Article Per Year (5 Year)
p-Index From 2021 - 2026
17.284
P-Index
This Author published in this journals
All Journal International Conference on Law, Business and Governance (ICon-LBG) International Conference on Education and Language (ICEL) International Conference on Engineering and Technology Development (ICETD) Jurnal English Education: Jurnal Tadris Bahasa Inggris Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika International Multidiciplinary Conference on Social Sciences (IMCoSS) Prosiding Seminar Nasional Darmajaya Jurnal Informasi dan Teknologi International Journal of Health, Economics, and Social Sciences (IJHESS) Jurnal Kolaboratif Sains Journal Of Human And Education (JAHE) Digital Transformation Technology (Digitech) ZAHRA: JOURNAL OF HEALTH AND MEDICAL RESEARCH Jurnal Ilmu Pendidikan dan Kearifan Lokal (JIPKL) Sanskara Hukum dan HAM Innovative: Journal Of Social Science Research Sahabat Sosial: Jurnal Pengabdian Masyarakat West Science Business and Management Jurnal Ekonomi dan Kewirausahaan West Science West Science Interdisciplinary Studies Jurnal ICT : Information and Communication Technologies West Science Accounting and Finance Indonesian Journal of Education (INJOE) Jurnal Ekonomi dan Bisnis West Science Interdisciplinary Studies Indonesian Journal of Enterprise Architecture West Science Social and Humanities Studies The Eastasouth Journal of Information System and Computer Science Journal of World Future Medicine, Health and Nursing Science Information System and Technology West Science Nature and Technology Journal of Innovative and Creativity The Journal of Artificial Intelligence and Development Marsialapari: Jurnal Pengabdian Kepada Masyarakat Journal of Engineering and Science Application Jurnal Ekonomi, Manajemen, Akuntansi Journal of Moeslim Research Technik Research of Scientia Naturalis Jurnal Sipakatau Indonesian Journal of Enterprise Architecture
Arnes Yuli Vandika
Universitas Bandar Lampung
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

Published : 172 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 172 Documents
Search

 14   15   16   17   18 
Digital Transformation and its Impact on Enterprise Systems in the Manufacturing Sector Judijanto, Loso; Vandika, Arnes Yuli; Zulkifli, Zulkifli; Aprian, Syawal
West Science Information System and Technology Vol. 2 No. 03 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i03.1522

Abstract

Digital transformation has revolutionized the manufacturing sector by enhancing the capabilities of enterprise systems. This study investigates the impact of digital transformation initiatives on operational efficiency, system flexibility, and decision-making capabilities within manufacturing enterprises. A quantitative approach was employed, involving 60 manufacturing enterprises and using a structured questionnaire based on a Likert scale (1-5). The data were analyzed with SPSS version 25, revealing significant positive relationships between digital transformation and the performance of enterprise systems. Regression analysis showed that digital transformation initiatives explained 42% of the variance in operational efficiency, 34% in system flexibility, and 38% in decision-making capabilities. The findings provide valuable insights into the transformative potential of digital technologies and highlight practical strategies for overcoming implementation challenges. This study contributes to the understanding of digital transformation's role in optimizing enterprise systems within the manufacturing sector.
Use of Artificial Intelligence in Operational Efficiency and Business Management Strategic Judijanto, Loso; Adnan, Ahmad Zaelani; Pranajasakti, Gilang; Vandika, Arnes Yuli; Astutik, Wahyuni Sri
West Science Information System and Technology Vol. 2 No. 03 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i03.1533

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.
Mapping the Cybersecurity Research through Bibliometric Analysis Judijanto, Loso; Endrianto , Endrixs; Vandika, Arnes Yuli
West Science Information System and Technology Vol. 2 No. 03 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i03.1534

Abstract

This bibliometric analysis explores the evolution and global landscape of cybersecurity research from 2010 to 2024. Utilizing data sourced exclusively from Scopus and analyzed through VOSviewer, this study identifies significant trends, key themes, and the dynamics of international collaboration within the field. The findings reveal a notable increase in the volume of publications over the years, highlighting a shift from basic security measures towards the integration of advanced technologies such as artificial intelligence, the Internet of Things (IoT), and blockchain. The study also maps the dense network of international collaborations, with the United States, China, India, Germany, and the United Kingdom emerging as central nodes. This analysis not only reflects the increasing complexity of cyber threats but also the global effort to develop proactive and dynamic defense mechanisms. The results emphasize the need for continuous innovation and expanded international cooperation to address the challenges posed by rapidly evolving cyber threats. Future research should consider more interdisciplinary approaches and a wider range of databases to capture the full spectrum of cybersecurity research.
Development of Machine Learning Algorithms for Anomaly Detection in Internet of Things (IoT) Networks Rith, Vicheka; Sok, Vann; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1560

