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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional ELKHA : Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Science and Technology ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Emerging Science Journal IT JOURNAL RESEARCH AND DEVELOPMENT ILKOM Jurnal Ilmiah TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Kumawula: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Robotics and Control Systems Prosiding Seminar Nasional Program Pengabdian Masyarakat Techno Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Proceeding International Pelita Bangsa Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters Signal and Image Processing Letters Scientific Journal of Computer Science
Alfian Ma’arif
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences

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Journal : International Journal of Robotics and Control Systems

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Ball and Beam Control: Evaluating Type-1 and Interval Type-2 Fuzzy Techniques with Root Locus Optimization Rawiphon Chotikunnan; Phichitphon Chotikunnan; Alfian Ma'arif; Nuntachai Thongpance; Yutthana Pititheeraphab; Anuchart Srisiriwat
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.997

Abstract

This study evaluates the performance of three control systems, namely the root locus method, type-1 Mamdani fuzzy logic system (FLS), and interval type-2 Mamdani FLS, in noise-free and noisy ball and beam systems. The main contribution of this study is enabling improved design and implementation of control systems in real-world applications by offering a comprehensive understanding of each control system's performance. The methodology involves conducting four tests focusing on various input types, including a 0.8-meter step input and sine wave function, and assessing the presence of noise in the system. The performance of each control system is analyzed using parameters such as rise time, setting time, and percentage overshoot, with the interval type-2 Mamdani FLS further examined by varying footprint of uncertainty values. Results from noise-free tests reveal that the root locus method has shorter rise and setting times, but a higher percentage overshoot compared to the type-1 Mamdani FLS and type-2 Mamdani FLS. In noisy environments, the type-2 Mamdani FLS with varying Footprint of Uncertainty values outperforms the type-1 Mamdani FLS with reduced rise time, setting time, and percentage overshoot. The root locus method shows a significantly higher percentage overshoot in noisy conditions compared to the other two control systems. In conclusion, the type-2 Mamdani FLS control system demonstrates superior capability under changing conditions compared to the type-1 Mamdani FLS, with its performance varying based on footprint of uncertainty values. This study highlights the importance of selecting the appropriate control system depending on specific needs and environmental factors.
Design and Application of PLC-based Speed Control for DC Motor Using PID with Identification System and MATLAB Tuner Dodi Saputra; Alfian Ma'arif; Hari Maghfiroh; Phichitphon Chotikunnan; Safinta Nurindra Rahmadhia
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.775

Abstract

Industries use numerous drives and actuators, including DC motors. Due to the wide-ranged and adjustable speed, DC motor is widely used in many industries. However, the DC motor is prone to external disturbance and parameter changes, causing its speed to be unstable. Thus, a DC motor requires an appropriate controller design to obtain a fast and stable speed with a small steady-state error. In this study, a controller was designed based on the PID control method, with the controller gains tuned by trial-and-error and MATLAB Tuner with an identification system. The proposed controller design was implemented using PLC OMRON CP1E NA20DRA in the hardware implementation. Each tuning method was repeated five times so that the system performances could be compared and improved. Based on hardware implementation results, the trial-error method gave acceptable results but had steady-state errors. On the other hand, the use of MATLAB Tuner provided fast system responses with no steady-state error but still had oscillations with high overshoot during the transition. Therefore, the PID controller gains acquired from MATLAB Tuner must be tuned finely to get better system responses.
Technical and Economic Evaluation and Optimization of an Off-Grid Wind/Hydropower Hybrid System Reza Alayi; Yaser Ebazadeh; Hossein Monfared; Salamollah Mohammadi-Aykar; Laveet Kumar; Alfian Ma’arif; Eskandar Jamali
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.996

