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All Journal Jurnal Ilmu Komputer dan Informasi Civis: Jurnal Ilmiah Ilmu Sosial dan Pendidikan JPK (Jurnal Pancasila dan Kewarganegaraan) Indonesian Journal of Artificial Intelligence and Data Mining Pendas : Jurnah Ilmiah Pendidikan Dasar Martabe : Jurnal Pengabdian Kepada Masyarakat Jambura Law Review Academy of Education Journal PKn Progresif : Jurnal Pemikiran dan Penelitian Kewarganegaraan Community Empowerment Jurnal Pendidikan Jurnal Pendidikan PKn Educate: Journal of Community Service in Education Innovative: Journal Of Social Science Research Jurnal Pendidikan Kewarganegaraan dan Politik Proceeding of International Conference on Humanity Education and Society Scientific Journal of Informatics International Journal of Mechanical, Industrial and Control Systems Engineering Al-Mabsut: Jurnal Studi Islam dan Sosial JUKEMAS : Jurnal Pengabdian Kepada Masyarakat Green Engineering: Journal of Engineering and Applied Science International Journal on Educational Issues (IJEI) Edumaspul: Jurnal Pendidikan Global Science: Journal of Information Technology and Computer Science Bhineka Tunggal Ika: Kajian Teori dan Praktik Pendidikan PKn Journal of Artificial Intelligence and Legal Technology Jurnal Pengabdian IPTEK
Suyahman Suyahman
Universitas Sugeng Hartono
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Nurturing Youth Character Education Through the “Sungkem Pertiwi” Cultural Performance Sawitri; Pujiyana; Suyahman
International Journal on Education Issues Vol. 2 No. 1 (2026): JANUARY
Publisher : CV Kalimasada Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59966/y69wa495

Abstract

Purpose – This study aims to analyze the influence of cultural arts performances on the character education of the younger generation. Design/Methodology/Approach – The research employs a qualitative approach, focusing on performances by various art groups, including karawitan, Javanese poetry (gurit jawa), Indonesian poetry, wayang padat (concise puppet shows), and dance. Data were gathered from primary and secondary sources through in-depth interviews, literature reviews, photographic documentation, and observations of digital content on platforms such as YouTube and TikTok. Findings – The results indicate that cultural arts performances have a positive and significant impact on character education. Through performance themes such as "Sungkem Pertiwi," these artistic showcases serve as a platform for the nation's youth to contribute while instilling moral values, such as respect for parents and elders, and the philosophy of "humanizing human beings" (memanusiakan manusia). The sequence of artistic works cohesively provides character lessons encompassing aspects of tolerance, responsibility, and cooperation. Originality/Value –This research offers a unique contribution by examining the intersection of cultural art performances and character education, focusing on specific art forms such as karawitan, Javanese poetry, wayang padat, and traditional dance, which are often overlooked in contemporary educational discourse.
ACTUALIZATION OF GLOBAL CITIZENSHIP CONCEPT IN THE PERSPECTIVE OF CIVIC EDUCATION IN INDONESIA; CASE STUDY OF CONSTITUTIONAL MATERIAL Suyahman
Bhineka Tunggal Ika Kajian Teori dan Praktik Pendidikan PKN
Publisher : Universitas Sriwijaya in Collaboration with AP3Kni (Asosiasi Profesi Pendidikan Pancasila dan Kewarganegaraan Indonesia/Indonesia Association Profession of Pancasila and Civic Education)