Abstract

The proliferation of Internet of Things (IoT) devices has increased the vulnerability of networks to security threats, making anomaly detection essential for maintaining system integrity. Traditional security measures often fall short in identifying and mitigating complex attack patterns that can jeopardize IoT networks. This research aims to develop a machine learning algorithm specifically designed for anomaly detection in IoT environments. The goal is to enhance the ability to identify unusual behavior indicative of potential security breaches while minimizing false positives. A dataset comprising network traffic from various IoT devices was collected and preprocessed to extract relevant features. Several machine learning algorithms, including decision trees, support vector machines, and neural networks, were implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were used to assess the effectiveness of each model. The results indicated that the proposed machine learning algorithm outperformed traditional methods, achieving an accuracy of 95% in detecting anomalies. The model demonstrated a significant reduction in false positives compared to existing techniques, thereby enhancing the reliability of anomaly detection in IoT networks. The research concludes that the developed machine learning algorithm is a robust solution for detecting anomalies in IoT environments. This advancement contributes to the field by providing an effective tool for improving security measures in the rapidly evolving landscape of IoT. Future work should focus on real-time implementation and further optimization of the algorithm to adapt to dynamic network conditions.
Effectiveness of Deep Learning Models in Cybercrime Prediction Mustofa, Muhammad; Akhtar, Shazia; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1561

Abstract

The rise of cybercrime poses significant challenges to security agencies and organizations worldwide. Traditional methods of crime prediction often fall short in accurately identifying potential threats. As a result, there is a growing interest in leveraging advanced technologies, such as deep learning, to enhance predictive capabilities in cybersecurity. This research aims to evaluate the effectiveness of deep learning models in predicting cybercrime incidents. The study investigates how these models can improve accuracy and reliability compared to conventional prediction techniques. A dataset comprising historical cybercrime incidents was collected and preprocessed to extract relevant features. Various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), were implemented. The models were trained and validated using a portion of the data, while performance metrics such as accuracy, precision, recall, and F1-score were used to assess their predictive capabilities. The findings indicate that deep learning models significantly outperform traditional methods in predicting cybercrime incidents. The best-performing model achieved an accuracy of 92%, showcasing its ability to identify complex patterns in the data. Additionally, deep learning models demonstrated lower false positive rates, enhancing their reliability in real-world applications. The research concludes that deep learning is a powerful tool for predicting cybercrime, offering enhanced accuracy and efficiency. These findings contribute to the field by highlighting the potential of advanced machine learning techniques in improving cybersecurity measures. Future work should focus on refining these models and exploring their applicability in real-time cyber threat detection.
Performance Analysis of Cloud Computing Systems in Collaborative Software Development Environments Li, Zhang; Xiang, Yang; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1562

Abstract

The rise of cloud computing has transformed software development, enabling collaborative environments that enhance productivity and efficiency. However, the performance of cloud computing systems in supporting collaborative software development remains an area of active research, with various factors influencing effectiveness. This study aims to analyze the performance of cloud computing systems in collaborative software development environments. The focus is on identifying key performance metrics and their impact on team productivity and project outcomes. A mixed-methods approach was employed, combining quantitative performance metrics and qualitative surveys from development teams using cloud-based tools. Key metrics analyzed included system uptime, response time, and resource utilization. Surveys gathered insights on user satisfaction and perceived efficiency improvements. The findings reveal that cloud computing systems significantly enhance collaboration among software development teams. Metrics indicated an average system uptime of 99.5%, with response times averaging under 200 milliseconds. Survey results showed that 85% of participants reported increased productivity when using cloud-based tools compared to traditional methods. The research concludes that cloud computing systems provide substantial performance advantages in collaborative software development environments. These systems facilitate better communication, resource sharing, and project management, ultimately leading to improved project outcomes. Future research should explore the long-term effects of cloud computing on software development practices and its implications for team dynamics.
Blockchain Based Software Development for Digital Identity Management Systems Tan, Ethan; Linm, Sofia; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1563

