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All Journal Majalah Ilmiah Teknologi Elektro 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 Jurnal Ilmiah Teknologi Pertanian Agrotechno 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 Majalah Ilmiah Teknologi Elektro Signal and Image Processing Letters
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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Optimization of an Autonomous Mobile Robot Path Planning Based on Improved Genetic Algorithms Abu, N. S.; Bukhari, W. M.; Adli, M. H.; Ma’arif, Alfian
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.v4i4.19306

Abstract

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised.
Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification Saputra, Dimas Chaerul Ekty; Ma'arif, Alfian; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20898

Abstract

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.
Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization Alyazidi, Nezar M.; Hassanine, Abdalrahman M.; Mahmoud, Magdi S.; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.18746

Abstract

Cranes hold a prominent position as one of the most extensively employed systems across global industries. Given their critical role in various sectors, a comprehensive examination was necessary to enhance their operational efficiency, performance, and facilitate the control of transporting loads. Furthermore, due to the complexities involved in disassembling and reinstalling cranes, as well as the challenges associated with precisely determining system parameters, it became essential to implement adaptive control methods capable of efficiently managing the system with minimal resource requirements. This work proposes a trajectory tracking control using adaptive sliding-mode control (SMC) with particle swarm optimization (PSO) to control the position and rope length of a 3D overhead crane system with unknown parameters. The PSO is mainly used to identify the model and estimate the uncertain parameters. Then, sliding-mode control is adapted using the PSO algorithm to minimize the tracking error and ensure robustness against model uncertainties. A model of the systems is derived assuming changing rope length. The model is nonlinear of second order with five states, three actuated states: position x and y, and rope length l, and two unactuated states, which are the rope angles θx and θy. The system has uncertain parameters, which are the system’s masses Mx, My and Mz, and viscous damping coefficients Dx, Dy and Dy. A simulation study is established to illustrate the influence and robustness of the developed controller and it can enhance the tracking trajectory under different scenarios to test the scheme.
Analysis and Performance Comparison of Fuzzy Inference Systems in Handling Uncertainty: A Review Furizal, Furizal; Ma'arif, Alfian; Wijaya, Setiawan Ardi; Murni, Murni; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22123

Abstract

Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FIS-M, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation.
Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

Abstract

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
Application of Machine Learning in Healthcare and Medicine: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19640

Abstract

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.
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.
Parameter Extraction of Triple-diode Photovoltaic Model via RIME Optimizer with Neighborhood Centroid Opposite Solution Izci, Davut; Ekinci, Serdar; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22256

Abstract

In this investigation, a novel application of the RIME optimizer with neighborhood centroid opposite solution is introduced to robustly estimate parameter values for an accurate photovoltaic triple-diode model. The suggested optimizer's performance is rigorously evaluated in comparison to other well-documented methods. The evaluation of the proposed optimizer is conducted using real data from the RTC France solar cell, and the results are assessed through various evaluation metrics, including root mean square error and statistical analyses for multiple independent runs. Specifically, the proposed optimizer demonstrates superior performance by achieving the lowest objective function values compared to other algorithms. Through a comprehensive quantitative and qualitative assessment, it can be inferred that the estimated parameters of the triple-diode model obtained using the proposed optimizer surpass the accuracy of those acquired through other optimization algorithms under consideration.
Stock Price Forecasting with Multivariate Time Series Long Short-Term Memory: A Deep Learning Approach Furizal, Furizal; Ritonga, Asdelina; Ma’arif, Alfian; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22460

Abstract

Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions.
Detection of Water Clarity Level with OpenCV Image Processing Method Maharani, Siti Mutia; Ramadhan, Yogi Reza; Pranoto, Kirana Astari; Ma’arif, Alfian; Caesarendra, Wahyu; Zy, Ahmad Turmudi
Proceeding International Pelita Bangsa Vol. 1 No. 01 (2023): September 2023
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Water is the most important compound for the survival of living organisms on Earth. Clean water is essential for human activities. Clean water is characterized by various physical parameters, one of which is water clarity or turbidity. Image processing can be implemented to determine the clarity of water. Advances in image processing technology have made it possible to detect objects and colors in them, supported by libraries such as OpenCV, which provides color spaces. Thus, this research experimentally detects the clarity of water using the HSV and HSL color spaces. This research show significant differences in color for different levels of water clarity using the HSV and HSL color spaces.
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 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 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 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 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 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