Article Per Year (5 Year)
p-Index From 2021 - 2026
9.379
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Pendidikan Matematika Raflesia JURNAL PENDIDIKAN TAMBUSAI Journal on Education Jurnal Review Pendidikan dan Pengajaran (JRPP) JURNAL PEMBELAJARAN DAN MATEMATIKA SIGMA (JPMS) Community Development Journal: Jurnal Pengabdian Masyarakat Widya Accarya MetaBio : Jurnal Pendidikan Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Pengabdian kepada Masyarakat FARABI: Jurnal Matematika dan Pendidikan Matematika Jurnal Ekonomi International Journal of Humanities Education and Social Sciences Jurnal Pengabdian Mandiri Jurnal Pengabdian West Science Jurnal Psikologi dan Konseling West Science Journal of Information System, Technology and Engineering The Eastasouth Journal of Information System and Computer Science Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Artificial Intelligence and Development Journal on Economics, Management and Business Technology Jurnal Ekonomi, Manajemen, Akuntansi Journal of Moeslim Research Technik Research of Scientia Naturalis Sciences du Nord Economics and Business Journal of Economic Studies International Journal of Teaching and Learning (INJOTEL) Jurnal Pengabdian Masyarakat SAPANGAMBEI MANOKTOK HITEI Journal of Community Dedication
Joni Wilson Sitopu
Universitas Simalungun
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 70 Documents
Search

 3   4   5   6   7 
HUMAN–MACHINE INTERACTION IN ENGINEERING SYSTEMS: CONTROL, COGNITION, AND SYSTEM INTEGRATION Joni Wilson Sitopu; Darwan Edyanto Saragih; Haruto Takahashi
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3949

Abstract

Higher education institutions are increasingly expected to produce graduates who possess not only academic competence but also social responsibility, civic engagement, and the ability to address complex community challenges. Service learning has emerged as a transformative pedagogical approach that integrates academic learning with meaningful community service, enabling students to connect theoretical knowledge with real-world experiences. Growing emphasis on experiential and community-based learning has intensified interest in understanding the educational value and broader impact of service learning within higher education contexts. This study aims to examine the integration of service learning in higher education and evaluate its contribution to student learning outcomes, civic development, and community engagement. A qualitative research design based on systematic literature review and thematic analysis was employed. Data were collected from peer-reviewed journal articles, institutional reports, policy documents, and educational studies published between 2015 and 2025. Findings indicate that service learning significantly enhances critical thinking, problem-solving skills, communication abilities, social awareness, and civic responsibility among students. Meaningful collaboration between universities and community partners also contributes to reciprocal benefits, including community empowerment and the development of sustainable social initiatives. Institutional support, curriculum alignment, reflective learning practices, and stakeholder collaboration emerged as key factors influencing successful implementation. The study concludes that integrating service learning into higher education strengthens the connection between academic knowledge and social engagement, fostering holistic student development while promoting universities’ contributions to community well-being and sustainable societal development.
BEYOND DETERMINISTIC MODELS: PROBABILISTIC APPROACHES TO RISK-AWARE CIVIL ENGINEERING SYSTEMS Joni Wilson Sitopu; Virgo Erlando Purba; Dermina Roni Santika Damanik; Sarah Williams
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3625

