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All Journal Journal of Robotics and Control (JRC) Innovation in Research of Informatics (INNOVATICS)
Auzan, Muhammad
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Humanoid Walking Control Using LQR and ANFIS Auzan, Muhammad; Lelono, Danang; Dharmawan, Andi
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.16444

Abstract

Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.
Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNN Architectures Pawestri, Sheraton; Murinto, Murinto; Auzan, Muhammad
INNOVATICS: Innovation in Research of Informatics Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11921

Abstract

Increasingly advanced technology and the creation of social media and the internet can become a forum for people to express things or opinions. However, comments or views from users sometimes contain sarcasm making it more difficult to understand. News headlines, sometimes contain sarcasm which makes readers confused about the content of the news. Therefore, in this research, a model was created for sarcasm detection. Many methods are used for sarcasm detection, but performance still needs to be improved. So this research aims to compare the performance of two text classification methods, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in detecting sarcasm in English news headlines using RoBERTa text transformation.  RoBERTa produces a fixed-size vector of numbers 1x768. The research results show that CNN has better performance than RNN. CNN achieved the highest average accuracy of 0.891, precision of 0.878, recall of 0.874, and f1-score of 0.876, with a loss of 0.260 and a processing time of 508.1 milliseconds per epoch. On the contrary, RNN shows an accuracy of 0.711, precision of 0.692, recall of 0.620, f1-score 0.654, and loss of 0.564, with a longer processing time of 116500 milliseconds per epoch. The 10-fold cross-validation evaluation method ensures the model performs well and avoids overfitting. So it is recommended to use the combination of RoBERTa and CNN in other text classification applications that require high speed and accuracy. Further research is recommended to explore deeper CNN architectures or other architectural variations such as Transformer-based models for performance improvements.
Co-Authors Andi Dharmawan Murinto Murinto Pawestri, Sheraton