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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi Informasi dan Komunikasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Juripol (Jurnal Institusi Politeknik Ganesha Medan) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi Zero : Jurnal Sains, Matematika, dan Terapan JURIKOM (Jurnal Riset Komputer) Data Science: Journal of Computing and Applied Informatics Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH ComTech: Computer, Mathematics and Engineering Applications Building of Informatics, Technology and Science Jurnal Mantik Indonesian Journal of Education and Mathematical Science International Journal of Advances in Data and Information Systems Randwick International of Social Science Journal Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences TECHSI - Jurnal Teknik Informatika Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) The Indonesian Journal of Computer Science Journal of Digital Market and Digital Currency IJoICT (International Journal on Information and Communication Technology)
Efendi, Syahril
Department Of Computer Science And Information Technology, University Of North Sumatra, Indonesia
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Digitalisasi Administrasi Desa Kuta Mbelin Berbasis SID Untuk Tata Kelola Pemerintahan Yang Efektif Berkelanjutan Syahril Efendi; Fauzan Nurahmadi; Fuzy Yustika Manik; Seniman; T. Henny Febriana Harumy; Ivan Jaya; Yeni Absah; Muhammad Romi Syahputra; Liana Dwi Sri Hastuti; Pauzi Ibrahim Nainggolan; Tulus Joseph Herianto; Dewi Sartika Br Ginting; Yudhistira
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 1 (2026): Januari 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i1.4326

Abstract

Abstract: The digital transformation of village administration has become an urgent need to enhance public service efficiency and ensure transparent local governance. This study analyzes the implementation of the Village Information System (SID) in Kuta Mbelin Village, STM Hulu Subdistrict, Deli Serdang Regency, as a mechanism for supporting administrative digitalization. It also evaluates the role of information technology in village governance and the integration of village statistical data within the framework of the Sustainable Development Goals (SDGs). The research employs a combination of literature review, field observation, and interviews with village officials and community members. The findings indicate that the adoption of SID in Kuta Mbelin Village improves administrative efficiency, accelerates public service delivery, and enables evidence-based data management that supports sustainable village development planning. This study recommends enhancing SID by integrating data analytics and strengthening information security to maximize its usefulness in village governance. Keywords: village digitalization; village information system; governance; SDGs; kuta mbelin village Abstrak : Transformasi digital dalam administrasi desa menjadi kebutuhan mendesak guna meningkatkan efisiensi layanan publik dan tata kelola pemerintahan yang transparan. Penelitian ini menganalisis implementasi Sistem Informasi Desa (SID) di Desa Kuta Mbelin, Kecamatan STM Hulu, Kabupaten Deli Serdang, dalam mendukung digitalisasi administrasi desa. Studi ini juga mengevaluasi peran teknologi informasi dalam tata kelola pemerintahan desa serta integrasi data statistik desa dalam perspektif Sustainable Development Goals (SDGs). Metode penelitian yang digunakan meliputi studi literatur, observasi lapangan, dan wawancara dengan perangkat desa serta masyarakat. Hasil penelitian menunjukkan bahwa penerapan SID di Desa Kuta Mbelin meningkatkan efisiensi administrasi, mempercepat pelayanan publik, dan memungkinkan pengelolaan data berbasis bukti yang mendukung perencanaan pembangunan desa berkelanjutan. Studi ini merekomendasikan optimalisasi SID dengan integrasi analitik data dan keamanan informasi untuk meningkatkan daya guna dalam tata kelola desa. Kata Kunci: digitalisasi desa; sistem informasi desa; tata kelola pemerintahan; SDGs; desa kuta mbelin
A Climate Driven Decision Support System for Rice Management Using SPI-3 Prediction and Particle Swarm Optimization Eka Putra; Syahril Efendi; Poltak Sihombing; T. Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1408