Abstract

Civil engineering systems increasingly operate under conditions of uncertainty, variability, and exposure to extreme events, challenging the adequacy of deterministic modeling approaches that rely on fixed assumptions and simplified safety margins. Probabilistic methods offer a more realistic representation by explicitly incorporating uncertainty into analysis and decision-making processes. This study aims to develop a risk-aware probabilistic framework that enhances reliability assessment and supports more informed engineering decisions. A mixed-methods computational design was employed, integrating stochastic modeling, Monte Carlo simulation, Bayesian updating, and reliability analysis across representative infrastructure systems. Results indicate that probabilistic and hybrid models achieve higher reliability indices, lower probabilities of failure, and reduced expected losses compared to deterministic approaches. Statistical analysis confirms significant differences in performance, while case-based validation demonstrates strong agreement between probabilistic predictions and observed system behavior. Findings further reveal that adaptive integration of data-driven techniques improves model accuracy and responsiveness under dynamic conditions. This study concludes that probabilistic approaches provide a robust and scalable paradigm for risk-aware civil engineering, offering substantial implications for infrastructure design, maintenance, and resilience planning.
Evaluasi Implementasi Motor Listrik Berkecepatan Variabel pada Industri Manufaktur Anggun Angkasa Bela Persada; Fatmawati Sabur; Jeffrey Payung Langi; Sulastri Kakaly; Joni Wilson Sitopu; Rudy Max Damara Gugat
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 April - Juni
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.5946

Abstract

Motor listrik merupakan salah satu komponen utama yang banyak digunakan dalam berbagai proses produksi di sektor industri manufaktur dan berkontribusi besar terhadap konsumsi energi listrik. Tingginya kebutuhan energi pada sistem motor listrik mendorong perlunya penerapan teknologi yang mampu meningkatkan efisiensi penggunaan energi. Salah satu teknologi yang banyak dikembangkan adalah motor listrik berkecepatan variabel atau Variable Speed Drive (VSD) yang memungkinkan pengaturan kecepatan motor sesuai dengan kebutuhan beban kerja. Penelitian ini bertujuan untuk mengevaluasi implementasi motor listrik berkecepatan variabel pada industri manufaktur berdasarkan berbagai hasil penelitian yang telah dipublikasikan sebelumnya. Metode yang digunakan dalam penelitian ini adalah studi literatur, yaitu dengan mengkaji dan menganalisis berbagai sumber ilmiah yang relevan seperti artikel jurnal dan prosiding konferensi yang membahas tentang efisiensi energi, kinerja motor listrik, serta penerapan teknologi VSD dalam sistem industri. Proses penelitian dilakukan melalui beberapa tahapan yaitu identifikasi topik penelitian, pengumpulan literatur, seleksi sumber yang relevan, analisis dan sintesis temuan penelitian, serta penarikan kesimpulan. Hasil kajian literatur menunjukkan bahwa implementasi motor listrik berkecepatan variabel mampu meningkatkan efisiensi energi secara signifikan dibandingkan dengan sistem motor berkecepatan tetap, terutama pada aplikasi industri yang memiliki variasi beban seperti pompa, kipas, dan kompresor. Selain itu, penggunaan VSD juga memberikan manfaat lain seperti peningkatan stabilitas operasi sistem, pengurangan keausan mekanis pada peralatan, serta kontribusi dalam menurunkan emisi gas rumah kaca akibat penurunan konsumsi energi. Dengan demikian, penerapan motor listrik berkecepatan variabel dapat menjadi salah satu solusi strategis dalam mendukung efisiensi energi, peningkatan produktivitas, serta keberlanjutan operasional pada sektor industri manufaktur.
MATHEMATICAL MODELING AND STATISTICAL ANALYSIS OF DISEASE OUTBREAKS TO OPTIMIZE PUBLIC HEALTH INTERVENTION STRATEGIES IN URBAN ENVIRONMENTS Joni Wilson Sitopu; Aarav Sharma; Ethan Thompson
Research of Scientia Naturalis Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i2.4171