Abstract

Owing to global population growth, more fossil fuels are being used to meet the energy demand, which has led to increases in environmental pollution. Therefore, alternative sources such as renewable energy can be employed to meet some of the needs. Renewable energy has been criticized for its related problems such as downtime and high investment costs. In this study, a system was proposed to address these two problems by integrating wind and hydropower turbines, in Khalkhal, Ardabil, Iran as a case study. To appraise the system, an economic model was generalized, and an Ant Colony Optimization (ACO) method aiming to reduce investment costs was, implemented. The results show that to cover 100% of the required energy demand with a hybrid system of wind and hydropower turbines, the total cost of this system will be $ 202138. Also, the Cost of Energy (COE) per unit of generated power with the hydropower system, wind, and battery storage is gauged at 0.261 $/kWh. For the case study, to supply the required energy demand, 10 wind turbines and 23 kVA batteries, as well as a 14.6 kW hydropower turbine are required.
Future Potential of E-Nose Technology: A Review Furizal Furizal; Alfian Ma'arif; Asno Azzawagama Firdaus; Wahyu Rahmaniar
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1091

Abstract

Electronic Nose (E-Nose) technology unlocks the fascinating world of electronic detection, identification, and analysis of scents and odors, paving the way for innovative research and promising applications.  E-Nose mimics the human sense of smell and has gained significant attention and is applied in various fields, including the food, health and drug industries, safety and crime, and the environmental and agricultural sectors. This technology has the potential to improve quality control, medical diagnostics, and hazardous material detection processes. The E-Nose consists of a combination of gas sensors that mimic the olfactory receptors of the human nose. These sensors detect and respond to different scent molecules, resulting in unique response patterns that can be interpreted and analyzed. E-Nose has found application in the food industry to assess food quality, detect contamination, and monitor fermentation processes. In the health field, it has been used for disease diagnosis, monitoring patient health, and detecting cancerous tissue. In addition, E-Nose has been used for security purposes, such as detection of explosives and prohibited substances, as well as identification of counterfeit products. In addition, it has been used in environmental monitoring for air quality assessment and agriculture for disease detection in crops.  Despite its promising potential, widespread adoption of E-Nose faces challenges related to sensor sensitivity, data analysis algorithms (complex data interpretation), response diversity, regulatory considerations, implementation complexity, and cost. This article reviews the latest developments in E-Nose technology, explores its applications and future potential, and highlights challenges that need to be addressed.  This is considered important because E-Nose opens up a world of electronic scent identification, and analysis with the potential to improve quality control, diagnosis, and detection.
Exploring Blockchain Data Analysis and Its Communications Architecture: Achievements, Challenges, and Future Directions: A Review Article Hamzah M. Marhoon; Noorulden Basil; Alfian Ma'arif
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1100

Abstract

Blockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations.
Finite-Time Synchronization of the Rabinovich and Rabinovich-Fabrikant Chaotic Systems for Different Evolvable Parameters Edwin A. Umoh; Alfian Ma'arif; Omokhafe J. Tola; Iswanto Suwarno; Muhammed N. Umar
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1149

Abstract

This paper addresses the challenge of synchronizing the dynamics of two distinct 3D chaotic systems, specifically the Rabinovich and Rabinovich-Fabrikant systems, employing a finite-time synchronization approach. These chaotic systems exhibit diverse characteristics and evolving chaotic attractors, influenced by specific parameters and initial conditions. Our proposed low-cost finite-time synchronization method leverages the signum function's tracking properties to facilitate controlled coupling within a finite time frame. The design of finite-time control laws is rooted in Lyapunov stability criteria and lemmas. Numerical experiments conducted within the MATLAB simulation environment demonstrate the successful asymptotic synchronization of the master and slave systems within finite time. To assess the global robustness of our control scheme, we applied it across various system parameters and initial conditions. Remarkably, our results reveal consistent synchronization times and dynamics across these different scenarios. In summary, this study presents a finite-time synchronization solution for non-identical 3D chaotic systems, showcasing the potential for robust and reliable synchronization under varying conditions.
Understanding of Convolutional Neural Network (CNN): A Review Purwono, Purwono; Ma'arif, Alfian; Rahmaniar, Wahyu; Fathurrahman, Haris Imam Karim; Frisky, Aufaclav Zatu Kusuma; Haq, Qazi Mazhar ul
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.888

Abstract

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.
Enhancing the Performance of a Wind Turbine Based DFIG Generation System Using an Effective ANFIS Control Technique Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Maarif, Alfian; A. Mossa, Mahmoud
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1451