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

Abstract

Penelitian ini bertujuan untuk mendeskripsikan aktualisasi konsep warga negara global dalam perspektif Pendidikan kewarganegaraan di Indonesia. Jenis penelitian yang digunakan adalah deskriptif kualitatif. Subjek penelitian adalah dosen dan mahasiswa PKn, Metode pengumpulan data: observasi, wawancara dan dokumentasi. Validitasnya menggunakan triangulasi sumber dan metode. Analisis data menggunakan teknik analisis interaktif. Hasil observasi di lapangan ditemukan bahwa dosen ketika menyampaikan materi Pendidikan kewarganegaraan kurang mampu menyampaikannya secara menarik dan kurang mampu mengembangkan materi dengan fenomena yang terjadi saat ini sehingga daya tarik mahasiswa kurang maksimal. Dampaknya, ketika diadakan evaluasi hasilnya juga kurang maksimal. Dari hasil wawancara dengan dosen dan mahasiswa PKn diperoleh informasi bahwa dosen kurang menguasai materi, dosen kurang mampu menghubungkan materi ketatanegaraan dengan perkembangan global. Kesimpulannya: aktualisasi konsep warga negara global dalam perspektif Pendidikan kewarganegaraan di Indonesia mutlak diperlukan karena warga negara Indonesia merupakan bagian dari warga negara global. This research aims to describe the actualization of the concept of global citizenship from the perspective of citizenship education in Indonesia. The type of research used is descriptive qualitative. Research subjects were Civics lecturers and students. Data collection methods: observation, interviews and documentation. Its validity uses triangulation of sources and methods. Data analysis uses interactive analysis techniques. The results of observations in the field found that lecturers when delivering citizenship education material were less able to convey it in an interesting way and less able to develop the constitutional material with current phenomena so that student attraction was less than optimal. As a result, when an evaluation was carried out the results were also less than optimal. From the results of interviews with Civics lecturers and students, information was obtained that lecturers lacked mastery of the material, lecturers were less able to connect the material with global developments. In conclusion: actualizing the concept of global citizenship from the perspective of citizenship education in Indonesia is absolutely necessary because Indonesian citizens are part of global citizenship.
Traditional Batik Pattern Recognition with MobileNetV2 and Sampling-Based Hyperparameter Optimization Suyahman; Saut Parulian, Onesinus; Prasetyo, Deny; Anwar Fauzi, Muhammad
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1597

Abstract

Batik holds significant cultural value in Indonesia, reflecting the nation's historical and artistic heritage through its intricate patterns. Preserving these designs is essential for maintaining cultural identity and supporting artistic and economic communities. With the advancement of technology, deep learning has emerged as an effective approach for recognizing and classifying batik patterns. Convolutional Neural Networks (CNNs), particularly MobileNetV2, are widely recognized for their efficiency and accuracy in image classification. However, the performance of deep learning models is highly influenced by hyperparameter selection, which remains a challenging task. This study investigates the effectiveness of MobileNetV2 in classifying traditional Indonesian batik motifs, including Kawung, Mega Mendung, Parang, and Truntum, by applying different hyperparameter optimization methods such as Treestructured Parzen Estimator (TPE), Gaussian Process Sampler (GPS), Grid Search, and Random Search. The experimental results show that TPE achieved the best overall performance with a test accuracy of 91.94% and an F1 score of 92.09%. GPS and Grid Search obtained identical test accuracy of 90.83% with F1 scores of 90.89% and 90.87%, respectively, while Random Search produced the lowest performance with an accuracy of 88.61% and F1 score of 88.61%. These findings highlight the importance of structured hyperparameter optimization, particularly TPE, in enhancing the robustness of CNN-based batik classification. The results provide valuable insights for the development of automated batik pattern recognition systems that support cultural heritage preservation and related image classification applications.
Trust Centric Machine Learning Framework for Secure Decision Making in Decentralized Digital Service Ecosystems Deny Prasetyo; Siska Narulita; Ahmad Jurnaidi Wahidin; Rosalina Yani Widiastuti; Suyahman Suyahman; Very Dwi Setiawan; Agus Wantoro
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.197

Abstract

This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.
PENERAPAN PERANGKAT LUNAK PYTHON UNTUK MENINGKATKAN KOMPETENSI ANALISIS DATA DALAM KEGIATAN RISET MAHASISWA Dwi Setiawan, Very; Utari Iswavigra, Dwi; Ulfa, Mutia; Anggiratih, Endang; Dwi Yulianto, Bagas; Praningki, Tutus; Suyahman, Suyahman; Wicaksono, Ardy; Mar'atullatifah, Yulaikha; Prasetyo, Deny; Mursalim, Mursalim
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 9, No 2 (2026): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v9i2.%p

