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All Journal Jurnal Teknologi Terpadu Indonesian Journal of Electrical Engineering and Computer Science
Mariya Al Qibtiya
Universitas Pendidikan Indonesia
Computer Science & IT 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%.  
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 Nendi Suhendi Syafei Nurul Fahmi Arief Hakim Robby Ikhfa Nulfatwa Silmi Ath Thahirah Al Azhima