Abstract

Rapid urbanization, increasing population density, extensive human mobility, and interconnected healthcare systems have intensified the complexity of infectious disease transmission, creating challenges for timely outbreak detection and effective public health response. Conventional epidemiological approaches often struggle to represent dynamic transmission patterns and changing urban conditions, limiting intervention planning. This study evaluated the effectiveness of mathematical modeling and statistical analysis in predicting disease outbreaks and optimizing public health interventions in urban environments. A mixed-methods sequential explanatory design was employed using approximately 2.4 million anonymized surveillance records collected from 185 hospitals, 420 primary healthcare centers, and eight metropolitan surveillance systems over ten years. Quantitative analyses integrated compartmental epidemic models, Bayesian inference, spatial epidemiological analysis, time-series forecasting, multivariate statistics, structural equation modeling, hierarchical regression, mediation, and moderation analyses, while qualitative evidence was analyzed through thematic analysis. Findings showed that integrated mathematical and statistical models significantly improved outbreak prediction accuracy, intervention timing, healthcare preparedness, resource allocation, and response efficiency. Prediction accuracy enhanced intervention effectiveness, whereas surveillance integration and institutional coordination strengthened healthcare resilience, supporting evidence-based decision-making and adaptive public health governance during infectious disease outbreaks.
Model Prediksi Statistik untuk Mengukur Perilaku Konsumen dalam Transaksi Digital Joni Wilson Sitopu
Journal of Economic Studies Vol. 1 No. 2 (2025)
Publisher : Riset Anak Bangsa

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

Abstract

Transformasi digital dalam ekosistem perdagangan telah menghasilkan volume data konsumen yang masif, membuka peluang untuk memahami dan memprediksi perilaku konsumen dengan akurasi yang lebih tinggi. Penelitian ini bertujuan mengembangkan dan mengevaluasi model prediksi statistik untuk mengukur perilaku konsumen dalam transaksi digital, dengan fokus pada identifikasi faktor-faktor determinan yang mempengaruhi keputusan pembelian dan loyalitas konsumen. Penelitian menggunakan pendekatan kuantitatif dengan metode analisis regresi logistik dan decision tree untuk memodelkan probabilitas pembelian berdasarkan karakteristik demografis, perilaku browsing, dan riwayat transaksi dari 2.500 responden pengguna platform e-commerce di Indonesia. Data dikumpulkan melalui survei online dan analisis log aktivitas pengguna selama periode enam bulan. Hasil penelitian menunjukkan bahwa model regresi logistik mampu memprediksi probabilitas pembelian dengan akurasi 82,3 persen, sementara decision tree model mencapai akurasi 85,7 persen. Variabel yang paling signifikan dalam memprediksi perilaku pembelian adalah frekuensi kunjungan situs, durasi sesi browsing, jumlah produk yang dilihat, riwayat pembelian sebelumnya, dan interaksi dengan fitur rekomendasi produk. Temuan menunjukkan bahwa konsumen dengan lebih dari lima kunjungan per bulan memiliki probabilitas pembelian 4,2 kali lebih tinggi dibandingkan konsumen dengan kunjungan yang jarang. Model prediksi yang dikembangkan dapat digunakan oleh praktisi untuk personalisasi marketing, optimalisasi inventory, dan peningkatan customer experience dalam platform transaksi digital. Penelitian ini memberikan kontribusi metodologis dalam penerapan teknik statistik dan machine learning untuk analitik konsumen digital serta implikasi praktis untuk strategi pemasaran berbasis data.
Mathematical Biology: Modeling the Dynamics of Ecosystems and Biodiversity Khoironi Fanana Akbar; Daiki Nishida; Joni Wilson Sitopu
Research of Scientia Naturalis Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1586

