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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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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.
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
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.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
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.
Kontrol Proporsional Integral Derivatif (PID) pada Kecepatan Sudut Motor DC dengan Pemodelan Identifikasi Sistem dan Tuning MA'ARIF, ALFIAN; ISTIARNO, RYAN; SUNARDI, SUNARDI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 2: Published April 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i2.374

Abstract

ABSTRAKPenelitian ini mengusulkan tentang sistem kontrol kecepatan sudut Motor Direct Current (DC) menggunakan kontrol Proporsional Integral Derivatif (PID). Pemodelan motor DC menggunakan model identifikasi sistem agar model sistem dapat mendekati sistem sesungguhnya. Data identifikasi sistem adalah nilai masukan tegangan dan nilai keluaran kecepatan sudut. Representasi model adalah model fungsi alih. Nilai kontrol PID didapatkan dengan fitur Tuning PID dengan Matlab. Perangkat penelitian adalah Arduino, sensor encoder, driver motor dan Motor DC. Pada pengujian, kendali motor DC dengan PID mampu untuk mendapatkan respon yang baik dengan nilai respon terbaik, rise time 9,4286 detik, settling time 18,5 detik dan overshoot 2 persen. Nilai variasi PWM untuk memperoleh model dan respon sistem motor DC yang bagus yaitu nilai variasi PWM 5, nilai variasi PWM 10 dan nilai variasi PWM 50, 150, 255. Dengan menggunakan metode ini, proses tuning kontrol PID dapat lebih efektif dan efisien.Kata kunci: Motor DC, Identifikasi Sistem, Kontrol PID, Tuning Matlab, Kecepatan ABSTRACTThis study proposes a direct current (DC) motor angular speed control system using Proportional Integral Derivative (PID) control. DC motor modeling uses a system identification model so that the system model can approach the real system. The system identification data is the input voltage value and the angular velocity output value. Model representation is a transfer function model. PID control values are obtained with the PID Tuning feature with Matlab. The research devices are Arduino, encoder sensor, motor driver and DC motor. In testing, the DC motor control with PID was able to get a good response with the best response value, rise time of 9.4286 seconds, settling time of 18.5 seconds and overshoot 2 percent. The PWM variation values to obtain a good DC motor system model and response are the PWM variation value 5, the PWM variation value 10 and the PWM variation value 50, 150, 255. By using this method, the PID control tuning process can be more effective and efficient.Keywords: DC Motor, System Identification, PID Control, Matlab Tuning, Speed
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.
A New Hybrid Intelligent Fractional Order Proportional Double Derivative + Integral (FOPDD+I) Controller with ANFIS Simulated on Automatic Voltage Regulator System Mohammed, Abdullah Fadhil; Marhoon, Hamzah M.; Basil, Noorulden; Ma'arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1336

Abstract

In the dynamic realm of Automatic Voltage Regulation (AVR), the pursuit of robust transient response, adaptability, and stability drives researchers to explore novel avenues. This study introduces a groundbreaking approach—the Hybrid Intelligent Fractional Order Proportional Derivative2+Integral (FOPDD+I) controller—leveraging the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). The novelty lies in the comparative analysis of three scenarios: the AVR system without a controller, with a traditional PID controller, and with the proposed FOPDD+I-based ANFIS. By fusing ANFIS with a hybrid controller, we forge a unique path toward optimized AVR performance. The hybrid controller, based on FOPID (Fractional Order Proportional Integral Derivative) principles, synergizes individual integral factors with ANFIS, augmenting them with a doubled derivative factor. The ANFIS design employs a hybrid optimization learning scheme to fine-tune the Fuzzy Inference System (FIS) parameters governing the AVR system. To train the fuzzy inference system, we utilize a Proportional-Integral-Derivative (PID) simulation of the entire AVR system, capturing essential data over approximately seven seconds. Our simulations, conducted in MATLAB/Simulink, reveal impressive performance metrics for the FOPDD+I-ANFIS approach: Rise time: 1.1162 seconds, settling time: 0.5531 seconds, Overshoot: 0%, Steady-state error: 0.00272, These results position our novel approach favorably against existing works, underscoring the transformative potential of intelligent creation in AVR control.
Selection and Evaluation of Robotic Arm based Conveyor Belts (RACBs) Motions: NARMA(L2)-FO(ANFIS)PD-I based Jaya Optimization Algorithm Fadhil Mohammed, Abdullah; Basil, Noorulden; Abdulmaged, Riyam Bassim; Marhoon, Hamzah M.; Ridha, Hussein Mohammed; Ma'arif, Alfian; Suwarno, Iswanto
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.v4i1.1243

