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All Journal Abdimas Emerging Science Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) INTEGRITAS : Jurnal Pengabdian International Journal of Robotics and Control Systems Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal of Fuzzy Systems and Control (JFSC)
Maghfiroh, Hari
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Online Navigation of Self-Balancing Robot using Gazebo and RVIZ Maghfiroh, Hari; Probo Santoso, Henry
Journal of Robotics and Control (JRC) Vol 2, No 5 (2021): September (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

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Abstract

Human activity has been increasing, to support the activity, people in the modern era create robots to replace some human activities. The interest in two-wheeled balance robots has continued to increase, this is because it is highly maneuverable, making it efficient for use in various areas. In this study, the online navigation of a two-wheeled self-balancing robot is done. The connection between the robot and online navigation is using a Wi-Fi connection. The world model base on the real room is created by Gazebo and then visualized in RVIZ. The map creation and navigation process are handled by the package provided by ROS. The results of the simulation and real tracking show that the robot can move from the starting point to the destination point in either a straight or a curved path. The difference of the final position of the robot between simulation and real tracking is only (15.4 cm, 4 cm) and (9.6 cm, 43 cm) for the straight and curved path. This result proved that online navigation can be used to navigate an autonomous robot without real navigation sensors.
PROTOTIPE AUTOMATIC FEEDER DENGAN MONITORING IoT UNTUK PERIKANAN BIOFLOK LELE MASYARAKAT DUKUH PRAYUNAN Maghfiroh, Hari; Hermanu, Chico; Adriyanto, Feri
Jurnal Abdimas Vol 24, No 1 (2020): June
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LP2M), Universitas Negeri Semarang

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Abstract

The Industrial Revolution 4.0 has brought many changes both positive and negative. The positive aspect is that there has been much use of automation and robots in the industrial world so that production can increase rapidly. While the negative angle, more and more human work is replaced by machines so that it reduces job opportunities. The existence of the industrial revolution 4.0 also brought a gap between the technology literacy group and the technology stutterer group (gaptek). Villagers are a large group from the second class. For this reason, a new business opportunity that can be carried out by the village community with secondary education level is urgently needed. Then the catfish bio-floc fisheries program was chosen. A touch of automation technology and the Internet of Things (IoT) is given to increase productivity and make people literate about the technological development of the industrial revolution era 4.0.
Speed Control of Induction Motor using LQG Maghfiroh, Hari; Iftadi, Irwan; Sujono, Augustinus
Journal of Robotics and Control (JRC) Vol 2, No 6 (2021): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.26138

Abstract

The electric motor is one of the technological developments which can support the production process. Not only in the manufacturing, but also in the transportation sector. The AC motor is divided into the synchronous and asynchronous motor. One type of asynchronous motor which widely used is the induction motor. In this study, the application of the IFOC control method and the LQG speed control method will be used to control the speed of an induction motor. The PID algorithm is also used as a comparison. Tests were carried out using MATLAB software. The speed variation and load variation are tested to validate the controller performance. PID is superior in terms of settling time and IAE. On the other hand, LQG is better in energy consumption. In terms of IAE, LQG has a higher value compared to PID by up to 56.67%. On the other hand, LQG is superior in terms of energy, which is 8.38% more efficient.
PROTOTIPE AUTOMATIC FEEDER DENGAN MONITORING IoT UNTUK PERIKANAN BIOFLOK LELE MASYARAKAT DUKUH PRAYUNAN Maghfiroh, Hari; Hermanu, Chico; Adriyanto, Feri
Jurnal Abdimas Vol 24, No 1 (2020): June 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/abdimas.v24i1.20636

