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All Journal Jurnal Teknologi Terpadu Indonesian Journal of Electrical Engineering and Computer Science EPSILON: Journal of Electrical Engineering and Information Technology
Silmi Ath Thahirah Al Azhima
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Hybrid Machine Learning Model untuk memprediksi Penyakit Jantung dengan Metode Logistic Regression dan Random Forest Silmi Ath Thahirah Al Azhima; Dwicky Darmawan; Nurul Fahmi Arief Hakim; Iwan Kustiawan; Mariya Al Qibtiya; Nendi Suhendi Syafei
Jurnal Teknologi Terpadu Vol. 8 No. 1: July, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i1.539

Abstract

The heart is the main organ that must work properly and regularly. If there is interference, it will be fatal, namely the onset of a heart attack. Heart attack is included in the 10 diseases with a high risk of death. This is caused by stress factors, blood pressure, excessive work, blood sugar, and others. The purpose of this study is to predict heart disease using Machine Learning (ML) algorithms as an early preventive measure on desktop-based information systems. With Machine Learning models, the hybrid model can increase the accuracy value of an ML method that is added to other ML methods. The accuracy value obtained from the Hybrid Model Machine Learning using the Random Forest and Logistic Regression methods is 84.48%, which is an increase of 1.32%.  
Sistem Informasi Rekam Medis Berbasis Aplikasi Desktop untuk Daerah Pedesaan Dwicky Darmawan; Silmi Ath Thahirah Al Azhima; Nurul Fahmi Arief Hakim
EPSILON: Journal of Electrical Engineering and Information Technology Vol 20 No 2 (2022): EPSILON: Journal of Electrical Engineering and Information Technology
Publisher : Department of Electrical Engineering, UNJANI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55893/epsilon.v20i2.89

Abstract

Rural areas have several challenges that must be resolved. The availability of internet connections in the rural area is also one of the facilities that until now has not been evenly distributed. The clinical administration system is still conventional which has many weaknesses such as data processing, time management, and requires document storage space. So that the conventional system reduces the quality of clinical health services in the area. Therefore, this research was made with the method used is the waterfall model which aims to solve the challenges in making information systems. The advantage of using this method is that it is easy to use, directed, structured, and good for optimization. In this research, the information system created is in the form of a desktop application and is made using the Python programming language and MySQL database as a data storage area. This system is able to store personal data and patient history data according to the required information. In addition, this clinical information system is able to process data quickly and accurately, minimize lost data, search for the required data quickly, make patient data reports quickly and neatly, the system can be accessed by users even though there is no internet signal, and can reduce use of paper and reduce the use of space for patient data document storage.
Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter Silmi Ath Thahirah Al Azhima; Dadang Lukman Hakim; Robby Ikhfa Nulfatwa; Nurul Fahmi Arief Hakim; Mariya Al Qibtiya
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp878-887

Abstract

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.
Co-Authors Dadang Lukman Hakim Dwicky Darmawan Iwan Kustiawan Mariya Al Qibtiya Nendi Suhendi Syafei Nurul Fahmi Arief Hakim Robby Ikhfa Nulfatwa