Abstract

This paper gives a look on producing energy using wind turbines and imposing robust Maximum Power Point Tracking (MPPT) technique to operate around an optimal rotational speed. A mechanical speed control based on PI controller is presented in order to extract the maximum power and optimizing the conversion efficiency of wind's kinetic energy into electric energy. A doubly-fed induction generator (DFIG) is utilized because it is preferable for applications in wind energy systems referring to the capability to regulate the output voltage and improve the stability of the grid. Its operational characteristics and the regulating procedures such as Indirect Vector Control (IVC) and other sophisticated strategies for instance the ANFIS controller enhance operating flexibility and optimum performance under diverse conditions. This has attributed the split to the improved ANFIS in that it includes the artificial neural networks besides the fuzzy logic since they improve on learning as well as parameter fine tuning. Some of them are working with a comparatively fewer number of data sets; and therefore, it can be useful in classification, modeling and control. This configuration enables to regulate the generator's magnetic flux, torque, and reactive power, adjusting to changes inside wind velocity and disruptions within the grid. The performance of the proposed MPPT-IIVC method is examined by way of simulations in Matlab/Simulink. The simulations concerned a dynamic model incorporating the wind turbine, the DFIG, and the electric grid. The results show that the proposed technique can incredibly enhance the wind energy, maintain precise regulation over speed, and effectively adjust and regulate grid voltage and frequency. The performance of the proposed ANFIS controller is compared with a PI controller and discovered that ANFIS enhances the robustness, precision, dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability and Overshoot (%).
Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review Furizal, Furizal; 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.v4i3.1489

Abstract

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.
Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda; Firdaus, Asno Azzawagama
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1375