Abstract

The increasing prevalence of digital identities has raised concerns about security, privacy, and data ownership. Traditional identity management systems often lack transparency and are vulnerable to breaches, necessitating more secure alternatives. Blockchain technology offers a decentralized approach that can enhance the security and integrity of digital identity management. This research aims to develop a blockchain-based software solution for digital identity management systems. The study focuses on creating a secure, user-centric platform that allows individuals to control their personal information while ensuring data integrity and privacy. A design-based research approach was employed, involving the development of a prototype using Ethereum blockchain technology. The system architecture was designed to facilitate secure identity verification and data storage. User testing was conducted to evaluate usability and effectiveness, with feedback collected through surveys and interviews. The prototype demonstrated significant improvements in security and user control over personal data. Key features included decentralized storage of identity information, smart contracts for verification processes, and enhanced privacy measures. User feedback indicated a high level of satisfaction with the system's usability and perceived security. The research concludes that blockchain technology presents a viable solution for digital identity management, offering enhanced security and user control. The developed software prototype demonstrates the potential for broader applications in various sectors, paving the way for future research to explore scalability and integration with existing identity management frameworks.
Parallel Processing System Optimization in High-Performance Computing for Fluid Simulation Yamamoto, Sota; Tanaka, kaito; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1565

Abstract

The growing complexity of fluid simulations in computational science necessitates the use of high-performance computing (HPC) systems. Efficient processing is critical for handling large datasets and complex algorithms, particularly in fields such as aerospace, meteorology, and biomedical engineering. Existing parallel processing methods often face limitations in scalability and resource utilization. This research aims to optimize parallel processing systems for high-performance computing applications in fluid simulations. The study focuses on enhancing computational efficiency and reducing execution time while maintaining accuracy in simulations. A multi-faceted approach was employed, combining algorithmic improvements with architectural enhancements. The research involved implementing advanced parallelization techniques, such as domain decomposition and load balancing, on a cluster of HPC nodes. Performance metrics were collected to evaluate the impact of these optimizations on simulation speed and resource utilization. The optimized system demonstrated a significant reduction in execution time, achieving up to a 60% improvement compared to baseline performance. Enhanced load balancing techniques resulted in more efficient resource distribution, leading to improved overall system performance. Accuracy of the fluid simulations remained consistent with previous results, validating the effectiveness of the optimizations. The study concludes that optimizing parallel processing systems significantly enhances the efficiency of fluid simulations in HPC environments. The findings provide valuable insights for researchers and practitioners seeking to improve computational performance in complex simulations. Future work should explore further optimizations and the integration of emerging technologies to continue advancing the capabilities of fluid simulation in high-performance computing
Application of Model Predictive Control (MPC) in Industrial Automation Robotic Systems Aslam, Bilal; Tariq, Usman; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1566

Abstract

The industrial automation sector is rapidly evolving, with a growing need for advanced control strategies to enhance the efficiency and precision of robotic systems. Model Predictive Control (MPC) has emerged as a promising approach due to its ability to handle multivariable control problems and constraints effectively. However, its application in robotic automation remains underexplored. This research aims to implement Model Predictive Control in industrial robotic systems to improve performance, adaptability, and operational efficiency. The study focuses on evaluating the effectiveness of MPC in real-time robotic applications, specifically in tasks requiring high precision and dynamic response. A simulation-based approach was employed, using a robotic arm model as a testbed for implementing MPC. The control algorithm was designed to predict future states of the system based on current measurements and optimize control inputs accordingly. Performance metrics, including tracking error and response time, were evaluated under various operational scenarios. The implementation of MPC resulted in a significant reduction in tracking error and improved response times compared to traditional control methods. The robotic arm demonstrated enhanced adaptability to changes in the environment and task requirements, showcasing the robustness of the MPC approach. The findings indicate that Model Predictive Control is an effective strategy for enhancing the performance of robotic systems in industrial automation. The successful application of MPC not only improves operational efficiency but also provides a framework for future research into more complex robotic applications. This study contributes to the growing body of knowledge on advanced control methods in automation.  
The Role of Geospatial Engineering in Handling Natural Disasters and Humanitarian Crises Htwe, Thandar; Zaw, Soe Thu; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1567

