentiostat system Supriyanti, Retno; Widanarto, Wahyu; Dwi Susanto, Putra; Ardi Wicaksono, Madya; Rais Akhdan, Syafrudin; Alqaaf, Muhammad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i3.pp725-733

Abstract

The potentiostat utilizing the ESP32 has a 12-bit analog-to-digital converter (ADC), meaning the maximum value for ADC voltage readings on the ESP32 is 4095. These ADC readings are then converted into actual voltage units, ensuring more accurate measurements on the potentiostat. To facilitate the use of the ESP32 potentiostat, a mobile application must be designed as a user interface for data communication. The application will be developed on a mobile platform using a Bluetooth low energy (BLE) communication channel for easier access. The development process will utilize visual studio code as the code editor and programming languages like Dart and Flutter. The resulting application will feature a user-friendly dashboard, display data in a cyclic voltammetry graph, and store data in comma-separated values (CSV) files or images in the phone’s memory. This stored data will simplify observing results obtained from the ESP32 potentiostat.
Mobile application for diagnosing alzheimer's based on clinical dementia rating Supriyanti, Retno; Putra Yubiksana, Muhammad; Mahardika Wijonarko, Bintang Abelian; Ramadhani, Yogi; Syaiful Aliim, Muhammad; Irham Akbar, Mohammad; Budi Widodo, Haris; Widanarto, Wahyu; Alqaaf, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1607-1617

Abstract

Alzheimer's is a neurodegenerative disease characterized by memory loss, impaired thinking abilities, and changes in behavior. It is the most common form of dementia, significantly affecting a person's ability to carry out daily activities. Statistics indicate that the number of individuals suffering from Alzheimer's worldwide continues to rise as the population ages. Diagnosing Alzheimer's is a complex process that typically requires a skilled medical team. One diagnostic tool that can be utilized is an MRI machine. Previous research focused on extracting features from MRI images taken from three different cross-sections: axial, coronal, and sagittal. Based on these three types of cross-sectional images, we developed a system to classify the severity of Alzheimer's. This paper focuses on creating an Alzheimer's classification system accessible through a mobile application. The results indicate that our system has a performance accuracy of 90% in classifying the severity of the disease.
Co-Authors Akbar, Mohammad Irham Akbar, Muhammad Irham Ardi Wicaksono, Madya Aryanto, Andreas S. Dwi Susanto, Putra Eman Sutrisna Haris Budi Widodo Haris Budi Widodo, Haris Irham Akbar, Mohammad Mahardika Wijonarko, Bintang Abelian Muhammad Syaiful Aliim Putra Yubiksana, Muhammad Rais Akhdan, Syafrudin Retno Supriyanti Riyanto, Arif Pujo Wahyu Widanarto, Wahyu Yogi Ramadhani