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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Electrical Engineering and Computer Science
Chawaphan, Pharan
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Computer Science & IT

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Designing stair climbing wheelchairs with surface prediction using theoretical analysis and machine learning Chawaphan, Pharan; Maneetham, Dechrit; Crisnapati, Padma Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp120-132

Abstract

Urban settings present considerable obstacles for those use personal mobility wheelchairs, especially when it comes to manoeuvring stairs. The objective of this study is to improve the safety and ease of use of wheelchairs designed for ascending stairs. The study aims to tackle the significant issue of instability and limited ability to adjust to different types of terrain. This research employs a holistic methodology that combines theoretical dynamic analysis, hardware design and simulation, and field testing, in addition to advanced machine learning approaches for surface prediction. Theoretical models guarantee the stability of the wheelchair, while hardware simulations offer valuable insights into its structural integrity. The data obtained from inertial measurement unit (IMU) sensors during field tests is analysed and categorised using models like random forest and gradient boosting, which exhibit exceptional accuracy in forecasting movement circumstances. The results demonstrate that the implementation of these combined techniques greatly enhances the wheelchair’s capacity to safely manoeuvre over urban barriers. The study finds that the suggested solutions show great potential for creating intelligent mobility aids, which might be used to improve accessibility for those with mobility limitations.
Analysis of Combination Machine Learning Classification with Feature Selection Technique for Lecturer Performance Analysis Model Srinadi, Ni Luh Putri; Antarajaya, I Nyoman Suraja; Widhyastuti, Luh Putu Wiwien; Hostiadi, Dandy Pramana; Rini, Erma Sulistyo; Chawaphan, Pharan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4356

Abstract

Machine learning-based classification techniques are widely utilized for accurate analysis in various fields. This study focuses on assessing lecturer performance in higher education to enhance teaching standards and produce high-quality learning outcomes. Previous studies have employed multiparameter approaches, such as statistical correlation analysis, but these methods fail to achieve optimal accuracy and precision due to limited alignment with data characteristics. This research proposes a lecturer performance measurement model by evaluating three machine learning algorithms: k-Nearest Neighbors (k-NN), Decision Tree, and Na¨pve Bayes. The model integrates three feature selection techniques to improve classification performance: ANOVA, Information Gain, and Chi-Square. The study aims to enhance classification accuracy and assess the impact of feature selection techniques on performance metrics. A significant contribution of this research is introducing a dynamic feature selection approach tailored to data characteristics, which improves classification model performance. The methodology comprises three main stages: data loading and measurement of relevant parameters; data preprocessing, including filtering, cleaning, transformation, normalization, and feature selection; and performance evaluation using a machine learning-based classification approach. Experimental results demonstrate that the Decision Tree algorithm combined with Chi-Square feature selection achieved an accuracy of 0.887, precision of 0.903, recall of 0.887, and F1-score of 0.884. The proposed modelprovides a reliable framework for evaluating lecturer performance and can be utilized to recognize and reward high-performing lecturers effectively.
Analysis of Combination Machine Learning Classification with Feature Selection Technique for Lecturer Performance Analysis Model Srinadi, Ni Luh Putri; Antarajaya, I Nyoman Suraja; Widhyastuti, Luh Putu Wiwien; Hostiadi, Dandy Pramana; Rini, Erma Sulistyo; Chawaphan, Pharan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4356

Abstract

Machine learning-based classification techniques are widely utilized for accurate analysis in various fields. This study focuses on assessing lecturer performance in higher education to enhance teaching standards and produce high-quality learning outcomes. Previous studies have employed multiparameter approaches, such as statistical correlation analysis, but these methods fail to achieve optimal accuracy and precision due to limited alignment with data characteristics. This research proposes a lecturer performance measurement model by evaluating three machine learning algorithms: k-Nearest Neighbors (k-NN), Decision Tree, and Na¨pve Bayes. The model integrates three feature selection techniques to improve classification performance: ANOVA, Information Gain, and Chi-Square. The study aims to enhance classification accuracy and assess the impact of feature selection techniques on performance metrics. A significant contribution of this research is introducing a dynamic feature selection approach tailored to data characteristics, which improves classification model performance. The methodology comprises three main stages: data loading and measurement of relevant parameters; data preprocessing, including filtering, cleaning, transformation, normalization, and feature selection; and performance evaluation using a machine learning-based classification approach. Experimental results demonstrate that the Decision Tree algorithm combined with Chi-Square feature selection achieved an accuracy of 0.887, precision of 0.903, recall of 0.887, and F1-score of 0.884. The proposed modelprovides a reliable framework for evaluating lecturer performance and can be utilized to recognize and reward high-performing lecturers effectively.
Co-Authors Antarajaya, I Nyoman Suraja Dandy Pramana Hostiadi Erma Sulistyo Rini, Erma Sulistyo Maneetham, Dechrit Ni Luh Putri Srinadi Padma Nyoman Crisnapati Widhyastuti, Luh Putu Wiwien