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All Journal Bulletin of Electrical Engineering and Informatics Abdimas Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) INTEGRITAS : Jurnal Pengabdian International Journal of Robotics and Control Systems Automotive Experiences Journal of Fuzzy Systems and Control (JFSC)
Maghfiroh, Hari
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : International Journal of Robotics and Control Systems

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Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting Furizal, Furizal; Fawait, Aldi Bastiatul; Maghfiroh, Hari; Ma’arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1546

Abstract

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.
Collision Avoidance in Mini Autonomous Electric Vehicles Using Artificial Potential Fields for Outdoor Environment Saputro, Joko Slamet; Juliatama, Hanif Wisti; Adriyanto, Feri; Maghfiroh, Hari; Apriaskar, Esa
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1708

Abstract

The rapid advancement of technology is driving the transition toward Society 5.0, where intelligent transportation systems enhance safety, efficiency, and sustainability. One of the biggest challenges in transportation is the high frequency of vehicle accidents, with approximately 80% attributed to driver error. To mitigate this, Advanced Driver Assistance Systems (ADAS) have been developed to improve vehicle autonomy and reduce accidents. This research proposes a potential field-based collision avoidance system for autonomous vehicle navigation, where the vehicle and obstacles act as positive poles, repelling each other, while the target destination serves as a negative pole, attracting the vehicle. Experimental results demonstrate a GPS positioning error of 1.55 m with a 66% success rate and LiDAR sensor accuracy of 96.4%, exceeding the required 95% threshold. Obstacle avoidance was tested with two safety thresholds (2 m and 2.5 m) across single- and two-obstacle scenarios. The 2 m threshold resulted in shorter travel distances (16.406 m vs. 16.535 m for 2.5 m) and faster completion times (19.036 s vs. 19.144 s), while the 2.5 m threshold provided greater clearance. GPS accuracy was significantly influenced by HDOP values and satellite count, with lower HDOP improving trajectory precision. The system successfully adjusted its trajectory in response to obstacles, ensuring effective real-time navigation.
Co-Authors Abu, N. S. Adha Imam Cahyadi Adli, M. H. Adriyanto, Feri Ahmad Baballe , Muhammad Al Madani Kurniawan, Aqsha Aldi Bastiatul Fawait Fawait Alfian Ma’arif ALYA MASITHA Anif Jamaluddin Anwar, Miftahul Apribowo, Chico Hermanu Brillianto Asno Azzawagama Firdaus Baballe, Muhammad Ahmad Bizuayehu, Getenesh Melie Bukhari, W. M. Chekol, Yenework Belayneh Dwi Ana Ratna Wati Esa Apriaskar Fahmi Syuhada Fahmizal Fahmizal, Fahmizal Fatimawardhani, Sekar Furizal, Furizal Geonoky Hanifah Rahmi Fajrin, Hanifah Rahmi Hermanu, Chico Inayati Inayati Irwan Irwan Joko Slamet Saputro Juliatama, Hanif Wisti Kariyamin, Kariyamin Lambang GH, R.Lulus Lora Khaula Amifia Meiyanto Eko Sulistyo Mekonnen, Atinkut Molla Moch. Iskandar Riansyah Muhammad Nizam Munaye, Yirga Yayeh Nia Maharani Raharja Oyas Wahyunggoro Probo Santoso, Henry Putra, Mufti Reza Aulia Ramelan, Agus Sitorini Zul Hizmi, Vernanda Sujono, Augustinus Surriani, Atikah Suwarno, Iswanto Syuhada, Fahmi Trio Putra, Jimmy Winda, Wiwik Nur