Abstract

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.
Co-Authors . Iswanto A. Mossa, Mahmoud A. Salah, Wael Abboud, Sarah Abdel-Nasser Sharkawy Abdel-Nasser Sharkawy Abdullah Çakan Abdullah Çakan Abdullah Çakan Abdulmaged, Riyam Bassim Abougarair, Ahmed Abougarair, Ahmed J Abougarair, Ahmed J. Abougarair, Ahmed Jaber Abu, N. S. Abu, Nur Syuhadah Aburakhis, Mohamed Adhianty Nurjanah Adli, M. H. Adriyanto, Feri Agus Aktawan, Agus Ahmad Nurimam Ahmed Jaber Abougarair Akbar, Afindra Hafiedz Al Ibrahmi, Elmehdi Al Madani Kurniawan, Aqsha Al-Quraan, Ayman Al-Sabur, Raheem Al-Tahir, Sarah O. Alanssari, Ali Ihsan Alayi, Reza Aldi Bastiatul Fawait Fawait Alfan Habibillah Alfan Habibillah Alfian, Eriko Alfian, Rio Ikhsan Ali Asghar Poorat Aljanabi, Haider Dheyaa Kamil Aljarhizi, Yahya ALYA MASITHA Alyazidi, Nezar M. Amelia, Shinta Ammi, Yamina Andino Maseleno Angelo Marcelo Tusset Anhar Anhar Aninditya Anggari Nuryono Anish Pandey Anna Nur Nazilah Chamim Ansarifard, Mehdi Anton Yudhana Antonius Rajagukguk Anuchart Srisiriwat Anwar, Miftahul Anwer, Noha Apik Rusdiarna Indra Praja Ardana, Regina Olivia Fitri Ariadita, Silfia Cindy Ariska Fitriyana Ningrum Arya Adiningrat Ashadi Setiawan Asih, Hayati Mukti Asno Azzawagama Firdaus Baballe, Muhammad Ahmad Bagas Putra Anggara Bakouri, Mohsen Bakti Setiawan Bangun Aji Saputra Barbara Gunawan Basil, Noorulden Bdirina, El Khansa Belabbas, Belkacem Benabdallah, Naima Benlaloui, Idriss Berlian Shanaza Andiany Bessous, Noureddine Bilah Kebenaran Binnerianto, Binnerianto Boulal, Abdellah Bousseksou, Radouane Boutabba, T. Bukhari, W. M. Cakan, Abdullah Carlos Antonio Márquez-Vera Carlos Sánchez-López Carlos Sánchez-López Cengiz Deniz Chico Hermanu Chivon, Choeung Chojaa, Hamid Chotikunnan, Phichitphon Chotikunnan, Rawiphon Dahmani, Abdennasser Dhias Cahya Hakika Dhiya Uddin Rijalusalam Dhiya Uddin Rijalusalam Dhiya Uddin Rijalusalam Dhiya Uddin Rijalusalam Diab, Yasser Dianda Rifaldi Dimas Chaerul Ekty Saputra Dimas Dwika Saputra Dimas Herjuno Dodi Saputra Dodi Saputra Dwi Ana Ratna Wati Dyah Mutiarin Edwin A. Umoh Eka Suci Rahayu Eka Suci Rahayu Eka Widya Suseno Ekinci, Serdar Elbadaoui, Sara Elzein, I. M. Eskandar Jamali Faalah, Fadjar Nur Fadhil Mohammed, Abdullah Fadlur Rahman T. Hasan Fahassa, Chaymae Fahmi Syuhada Faikul Umam Farnaz Jahanbin Fathurrahman, Haris Imam Karim Fathurrahman, Haris Imam Karim Fatma Nuraisyah, Fatma Febryansah, M. Iqbal Ferydon Gharadaghi Feter, Muslih Rayullan Fitri Arofiati Frisky, Aufaclav Zatu Kusuma Furizal Furizal Furizal Furizal, Furizal Gatot Supangkat Gatot Supangkat Gonibala, Fajriansya Guntur Nugroho Habachi, Rachid Hadoune, Aziz Hamdi Echeikh Hamzah M Marhoon Hamzah M. Marhoon Hanini, Salah Hanna, Ahmad Zyusrotul Haq, Qazi Mazhar ul Hari Maghfiroh Hari Maghfiroh Haryo Setiawan Hasibuan, Ahmad Firdaus Hassan, Ammar M. Hassan, Reem Falah Hassanine, Abdalrahman M. Hedroug, Mohamed Elamine Hendriyanto, Raeyvaldo Dwi Heroual, Samira Hossein Monfared Houari Khouidmi Ikhsan Alfian, Rio Ikram, Kouidri Ilham Mufandi Imam Riadi Iman Permana Iman Sahrobi Tambunan Imura, Pariwat Ipin Prasojo Irfan Ahmad Irfan Ahmad Irianto Irianto Israa Al_barazanchi ISTIARNO, RYAN Iswanto Iswanto Iswanto Iswanto Iswanto Iswanto Iswanto Iswanto Iswanto Suwarno Iswanto Suwarno Iswanto Suwarno Iswanto Suwarno Izci, Davut Javana, Kanyanat Jazaul Ikhsan Jihad Rahmawan Joko Pitoyo Joko Slamet Saputro Jonattan Niño Parada Juwitaningtyas, Titisari Kamel Guesmi, Kamel Kariyamin, Kariyamin Keawkao, Supachai Kemal Thoriq