Abstract

Climate variability and irregular rainfall patterns have become critical challenges affecting rice productivity, irrigation planning, and agricultural sustainability. Previous studies have primarily focused on rainfall forecasting or drought monitoring independently, with limited attention given to transforming climate predictions into actionable agricultural management strategies. This study addresses this gap by proposing an integrated climate-driven decision support framework that combines predictive drought-index modeling with optimization-based agronomic decision-making for adaptive rice field management. The proposed framework integrates satellite-based rainfall observations, seasonal climatic characteristics, and large-scale climate variability indicators to predict short-term moisture conditions represented by the three-month standardized precipitation index. The framework consists of three interconnected stages: climate prediction, optimization, and recommendation generation. In the prediction stage, a gradient boosting regression model enhanced with Bayesian hyperparameter optimization was employed to model nonlinear relationships among rainfall accumulation, lag rainfall patterns, seasonal cyclic features, and climate variability indicators. In the optimization stage, particle swarm optimization was applied to determine optimal fertilizer dosage, irrigation allocation, and harvest timing under varying climate conditions. Experimental procedures included comparative evaluations across multiple machine learning models, hyperparameter tuning strategies, and optimization iterations. The research figures and tables demonstrate the complete framework architecture, prediction performance comparisons, optimization convergence behavior, and adaptive rice management recommendations. Experimental results show that the proposed framework achieved strong predictive performance with a coefficient of determination of 0.851, a root mean square error of 0.391, and a mean absolute error of 0.322. Comparative analysis further confirmed that integrating climate variability indicators significantly improved predictive accuracy compared with baseline models using only historical rainfall information. The optimization process also demonstrated stable convergence toward climate-adaptive agronomic recommendations.
Deep Deterministic Policy Gradient for Simulation-Based Control of Water Quality in Nano Aquatic Systems Iwan Fitrianto Rahmad; Syahril Efendi; Poltak Sihombing; Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1447

Abstract

This study proposes a simulation-based Deep Deterministic Policy Gradient (DDPG) framework for water-quality control in nano aquatic systems. Nano tanks are highly sensitive to small disturbances because their limited water volume reduces buffering capacity and causes rapid changes in dissolved oxygen, ammonia, pH, temperature, biological oxygen demand, and chemical oxygen demand. To represent these coupled dynamics, a nonlinear simulation model adapted from the Continuously Stirred Tank Reactor concept is developed and implemented as an OpenAI Gym-compatible reinforcement learning environment. The DDPG agent learns continuous control actions related to aeration, feeding, and filtration through repeated interaction with the simulated nano-tank environment. The proposed nonlinear CSTR-DDPG framework is evaluated against a linear-model DDPG baseline using RMSE, cumulative reward, and closed-loop control performance. Simulation results show that the nonlinear model reduced RMSE by 45.2% for dissolved oxygen, 64.0% for ammonia, and 61.3% for pH compared with the linear baseline. The DDPG agent also achieved a 41.7% higher cumulative reward under the same reward structure. These findings indicate that nonlinear simulation can provide a more informative training environment for DDPG-based water-quality control. However, the present study remains limited to simulation-based evaluation, and physical validation using calibrated sensors, actuators, communication-delay analysis, and real nano-tank experiments is required in future work.
INOVASI DIGITAL DAN TEKNOLOGI UNTUK PEMBANGUNAN BERKELANJUTAN DI DESA KUTA MBELIN Syahril Efendi; Fauzan Nurahmadi; Fuzy Yustika Manik; Seniman Seniman; T. Henny Febriana Harumy; Ivan Jaya; Yeni Absah; Muhammad Romi Syahputra; Liana Dwi Sri Hastuti; Pauzi Ibrahim Nainggolan; Tulus Joseph Herianto; Dewi Sartika Br Ginting; Yudhistira Yudhistira
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 5 No. 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5154