Abstract

The background of this research is the increasing frequency and intensity of natural disasters and humanitarian crises that require rapid and effective handling. Geospatial techniques have emerged as an important tool in disaster management, offering solutions for real-time mapping, monitoring, and analysis of emergency situations. The purpose of this research is to evaluate the role and effectiveness of geospatial techniques in handling natural disasters and humanitarian crises, and to identify areas that need improvement. The research method used involves analysis of current literature and case studies of various natural disaster incidents and humanitarian crises around the world. Data is collected from reliable sources such as scientific journals, government reports, and non-governmental organizations. This approach allows researchers to evaluate the practical application of geospatial techniques and identify key factors that influence their success. The results of the study show that geospatial techniques play a vital role in various stages of disaster management, from mitigation, preparedness, response, to recovery. Risk mapping, environmental change monitoring, and spatial analysis have been shown to improve the efficiency and effectiveness of emergency response operations. However, the study also identified challenges such as limited data access, the need for specialized training, and adequate technological infrastructure.The study’s conclusion confirms that geospatial techniques are a crucial component in managing natural disasters and humanitarian crises. Proper implementation can save lives and significantly reduce negative impacts. Therefore, investment in geospatial technologies, human resource training, and infrastructure development should be a priority to improve emergency response capacity in the future.
Co-Authors - Afrizal Abdillah, Muh Reza Abdul Muid Fabanyo Achmad Choerudin Ade Kurniawan Ade Kurniawan Ade Suhara Adnan, Ahmad Zaelani Afen Prana Utama Sembiring Afrizal Afrizal Agus Mukholid Agustina, Dian Resha Ahmad Cucus Ahmad Nur Budi Utama Ahmad Zaelani Adnan Ainun Jariyah Akhtar, Shazia Aldo, Novian Aldo, Novian Ali, Zainab Alim Hardiansyah Ambarwati, Rini Amelia S. Sarungallo Andi Naila Quin Azisah Aliasyahbana andrew shandy utama, andrew shandy Anggeraeni, Anggeraeni Anggit Wasesa Praja Anggun Nugroho Anggun Nugroho, Anggun Ansar Ansar Anwar, Wahyuni Aprian, Syawal Archristhea Amahoru, Archristhea Ardiana Batubara Ardiyanto Saleh Modjo Arfah, Andi Ari Kurniawan Saputra ARIEF BUDI PRATOMO Arief Yanto Rukmana Arif Mudi Priyatno Arifudin, Opan Aris Triwiyatno Aris Triwiyatno Arnadi Arnadi Arnanda, Muh Arnesta Asfahani Asfahani Aslam, Annisa Paramaswary Aslam, Bilal Aslan Aslan Asri, Ikhwanto Astutik, Wahyuni Sri Azisah Alisyahbana, Andi Naila Quin Bambang Prihantoro Nugroho Bambang Winardi Bambang Winardi Baso Intang Sappaile Basri, T Saiful Basri, T. Saiful Batubara, Ana Uzla Bekti Setiadi Bekti Utomo Bilondato, Nikma Chatarina Umbul Wahyuni Chevy Herli Sumerli Dadang Muhammad Hasyim Debi Herlina Meilani Debi Herlina Meilani Deddy Hidayat Devi Rahmah Sope Dewantara, Rizki Dewantara, Rizki Dewi Endah Fajariana, Dewi Endah Dian Resha Agustina Dina Ika Wahyuningsih Dora, Mechi Silvia Dora, Mechi Silvia Dunggio, Abdul Rivai Saleh Dwi Aris Nurohman Effendy, Femmy Eka Imama Novita Sari Eka Imama Novita Sari Eka Imama Novita Sari Eka Imama Novita Sari Eka Imama Novitasari Eko Sudarmanto Endrianto , Endrixs Endrixs Endrianto Fadhilah, St. Annisa Nurul Fadlurahman, Hannan Fahrijal, Rival Faiz Muqorrir Kaaffah Farida Arinie Soelistianto Faridah Faridah Faridah Faridah Fenty Ariani Feriyanti, Yang Gusti Fildansyah, Rully Fitriani.K, Fitriani.K Frans Sudirjo Frans Sudirjo Fujita, Miku Gilang