Abstract

Background: Mathematical biology plays a crucial role in understanding the dynamics of ecosystems and biodiversity. By employing mathematical models, researchers can analyze complex biological interactions and predict changes within ecosystems over time. This approach is vital for addressing environmental challenges and informing conservation strategies. Objective: This study aims to develop mathematical models that accurately represent the dynamics of ecosystems and the factors influencing biodiversity. The focus is on identifying key interactions between species and their environment, as well as the implications of these interactions for ecosystem stability. Methodology: A combination of differential equations and computational simulations was employed to model various ecological scenarios. Data from field studies and ecological surveys were utilized to parameterize the models, allowing for realistic representations of species interactions and environmental influences. Results: Findings indicate that specific species interactions, such as predation and competition, significantly affect biodiversity and ecosystem dynamics. The models revealed thresholds beyond which ecosystems could shift to alternative stable states, emphasizing the importance of maintaining biodiversity for ecosystem resilience. Conclusion: This research highlights the value of mathematical modeling in the study of ecosystems and biodiversity. By providing insights into the intricate relationships between species and their environment, the study contributes to a better understanding of ecological dynamics and informs effective conservation strategies.
Analyzing the Impact of Packaging Design on Consumer Purchasing Decisions in the Cosmetics Industry Joni Wilson Sitopu; Adhy Firdaus
Journal on Economics, Management and Business Technology Vol. 3 No. 1 (2024): September: Economics, Management and Business Technology
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jembut.v3i1.220

Abstract

This study examines the influence of product packaging on consumer purchasing decisions in the cosmetics industry, aiming to understand how various packaging elements impact consumer behavior. Utilizing a mixed-methods approach, including quantitative surveys, qualitative focus groups, and observational studies, the research provides comprehensive insights into consumer preferences and the role of packaging in shaping purchasing decisions. Key findings indicate that aesthetic appeal, functional design, brand congruence, and sustainability are critical factors influencing consumer choices. Visually attractive and modern packaging designs significantly enhance purchase intent, while functional elements such as ease of use and convenience improve consumer satisfaction and loyalty. Consistent alignment between packaging and brand identity fosters trust and loyalty, and sustainability features appeal to eco-conscious consumers. Emotional engagement through packaging also plays a pivotal role in creating lasting consumer connections. The study's practical implications suggest that cosmetic companies should invest in innovative, functional, and sustainable packaging designs that align with their brand identity to attract and retain consumers.
QUANTUM COMPUTING APPLICATIONS IN SOLVING COMPLEX NONLINEAR EQUATIONS FOR ADVANCING COMPUTATIONAL FLUID DYNAMICS IN AEROSPACE ENGINEERING Joni Wilson Sitopu; Darwan Edyanto Saragih; Li Wei
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4237

Abstract

Increasing complexity in computational fluid dynamics (CFD) simulations demands faster, more accurate, and scalable approaches for solving highly nonlinear equations. Conventional high-performance computing remains effective in aerospace engineering but faces limitations in large-scale turbulence modeling, compressible flows, multiphysics interactions, and optimization requiring intensive iterations. This study evaluated the effectiveness of hybrid quantum-classical computing for solving complex nonlinear CFD equations and improving aerospace simulation performance. A mixed-methods sequential explanatory design involved 540 computational benchmark simulations and 240 experimental scenarios covering conventional solvers, hybrid quantum-classical optimization, and quantum-enhanced nonlinear solvers. Quantitative analyses used descriptive statistics, structural equation modeling, hierarchical regression, mediation, and moderation analysis, while qualitative evidence from expert interviews, computational observations, software evaluations, and document reviews underwent thematic analysis. Results showed that hybrid quantum-classical computing significantly improved convergence efficiency, numerical accuracy, turbulence prediction, scalability, residual error reduction, and simulation reliability. Hybrid optimization partially mediated the effect of quantum algorithms on computational efficiency, while mesh optimization strengthened convergence and engineering accuracy. These findings support integrating quantum computing with established CFD methods to enable faster optimization, stronger prediction, and scalable next-generation aerospace simulations.
Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications Desfita Eka Putri; Santi Prayudani; Joni Wilson Sitopu
Journal of Artificial Intelligence and Development Vol. 2 No. 01 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v4i1.702