Abstract

 Abstract: Kuta Mbelin Village in STM Hulu District, Deli Serdang Regency, utilizes digital technology to accelerate sustainable development through its official village website, kutambelin.com. This website plays a crucial role in promoting the village's potential, particularly in the agriculture and nature tourism sectors, which support the achievement of SDG 8 (Decent Work and Economic Growth). Furthermore, kutambelin.com highlights sustainable environmental management programs relevant to SDG 12 (Responsible Consumption and Production), and supports digital infrastructure innovation related to SDG 9 (Innovation, Infrastructure, and Industrialization). Keywords: Kuta Mbelin Village, digital innovation, sustainable development, SDGs, village website, local promotion, agriculture, tourism. Abstrak: Desa Kuta Mbelin di Kecamatan STM Hulu, Kabupaten Deli Serdang, memanfaatkan teknologi digital untuk mempercepat pembangunan berkelanjutan melalui situs web resmi desa, kutambelin.com. Website ini memainkan peran penting dalam mempromosikan potensi desa, khususnya di sektor pertanian dan pariwisata alam, yang mendukung pencapaian SDG 8 (Pekerjaan Layak dan Pertumbuhan Ekonomi). Selain itu, kutambelin.com menyoroti program pengelolaan lingkungan yang berkelanjutan, relevan dengan SDG 12 (Konsumsi dan Produksi yang Bertanggung Jawab), serta mendukung inovasi infrastruktur digital yang terkait dengan SDG 9 (Inovasi, Infrastruktur, dan Industrialisasi). Kata Kunci : Desa Kuta Mbelin, inovasi digital, pembangunan berkelanjutan, SDGs, website desa, promosi lokal, pertanian, pariwisata.
OPTIMALISASI VALIDITAS KLASTERISASI IPM MELALUI PENERAPAN VARIASI DISTANCE MEASURE PADA ALGORITMA K-MEANS++ Sardo Pardingotan Sipayung; Syahril Efendi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.5420

Abstract

Abstract: The Human Development Index (HDI) is an important indicator for measuring the quality of regional development through the dimensions of health, education, and decent living standards. In North Sumatra Province, HDI achievements between districts/cities still show significant disparities, requiring a data-based analytical approach to map development patterns objectively. This study aims to optimize the validity of regional HDI clustering through the application of the K-Means++ algorithm with distance measure variations. This study uses a quantitative approach with an unsupervised learning method. The data analyzed includes HDI, Average Length of Schooling (ALS), and Adjusted Per Capita Expenditure sourced from the Central Statistics Agency. The research stages include data preprocessing and standardization, determining the optimal number of clusters using the Elbow method, applying the K-Means++ algorithm, and evaluating cluster quality using the Davies–Bouldin Index (DBI) and Purity Index. In addition, a comparison of clustering performance based on Euclidean, Manhattan, and Cosine distances was conducted. The results of the study show that the optimal number of clusters is three clusters representing high, medium, and low levels of human development. A DBI value of 0.60 and a Purity Index of 0.61 indicate good clustering quality. Euclidean and Manhattan distances produced the best performance compared to Cosine distance. Keyword: Human Development Index; K-Means++; Clustering; Distance Measure; Davies–Bouldin Index; Purity Index. Abstrak: Indeks Pembangunan Manusia (IPM) merupakan indikator penting untuk mengukur kualitas pembangunan wilayah melalui dimensi kesehatan, pendidikan, dan standar hidup layak. Di Provinsi Sumatera Utara, capaian IPM antar kabupaten/kota masih menunjukkan ketimpangan yang cukup signifikan, sehingga diperlukan pendekatan analitis berbasis data untuk memetakan pola pembangunan secara objektif. Penelitian ini bertujuan untuk mengoptimalkan validitas klasterisasi IPM wilayah melalui penerapan algoritma K-Means++ dengan variasi distance measure. Penelitian ini menggunakan pendekatan kuantitatif dengan metode unsupervised learning. Data yang dianalisis meliputi IPM, Rata Lama Sekolah (RLS), dan Pengeluaran per Kapita Disesuaikan yang bersumber dari Badan Pusat Statistik. Tahapan penelitian mencakup praproses dan standarisasi data, penentuan jumlah klaster optimal menggunakan metode Elbow, penerapan algoritma K-Means++, serta evaluasi kualitas klaster menggunakan Davies–Bouldin Index (DBI) dan Purity Index. Selain itu, dilakukan perbandingan kinerja klasterisasi berdasarkan Euclidean, Manhattan, dan Cosine distance. Hasil penelitian menunjukkan bahwa jumlah klaster optimal adalah tiga klaster yang merepresentasikan tingkat pembangunan manusia tinggi, menengah, dan rendah. Nilai DBI sebesar 0,60 dan Purity Index sebesar 0,61 menunjukkan kualitas klasterisasi yang baik. Euclidean dan Manhattan distance menghasilkan performa terbaik dibandingkan Cosine distance. Kata kunci: Indeks Pembangunan Manusia; K-Means++; Klasterisasi; Distance Measure; Davies–Bouldin Index; Purity Index.
A Climate Driven Decision Support System for Rice Management Using SPI-3 Prediction and Particle Swarm Optimization Eka Putra; Syahril Efendi; Poltak Sihombing; T. Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1408