Abstract

The Industrial Revolution 4.0 has brought many changes both positive and negative. The positive aspect is that there has been much use of automation and robots in the industrial world so that production can increase rapidly. While the negative angle, more and more human work is replaced by machines so that it reduces job opportunities. The existence of the industrial revolution 4.0 also brought a gap between the technology literacy group and the technology stutterer group (gaptek). Villagers are a large group from the second class. For this reason, a new business opportunity that can be carried out by the village community with secondary education level is urgently needed. Then the catfish bio-floc fisheries program was chosen. A touch of automation technology and the Internet of Things (IoT) is given to increase productivity and make people literate about the technological development of the industrial revolution era 4.0.
Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments Abu, N. S.; Bukhari, W. M.; Adli, M. H.; Maghfiroh, Hari; Ma’arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21114

Abstract

This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.
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.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Improving Dynamic Routing Protocol with Energy-aware Mechanism in Mobile Ad Hoc Network Mekonnen, Atinkut Molla; Munaye, Yirga Yayeh; Chekol, Yenework Belayneh; Bizuayehu, Getenesh Melie; Maghfiroh, Hari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11994

Abstract

A Mobile Ad hoc Network (MANET) is designed for specific communication needs, where nodes dynamically interact. In a MANET, mobile nodes self-configure and frequently adapt to changes in topology due to their ability to move freely. Each node operates as a router, forwarding data to other designated nodes within the network. Since these mobile nodes rely on battery power, energy management becomes critical. This paper addresses the challenges of routing in MANETs by improving the Dynamic Source Routing (DSR) protocol. The proposed enhancement, termed energy-aware DSR, aims to mitigate and reduce packet loss and improve the packet delivery ratio, which often suffers due to node energy depletion. Simulations conducted with the NS-3.26 tool across varying node counts demonstrate that the energy-aware DSR protocol significantly outperforms the traditional DSR in terms of efficiency and reliability.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Concerns of Ethical and Privacy in the Rapid Advancement of Artificial Intelligence: Directions, Challenges, and Solutions Furizal, Furizal; Ramelan, Agus; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Kariyamin, Kariyamin; Masitha, Alya; Fawait, Aldi Bastiatul
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.24090

Abstract

AI is a transformative technology that emulates human cognitive abilities and processes large volumes of data to offer efficient solutions across various sectors of life. Although AI significantly enhances efficiency in many areas, it also presents substantial challenges, particularly regarding ethics and user privacy. These challenges are exacerbated by the inadequacy of global regulations, which may lead to potential abuse and privacy violations. This study provides an in-depth review of current AI applications, identifies future needs, and addresses emerging ethical and privacy issues. The research explores the important roles of AI technologies, including multimodal AI, natural language processing, generative AI, and deepfakes. While these technologies have the potential to revolutionize industries such as content creation and digital interactions, they also face significant privacy and ethical challenges, including the risks of deepfake abuse and the need for improved data protection through platforms like PrivAI. The study emphasizes the necessity for stricter regulations and global efforts to ensure ethical AI use and effective privacy protection. By conducting a comprehensive literature review, this research aims to provide a clear perspective on the future direction of AI and propose strategies to overcome barriers in ethical and privacy practices.
Co-Authors Abu, N. S. Adha Imam Cahyadi Adli, M. H. Ahmad Baballe , Muhammad Aldi Bastiatul Fawait Fawait Alfian Ma’arif ALYA MASITHA 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 Feri Adriyanto Furizal Furizal, Furizal Geonoky Hanifah Rahmi Fajrin, Hanifah Rahmi Hermanu, Chico Joko Slamet Saputro Juliatama, Hanif Wisti Kariyamin, Kariyamin Lambang GH, R.Lulus Meiyanto Eko Sulistyo Mekonnen, Atinkut Molla Munaye, Yirga Yayeh Nia Maharani Raharja Nugraha, Ikhwan Oyas Wahyunggoro Probo Santoso, Henry Puriyanto, Riky D. Ramelan, Agus Sitorini Zul Hizmi, Vernanda Sujono, Augustinus Surriani, Atikah Suwarno, Iswanto Syuhada, Fahmi Trio Putra, Jimmy Winda, Wiwik Nur Yunandha, Isro D.