Pranajasakti Guntur Arie Wibowo Gusma Afriani Guterres, Juvinal Ximenes Hakim, Nur Hamzah, Abd Natsir Hamzali, Said Handy Widjaya Hanifah Nurul Muthmainah Hannan Fadlurahman Harsya, Rabith Madah Khulaili Hazmi, Muhammad Helta Anggia Hendri Khuan Heri Aji Setiawan Hermansyah Hermansyah Hery Widijanto Hidayat, Deddy Hildawati Hildawati Hildawati, Hildawati Htwe, Thandar Husain Nurisman I Putu Dody Suarnatha I Wayan Adi Pratama Ikhwanto Asri Ilham Ilham Ilham Ilham Iqbal, Kiran Ismail, Rima Ruktiari Jackson Yumame Jasmin Jasmin Jasmin, Jasmin Jatmiko Wahyu Nugroho Jauhari, Burhanuddin Johannes Triestanto Joko Santoso Joko Santoso Judijanto, Loso K, Hairuddin Kasim, Jamila Kasmudin Mustapa Khrisna Agung Cendekiawan Khuan, Hendri Kirana, Sukma Ayu Candra Lan, Tran Thi Latifah Latifah Legito Lesmana, Tera Lestari Wuryanti Linm, Sofia Lola Yustrisia Loso Judijanto Loso Judijanto Luckhy Natalia Anastasye Lotte Lucky Mahesa Yahya M. Ammar Muhtadi M. Anwar Aini Made Susilawati Made Susilawati Manalu, Margareta Margareta Manalu Markus Wibowo Marwah Mayasari, Nanny Mei Rani Amalia Merakati, Indah Mislan Sihite, Mislan Moeis, Dikwan Mohammad Arifin Noor Much Deiniatur Muh Arnesta Arnanda Muh Reza Abdillah Muhammad Bitrayoga Muhammad Hazmi MUHAMMAD LUTFI Muhammad Mustofa Muhammad Syafri Muhammad Syafri, Muhammad Muhammad Syarif Hartawan Muhammadong Muhammadong Muhtadi, M. Ammar Mulia, Madepan Munazar Munazar Nam, Le Hoang Nampira, Ardi Azhar Nanny Mayasari Natasya Yunita Sugiastuti Ni Desak Made Santi Diwyarthi Ningsih, Yunia Noning Verawati Novianty Djafri Novycha Auliafendri Nugroho, Bambang Prihantoro Nukman Nunung Suryana Jamin Nur Afiani, Rulan Nur Asmah Nur Hakim Nuridayanti Nuridayanti Nurisman, Husain Nurlela, Lela Nurohman, Dwi Aris Nurul Aisyiyah Puspitarini Omar Ahmad Palupiningtyas, Dyah Pannyiwi, Rahmat Pasaribu, Daniel Peluw, Zulfikar Peng, Nam Pertiwi, Triani Prata Pranajasakti, Gilang Pratama, I Wayan Adi Priyana, Yana Qudratullah, Fyzria Raden Mohamad Herdian Bhakti Radiah Ilham Rahman Rahmat, Rezqiqah Aulia Rahmi Setiawati Rasmita, Dina Rezki Fitriani Rifky , Sehan Rina Destiana Rith, Vicheka Rival Fahrijal Rizki Andita Noviar Rizki Wahyudi Rosmawati Harahap Rosmiati Rosmiati Rovanita Rama Rovanita Rama Rulan Nur Afiani Rully Fildansyah Ruri Koesliandana Ruri Koesliandana Ruri Koesliandana Ruri Koesliandana Ruri Koesliandana Safarudin, Muhamad Sigid Sagena, Unggul Said Hamzali Samsul Arifin Samsul Arifin Santi Diwyarthi, Ni Desak Made Saputra, M. Khalid Fredy Sari, Nidia Wulan Sarungallo, Amelia S. Satria Eureka Nurseskasatmata Satya Arisena Hendrawan Sawaluddin Siregar Sayed Achmady Sehan Rifky Setiadi, Bekti Setiawan, Heri Aji Setiawan, Zunan setiawati, rahmi Setyorini, Dhiana SILVIA EKASARI Sima, Yenny Simarangkir, Manase Sahat H Soelistianto, Farida Arinie Sok, Vann Souisa, Wendy Sri Ariyanti Sri Widiastuti Sudarmanto, Eko sudarmo sudarmo Sudarmo Sudarmo Suhara, Ade Suharni Suharni Suharni Sumerli A., Chevy Herli Supriyanti Supriyanti Susanti Susanti Syafril Barus Syam Gunawan Syam Gunawan Syawal Aprian Tahir, Usman Tan, Ethan Tanaka, Kaito Tariq, Usman Tera Lesmana Thalib, Kiki Uniatri Thea Marisca Marbun B.N Tia Tanjung Tia Tanjung, Tia Titiek Rachmawati Titiek Rachmawati Toalib, Ramli Tri Budi Rahayu, Tri Budi Triyugo Winarko Triyugo Winarko, Triyugo Tu, Nguyen Minh Ummu Kalsum Unggul Sagena Upeka Mendis Usman Tahir Utama Sembiring, Afen Prana Utomo, Bekti Wahidyanti Rahayu Hastutiningtyas Wahyuni Sri Astutik Wardhani, Diky Wendy Souisa Wifasari, Septi Wijaya, Hamid Wiwin Susanty Wuryanti, Lestari Xiang, Yang Yamamoto, Sota Yana Priyana Yoga Dwi Goesty D.S Yulis, Dian Meiliani Yusuf Yusuf Zaenal Arief Zani, Benny Novico Zaw, Soe Thu Zhang Li Zohaib Hassan Sain Zulkifli Zulkifli Zulkifli Zulkifli Zunan Setiawan