Abstract

Climate variability and irregular rainfall patterns have become critical challenges affecting rice productivity, irrigation planning, and agricultural sustainability. Previous studies have primarily focused on rainfall forecasting or drought monitoring independently, with limited attention given to transforming climate predictions into actionable agricultural management strategies. This study addresses this gap by proposing an integrated climate-driven decision support framework that combines predictive drought-index modeling with optimization-based agronomic decision-making for adaptive rice field management. The proposed framework integrates satellite-based rainfall observations, seasonal climatic characteristics, and large-scale climate variability indicators to predict short-term moisture conditions represented by the three-month standardized precipitation index. The framework consists of three interconnected stages: climate prediction, optimization, and recommendation generation. In the prediction stage, a gradient boosting regression model enhanced with Bayesian hyperparameter optimization was employed to model nonlinear relationships among rainfall accumulation, lag rainfall patterns, seasonal cyclic features, and climate variability indicators. In the optimization stage, particle swarm optimization was applied to determine optimal fertilizer dosage, irrigation allocation, and harvest timing under varying climate conditions. Experimental procedures included comparative evaluations across multiple machine learning models, hyperparameter tuning strategies, and optimization iterations. The research figures and tables demonstrate the complete framework architecture, prediction performance comparisons, optimization convergence behavior, and adaptive rice management recommendations. Experimental results show that the proposed framework achieved strong predictive performance with a coefficient of determination of 0.851, a root mean square error of 0.391, and a mean absolute error of 0.322. Comparative analysis further confirmed that integrating climate variability indicators significantly improved predictive accuracy compared with baseline models using only historical rainfall information. The optimization process also demonstrated stable convergence toward climate-adaptive agronomic recommendations.
Deep Deterministic Policy Gradient for Simulation-Based Control of Water Quality in Nano Aquatic Systems Iwan Fitrianto Rahmad; Syahril Efendi; Poltak Sihombing; Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1447

Abstract

This study proposes a simulation-based Deep Deterministic Policy Gradient (DDPG) framework for water-quality control in nano aquatic systems. Nano tanks are highly sensitive to small disturbances because their limited water volume reduces buffering capacity and causes rapid changes in dissolved oxygen, ammonia, pH, temperature, biological oxygen demand, and chemical oxygen demand. To represent these coupled dynamics, a nonlinear simulation model adapted from the Continuously Stirred Tank Reactor concept is developed and implemented as an OpenAI Gym-compatible reinforcement learning environment. The DDPG agent learns continuous control actions related to aeration, feeding, and filtration through repeated interaction with the simulated nano-tank environment. The proposed nonlinear CSTR-DDPG framework is evaluated against a linear-model DDPG baseline using RMSE, cumulative reward, and closed-loop control performance. Simulation results show that the nonlinear model reduced RMSE by 45.2% for dissolved oxygen, 64.0% for ammonia, and 61.3% for pH compared with the linear baseline. The DDPG agent also achieved a 41.7% higher cumulative reward under the same reward structure. These findings indicate that nonlinear simulation can provide a more informative training environment for DDPG-based water-quality control. However, the present study remains limited to simulation-based evaluation, and physical validation using calibrated sensors, actuators, communication-delay analysis, and real nano-tank experiments is required in future work.
A Fibonacci-Based Dynamic Neural Network Architecture for Imbalanced Classification of Higher Education Performance Muhammad Ardiansyah Sembiring; Syahril Efendi; Mohammad Andri Budiman; T.Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1459