Al-Azis Kherrour, Sofiane Khoirudin Wisnu Mahendra Khotakham, Wanida Kurniasari, Indah Dwi Kusuma, Isnainul Laidi, Maamar Laveet Kumar Li-Yi Chin Listyantoro, Fiki Lora Khaula Amifia Loulijat, Azeddine Magdi S. Mahmoud Magdi Sadek Mahmoud Magdi Sadek Mahmoud Maghfiroh, Hari Maharani, Siti Mutia Mahmoud A. Mossa Mahmoud Zadehbagheri Mahmoud Zadehbagheri Mahmoud, Magdi S. Mahmoud, Magdi Sadek Mahmoud, Mohamed Metwally MAJDOUBI, Rania Mangku Negara, Iis Setiawan Mangkunegara, Iis Setiawan Marco Antonio Márquez Vera Marco Antonio Márquez Vera Marhoon, Hamzah M Marhoon, Hamzah M. Mechnane, F. Meiyanto Eko Sulistyo Mekonnen, Atinkut Molla Miftahul Anwar Mila Diah Ika Putri Moch. Iskandar Riansyah Mohamad, Effendi Mohamed Akherraz Mohammad Javad Kiani Mohammed, Abdullah Fadhil Mossa, Mahmoud A. Moutchou, Mohamed Much. Fuad Saifuddin Muchammad Naseer Muhaimin Toh-arlim Muhammad Abdus Shomad Muhammad Ahmad Baballe Muhammad Amin Muhammad Arif Seto Muhammad Fuad Muhammad Heri Zulfiar Muhammad Iman Nur Hakim Muhammad Irfan Pure Muhammad Maaruf Muhammad Nizam Muhammed N. Umar Murni Murni Nadheer, Israa Nail, Bachir Nakib, Arman Mohammad Natawangsa, Hari Naufal Rahmat Setiawan Nia Maharani Raharja Nia Maharani Raharja Nia Maharani Raharja Nia Maharani Raharja Nia Maharani Raharja Nia Maharani Raharja Nia Maharani Raharja Nima Shafaghatian Nirapai, Anuchit Noorulden Basil Noorulden Basil Mohamadwasel Nugraha, Ikhwan Nugroho H, Yabes Dwi Nugroho, Oskar Ika Adi Nuntachai Thongpance Nur Syuhadah Abu Nur’Aini, Etika Nuryono Satya Widodo Nuryono, Aninditya Anggari Olunusi, Samuel Olugbenga Omar Muhammed Neda Omokhafe J. Tola Ouhssain, Said Oun, Abdulhamid A. Phichitphon Chotikunnan Phichitphon Chotikunnan Phichitphon Chotikunnan Pisa, Pawichaya Pranoto, Kirana Astari Prasetya, Wahyu Latri Prisma Megantoro Puriyanto, Riky D. Puriyanto, Riky Dwi Puriyanto, Riky Dwi Purwono Purwono, Purwono Purwono, Purwono Putra, Rean Andhika Putra, Rizal Kusuma Qazi Mazhar ul Haq Qolil Ariyansyah Rachmad Andri Atmoko Rachmawan Budiarto Radhwan A. A. Saleh Rahim Ildarabadi Rahmadhia, Safinta Nurinda Rahmadini, Vatia Fahrunisa Rahmaniar, Wahyu Rahmat Setiawan, Naufal Rahmat Setiawan, Naural Ramadhan, Yogi Reza Ramadhani, Nur Ramelan, Agus Ramli, Nor Hanuni Rania Majdoubi Ravi Sekhar Rawiphon Chotikunnan REKIK, Chokri Reza Alayi Reza Alayi Reza Alayi Ridha, Hussein Mohammed Rikwan, Rikwan Rio Ikhsan Alfian Ritonga, Asdelina Riyanarto Sarno Roongprasert, Kittipan Sabbar, Bayan Mahdi Sagita, Muhamad Rian Salah, Wael Salah, Wael A. Salamollah Mohammadi-Aykar Samir Ladaci Saputra, Ekky Haqindytia Sari, Nurjanah Arvika Sashikala Mishra Sawan, Salah I. Semma, El Alami Setiawan, Muhammad Haryo Setiawan, Naufal Rahmat Shafeeq Bakr, Zaid Sharkawy, Abdel-Nasser Shinta Amelia Shomad, Muhammad Abdus Siti Fatimah Anggraini Siti Jamilatun Sneineh , Anees Abu Subrata, Arsyad Cahya Suko Ferbriyanto sunardi sunardi Sunardi Sunardi Sunardi, Sunardi Sunat, Khamron Suwarno, Iswanto Syuhada, Fahmi Taufan Maulana Hazbi Tayane, Souad Tayeb Allaoui Thajai, Phanassanun Thongpance, Nuntachai Tibermacine, Imad Eddine Tohari Ahmad Tony K Hariadi Touti, Ezzeddine Tri Stiyo Famuji Umoh, Edwin Vera, Marco Antonio Marquez Vernandi Yusuf Muhammad Vicky Fajar Setiawan Wahyu Caesarendra Wahyu Rahmaniar Wahyu Rahmaniar Wibisono, Muhammad Damar Wijaya, Setiawan Ardi Wulandari, Annastasya Nabila Elsa Yaser Ebazadeh Yaser Ebazadeh Yassine Zahraoui Yunandha, Isro D. Yutthana Pititheeraphab Zahraoui, Yassine Zaineb Yakoub Zy, Ahmad Turmudi