Abstract

Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)-FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology.
Comparative Analysis of Sensor Fusion for Angle Estimation Using Kalman and Complementary Filters Chotikunnan, Phichitphon; Khotakham, Wanida; Ma'arif, Alfian; Nirapai, Anuchit; Javana, Kanyanat; Pisa, Pawichaya; Thajai, Phanassanun; Keawkao, Supachai; Roongprasert, Kittipan; Chotikunnan, Rawiphon; Imura, Pariwat; Thongpance, Nuntachai
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1674

Abstract

In engineering, especially for robots, navigation, and biomedical uses, accurate angle estimation is absolutely crucial. Using data from the IMU6050 sensor, which combines accelerometer and gyroscope readings, this work contrasts two sensor fusion methods: the Kalman filter and the complementary filter. The aim of the research is to find the most efficient filtering method for preserving accuracy and resilience throughout several motion contexts, including low-noise (standard rotation) and high-noise (external disturbances). With an eye toward improving sensor accuracy in dynamic applications, the study contribution is a thorough investigation of filter performance under different noise levels. MATLAB quantified estimate accuracy using key metrics like root mean square error (RMSE) and mean absolute error (MAE). Under controlled noise levels, our approach included methodical error analysis of both filters. Results show that, especially under low-noise conditions, the Kalman filter beats the complementary filter in terms of lower MAE and RMSE; it also shows adaptability and robustness in high-noise environments with much fewer errors than accelerometer-only and complementary filter outputs. These results show the relevance of the Kalman filter in practical settings like robotic control, motion tracking, and possible biomedical equipment, including patient positioning systems and wheelchairs with balance control. Future studies might investigate the implementation of the Kalman filter in sophisticated systems requiring accuracy, such as telemedicine robots or autonomous navigation. This work develops sensor fusion techniques and offers understanding of consistent sensor data processing in several operating environments.
Deep Learning-Based Automated Approach for Classifying Bacterial Images Abougarair, Ahmed Jaber; Oun, Abdulhamid A.; Sawan, Salah I.; Ma’arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1423

Abstract

Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resource of photographs of tiny bacteria.  This work uses a dataset that differs in terms of bacterial morphology, staining methods, and imaging circumstances. This paper aims to enhance the accuracy and reduce the computational requirements for Convolutional Neural Networks (CNN) based classification of bacterial species using GoogLeNet and AlexNet to train the models. This paper focuses on employing transfer learning to retrain pre-trained CNN models using a dataset consisting of 2000 images encompassing 12 distinct bacteria species known to be harmful to human health.  The concept of transfer learning was utilized to expedite the network's training process and enhance its categorization performance.  The results are promising, with the method achieving an accuracy of 98.7% precision, recall of 99.50%, and an F1-score of 99.45%   with classifier speed. Furthermore, the proposed bacteria classification approach demonstrated strong performance, irrespective of the size of the training data used.  This paper contributes by automating bacterial classification to facilitate faster and more accurate identification of bacterial species, which facilitates the treatment of infections and related diseases, in addition to monitoring public health, and promoting the wise use of antimicrobial drugs. To improve outcomes in the future, researchers can also integrate deep learning techniques with other machine learning methods.
Systematic Review of Unmanned Aerial Vehicles Control: Challenges, Solutions, and Meta-Heuristic Optimization Basil, Noorulden; Sabbar, Bayan Mahdi; Marhoon, Hamzah M.; Mohammed, Abdullah Fadhil; Ma'arif, Alfian
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.v4i4.1596

Abstract

Unmanned Aerial Vehicles (UAVs) are powerful tools with vast potential, yet they face significant challenges. One of the primary issues is flight endurance, limited by current battery technology. Researchers are exploring alternative power sources, including hybrid systems and internal combustion engines, and considering docking stations for battery exchange or recharging. Beyond endurance, UAVs must address safety, efficient path planning, payload capacity balancing, and flight autonomy. The complexity increases when considering swarming behaviour, collision avoidance, and communication protocols. Despite these challenges, research continues to unlock UAVs’ potential, with path planning optimization significantly advanced by meta-heuristic algorithms like the Cuckoo Optimization Algorithm (COA). Whereas, meta-heuristic algorithms can be defined as system-level strategies that are used to seek suboptimal solutions to optimization problems. It uses heuristic approaches together with the exploration/exploitation scheme in order to effectively employ within large solution spaces. However, dynamic environments still present difficulties. UAVs have evolved beyond recreational use, becoming essential in industries like agriculture, delivery services, surveillance, and disaster relief. By resolving issues related to autonomy, battery longevity, and security, the benefits of UAV technology can be fully optimized. This systematic review emphasizes the importance of continuous innovation in UAV research to overcome these challenges.
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 Ayati, Kheloud 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 Blazek, Vojtech 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 Haloui, Rabia 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, Ahmed Tawfik 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