Abstract

This study aims to address two problems in neural network design: the lack of a principled method for choosing hidden-layer neuron counts, and the reliability loss caused by severe class imbalance. To meet this objective, the study propose the Fibonacci Dynamic Neural Network, contributing, to our knowledge, the first architecture unifying number-theoretic hidden-layer sizing with imbalanced-learning correction, an unbiased selection property proven formally, and cross-domain validation. Conceptually, candidate hidden-layer widths are generated from the Fibonacci sequence: because consecutive terms grow proportionally toward the Golden Ratio, the resulting set spans a non-redundant, well-distributed range of capacities, unlike arbitrary sets from conventional methods. One candidate is drawn per layer via a discrete uniform distribution, giving every option equal selection probability, while class imbalance is corrected through synthetic minority oversampling applied only to training data. The architecture was evaluated experimentally on three benchmark datasets spanning higher education, credit card fraud, and charitable donation prediction, with imbalance ratios from about eighteen-to-one to nearly five hundred eighty-to-one, tested across five seeds against five baselines and an ablation study via the Wilcoxon signed-rank test. It achieved macro F1-scores of approximately 0.83, 0.94, and 0.81, improvements of sixteen, five, and sixteen percent over the strongest baseline, with the smallest training-test performance gap of any model tested; oversampling and Fibonacci spacing contributed most to these gains. These findings suggest number-theoretic structures offer a practical, reproducible basis for architecture design under class imbalance, useful for future work on automated range selection and for deploying reliable classifiers in imbalanced real-world settings.
Mathematical Modeling of Water Quality Dynamics in Aquaculture: A Foundation for IoT Integration and Machine Learning-Driven Predictive Analytics Muhammad Irfan Sarif; Syahril Efendi; Poltak Sihombing; Herman Mawengkang
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.819