Abstract

The integration of deep learning into cloud-based healthcare systems has opened new frontiers in medical image analysis, enabling faster, more accurate, and accessible diagnostics. However, the high computational demands of conventional deep learning models pose significant challenges for deployment in cloud environments, especially in latency-sensitive and resource-limited settings. This study aims to optimize deep learning algorithms to enhance their efficiency and scalability for medical image detection within cloud computing infrastructures. A quantitative research approach was employed, involving algorithmic optimization techniques such as pruning, quantization, transfer learning, and federated learning. The models were tested using benchmark medical image datasets and deployed in a simulated cloud environment to evaluate performance metrics such as accuracy, inference time, resource usage, and privacy compliance. Results showed that optimized models, particularly EfficientNet with pruning and quantization, achieved high diagnostic accuracy (up to 91.7%) while significantly reducing computational overhead. Federated learning proved effective in maintaining data privacy with minimal loss in accuracy. The findings suggest that lightweight, secure, and fast deep learning models can be realistically integrated into cloud-based healthcare applications. This study contributes a framework for efficient and scalable AI deployment in clinical settings, particularly in underserved or remote areas.
Information Technology Governance Research in the Digital Era: A Scopus-Based Bibliometric Analysis Loso Judijanto; Hanifah Nurul Muthmainah; Joni Wilson Sitopu
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 01 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v4i01.1206