Abstract

Effective water quality management is paramount for sustainable aquaculture, yet conventional methods often fall short in providing timely and predictive insights. This paper details the development and analysis of a comprehensive suite of mathematical models designed to simulate key water quality dynamics in aquaculture systems. These models encompass critical biogeochemical processes, including the nitrogen cycle (ammonia, nitrite, nitrate, organic nitrogen), phosphorus cycle, Dissolved Oxygen (DO) balance, and Biochemical Oxygen Demand (BOD). Simulation results derived from these models illustrate the temporal evolution of these critical parameters, demonstrating their capability to capture complex interactions and provide a mechanistic understanding of the aquatic environment. This foundational modeling approach offers a robust tool for quantitative analysis and prediction of system responses under various conditions. The core contribution of this work is the articulation of these mathematical models, which serve as a crucial foundation for advanced, data-driven aquaculture management. To enhance their practical utility, we propose a conceptual framework for integrating these models with Internet of Things (IoT) sensor networks. Real-time data acquisition via IoT can be essential for model parameterization, continuous calibration, and validation against operational conditions. Furthermore, this paper discusses how outputs from these validated mechanistic models can serve as robust inputs for Machine Learning (ML) algorithms. This synergy enables the development of sophisticated predictive analytics for critical events, such as forecasting water quality deterioration, and supports optimized, proactive management strategies. This research lays the theoretical and methodological groundwork for developing more precise and resilient decision support systems in aquaculture. By emphasizing the synergistic potential of combining foundational mathematical modeling with data science techniques like IoT and ML, this work aims to contribute to transforming aquaculture into a more productive, sustainable, and environmentally responsible industry. Future efforts should focus on empirical validation and the practical implementation of the proposed integrated framework.
Co-Authors Abi Rafdi Ade Candra Ahmad Rozy Ahmadi, Fauzan Nur Al Khowarizmi Aminuyati Andysah Putera Utama Siahaan Anton Abdulbasah Kamil Arjon Turnip Asrizal Asrizal Badawi, Afif Br Bangun, Desy Milbina Budi K. Hutasuhut Chairil Umri Dadang Priyanto Dewi Sartika Br Ginting Eka Putra Erna Budhiarti Erna Budhiarti Nababan Erna Budhiarti Nababan Ernawati Fahmi Fahmi Fajar Muhajir Fatma Sari Hutagalung Fauzan Nurahmadi Fauzan Nurahmadi Fauzi Amri Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Halim Maulana Hamzani, Fitri Rezky Harahap, Lailan Hariyati Lubis, Hariyati Harumy, T. Henny Febriana Hasibuan, Nisma Novita Hasugian , Paska Marto Hengki Tamando Sihotang Henny Febriana Harumy Herianto, Tulus Joseph Herimanto Herimanto Herman Mawengkang herman mawengkang Hotmaida Lestari Siregar Ichsanuddin Hakim Ignazio Ahmad Pasadana Iin Parlina Imanuel Zega Indah Purnama Sari Indra Edy Syahputra Irzal Sofyan Ivan Jaya Iwan Fitrianto Rahmad Jaya, Ivan Khowarizmi, Al- Lailan Harahap Liana Dwi Sri Hastuti Lidya Rosnita Lili Tanti Lubis, Fahrurrozi M Safii M. Isa Indrawan Mahyuddin K. M Nasution Manurung, Rodiyah Aini Mardiansyah, Heru Marischa Elveny, Marischa Maya Silvi Lydia Mesran, Mesran Mochamad Wahyudi Mohammad Andri Budiman Muhammad Ardiansyah Sembiring Muhammad Iqbal Muhammad Iqbal Muhammad Irfan Sarif Muhammad Riki Atsauri Muhammad Romi Syahputra Muhammad Rusdi dan Afritha Amelia - Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis, Muhammad Muliawan Firdaus Mulkan Azhari Naemah Mubarakah Nainggolan, Pauzi Ibrahim Nasution, Devi Maiya Sari Nugroho Syahputra Oktaviana Bangun Pahala Sirait Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Prayoga, Nanda Dimas Purwanto Purwanto Rahmad Syah Rika Permata Sari Siregar Rizki Suwanda Saib Suwilo Santoso, Zikri Akmal Saraswati Yoga Andriyani Sardo Pardingotan Sipayung Sarif, Muhammad Irfan Sawaluddin Sawaluddin Sembiring, Rahmat W Seniman Seniman Seniman Seniman, Seniman Siagian, Deliyana Simamora, Windi Saputri Sipayung, Sardo Pardingotan Solly Aryza Sri Dwi Hastuti Sri Melvani Hardi Suherman, Suherman Suherman, Suherman Sutarman Sutarman Sutarman Sutarman Syah, Rahmad B. Y. Syahputra, Indra Edy Syahputra, Muhammad Romi Syahraini, Syahraini Syahriol Sitorus T. Henny Febriana Harumy T. Henny Febriana Harumy T.Henny Febriana Harumy Taufiqurrahman Taufiqurrahman Tulus Joseph Herianto Tulus Tulus Tulus Tulus Vinsensia, Desi Watts, Michael J. Weber, Gerhard Wilhelm yeni absah yeni absah Yudhistira Yudhistira Yudhistira Yudhistira Yudhistira Yudhistira Zakarias Situmorang Zuhri Ramadhan Zulkarnain Lubis