Abstract

This study aims to map and analyze the global research landscape of information technology (IT) governance in the digital era through a bibliometric approach. Data were retrieved from the Scopus database and analyzed using VOSviewer software to examine co-authorship patterns, institutional collaborations, country contributions, keyword co-occurrence structures, and citation impact. The co-authorship network reveals that this segment of the literature is dominated by a single, tightly interconnected research team rather than a broad community of independent contributors, while institutional collaboration remains sparse and is bridged only by a public-administration-affiliated unit connecting a geospatial land-governance research community with a cross-border, financial-technology-oriented data-governance community. At the country level, China, the United States, the United Kingdom, India, and Indonesia emerge as the principal hubs of international collaboration, supported by extensive participation from Europe, the Middle East, and Southeast Asia, indicating that IT governance research constitutes a genuinely global, multipolar undertaking. The keyword co-occurrence analysis shows that the field is organized around four interconnected clusters covering cybersecurity and blockchain-based governance, artificial intelligence and digital transformation, big data-driven decision-making, and e-government and ICT-based public governance, with digital governance and information management forming the field's conceptual core. The citation analysis further shows that the field's intellectual foundation is anchored in the digital-era governance model and its subsequent extension toward artificial intelligence, alongside diverse contributions from e-government adoption, geospatial data integration, and data-driven policymaking research. Taken together, these findings indicate that IT governance research has developed into a rapidly expanding, technology-driven field, with growing convergence toward artificial intelligence, cybersecurity, and data governance as its emerging frontiers.
Co-Authors Aarav Sharma Aat Ruchiat Nugraha Abdul Wahab Syakhrani Achmad Zahruddin Ade Taufan Adhy Firdaus Ageng Satria Pamungkas Agus Faisal Asyha Agus Junaidi Agus Rofi’i Al Ikhlas Ali Ramatni Alim Hardiansyah Amin Harahap Andi Sahtiani Jahrir ANGGIL JUFINDA Anggun Angkasa Bela Persada Antoni Antoni Antonius Rino Vanchapo ARIEF BUDI PRATOMO arjang Aslan Asmina Herawaty Asril Nizar Ayu Wandira Azwar Anas Manurung Baso Intang Sappaile Baso Intang Sappaile Bayu Retno Brasie Pradana Sela Bunga Riska Ayu Cundra Bahar Daiki Nishida Darsi Darsi Darwan Edyanto Saragih Deardo Samuel Saragih Dermina Roni Santika Damanik Dermina Roni Santika Damanik Desfita Eka Putri Desrianti Sahida Destri Wahyuningsih Desty Endrawati Subroto Dewi Asriyati Dhika Elfrida Hulu Dian Perayanti Sinaga Dian Ratna Sari Didik Cahyono Dikwan Moeis Dina Mayadiana Suwarma Djuniawan Karna Djaja Dodo Tomi Ebbit Dermawan Purba Ediaman Sitepu Ellyas Palalas Elvina Safitri Emi Frihatini Emmy Hamidah Erna Kustyarini Ethan Thompson Fatmawati Sabur Fenny Mustika Piliang Fenny Mustika Piliang Firman Aziz Freddy Sibarani Gogor Christmass Setyawan Gugat, Rudy Max Damara Gusnidar Gusnidar Hanifah Nurul Muthmainah Haris Karyadi HARLEN SILALAHI Harniati Haruto Takahashi Heppy Sapulete Hersiyati Palayukan Heru Widoyo Hetty Elfina I Gede Sujana Ika Rosenta Puba Ika Rosenta Puba Ika Rosenta Purba Ika Rosenta Purba Imam Prawiranegara Gani Immanuelta Sitepu Imron Ramdhana Indra Satria Irajuana Haidar Irwan Lihardo Hulu Ismail Nasar Iwan Ridwan Janes Sinaga Jannus Parulian Sihombing Jeffrey Payung Langi Jenuri Juliana Rolinca Siburian Junita Kadek Ayu Kartika Septiana Kakaly, Sulastri Khairul Azhar Khoironi Fanana Akbar Komaru M.Komarul Huda Laila Hafni Li Wei Loria Wahyuni, Loria Loso Judijanto M Efrizal Lubis M Khoiri M. Komarul Huda M. Komarul Huda M.Komarul Huda M.Komarul Huda Machsunah, Yayuk Chayatun Majidah Khairani, Majidah Manu, Charisal B.S. Marisa Amelia Marlina Andriani Marlindoaman Saragih Marta Lumbanahor Mas'ud Muhammadiah Masli Nurcahya Zoraida Megi Afroka Melia Roza, Melia Mikhael Jibril Balo Mislan Sihite, Mislan Moh Solehuddin Moh. Solehuddin MS Viktor Purhanudin Muh.Reza Zulfikar Muhamad Risal Tawil Muhammad Arsyad Muhammad Fuad Muhammad Ihsan Dacholfany Muhammad Komarul Huda Muhammad Yudistira Arya Maulana Mukhtar Zaini Dahlan Mustofa Mustofa Napsin Napsin Navel Oktaviandy Mangelep Nelly Ervina Nofirman, Nofirman Nofri Yudi Arifin Novdin Manoktong Sianturi Nur Fajrin Maulana Yusuf Nur Fitriani Sahamony Nurkadarwati Nurkadarwati Okta Veza Ova Huzaefah Paulus Haniko Pitri, Nandia Rasimin Rasimin Rendi Hadian A. Tamagola Reynaldi David Bekham Manik Risjunardi Damanik Risjunardi Damanik Risnawati Risnawati Risqah Amaliah Kasman Rivaldi Samuel Roni Chandra Rosa Zulfikhar Rukhmana, Trisna Safruddin Safruddin Salome Rajagukguk Salome Rajagukguk Salome Rajagukguk Salome Rejagukguk Santi Prayudani Sarah Williams Sari, Mike Nurmalia Seriadi Situmorang Singgih Prastawa Siswadi Siswadi Sofyan Solissa, Everhard Markiano Sri Kadarsih Sri Suharti Suci Ananda Suhartini Salingkat Sumarni Tridelpina Purba Sumarni Tridelpina Purba Sumarny Tridelpina Sumarny Tridelpina Purba Sutrisno Sutrisno Teguh Setiawan Wibowo Tuahman Sipayung Ulung Napitu Unan Yusmaniar Oktiawati Unggul Sitorus Utomo Utomo Virgo Erlando Purba Virgo Erlando Purba Wanto, Anjar Widyatmoko Widyatmoko Windi Ariska Wiwid Suryono Yenny Anggreini Sarumaha Yogi Nurfauzi Yohanis Hukubun Yusmaita Fafriani Saragih