Abstract

Perkembangan teknologi informasi menuntut mahasiswa memiliki kompetensi analisis data yang memadai untuk mendukung kegiatan riset akademik. Namun, kenyataannya masih banyak mahasiswa yang mengalami keterbatasan dalam pemanfaatan perangkat lunak analisis data berbasis komputasi dan cenderung bergantung pada aplikasi spreadsheet sederhana. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi analisis data mahasiswa melalui penerapan perangkat lunak Python dalam kegiatan riset. Pelatihan dilaksanakan di Universitas Islam Batik Surakarta melalui kolaborasi antara Program Studi Teknik Industri Universitas Batik Surakarta dan Program Studi Teknik Industri Universitas Nahdlatul Ulama Jepara. Metode yang digunakan adalah pelatihan berbasis praktik langsung (hands-on training) yang meliputi pengenalan dasar pemrograman Python, pengolahan dan preprocessing data, serta visualisasi data penelitian menggunakan pustaka Pandas, NumPy, Matplotlib, dan Seaborn. Evaluasi kegiatan dilakukan melalui pre-test dan post-test untuk mengukur peningkatan kompetensi peserta. Hasil evaluasi menunjukkan peningkatan yang signifikan pada seluruh aspek kompetensi, termasuk pemahaman konsep dasar Python, kemampuan pengolahan dan pembersihan data, keterampilan visualisasi data, serta pemanfaatan Python dalam penyusunan laporan penelitian. Peningkatan nilai post-test yang lebih tinggi dibandingkan pre-test mengindikasikan bahwa pendekatan pelatihan yang diterapkan efektif dalam meningkatkan literasi komputasional dan kualitas analisis data mahasiswa. Kegiatan ini berkontribusi positif terhadap peningkatan mutu riset mahasiswa serta mendorong pemanfaatan perangkat lunak open-source dalam lingkungan akademik. Pelatihan ini juga berpotensi menjadi model Pengabdian kepada Masyarakat yang berkelanjutan dalam pengembangan kompetensi analisis data di perguruan tinggi.
Carbon Neutral Industrial Process Optimization through Hybrid Machine Learning and Real Time Energy Efficiency Monitoring Framework Suyahman Suyahman; Ardy Wicaksono; Dwi Utari Iswavigra; Yogiek Indra Kurniawan; Very Dwi Setiawan; Dedi Setiadi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.285

Abstract

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.
Edge Computing Enabled Real Time Anomaly Detection Framework for Secure Industrial Cyber Physical Systems Using Lightweight Deep Neural Networks Deny Prasetyo; Suyahman Suyahman; Rosalina Yani Widiastuti; Mursalim Mursalim; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.399

Abstract

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery Suyahman Suyahman; Deny Prasetyo; Ahmad Budi Trisnawan; Ardy Wicaksono; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.402

Abstract

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.
Pengabdian Masyarakat Karawitan Jawa bagi Ibu–ibu di Desa Kalongan, Karangannyar Sawitri, Sawitri; Pujiyana, Pujiyana; Suyahman, Suyahman; Rahayu, Maria Helena Sri
JUKEMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 4 (2025): JUKEMAS: Jurnal Pengabdian Kepada Masyarakat, Desember 2025
Publisher : Lumbung Pare Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60126/jukemas.v2i4.1297

Abstract

Kegiatan pengabdian masyarakat ini bertujuan meningkatkan keterampilan karawitan ibu-ibu di Desa Kalongan, Karanganyar melalui pelatihan karawitan berbasis pendampingan intensif. Kelompok karawitan putri di desa tersebut telah membentuk organisasi sebagai sarana silaturahmi dan pelestarian budaya, namun belum memiliki pelatih profesional sehingga perkembangan kemampuan memainkan gending belum maksimal. Peserta pelatihan berjumlah 20 ibu-ibu yang berdomisili di sekitar desa. Pelatihan dilaksanakan pada 4 Oktober 2025 dan diawali dengan penabuhan bersama untuk memetakan kemampuan awal. Hasil pre-test menunjukkan bahwa 75% peserta belum mampu memainkan gending Ibu Pertiwi, Mari Kangen, Gugur Gunung, dan Eka Prasetya Pancakarsa, sedangkan 25% lainnya sudah mampu. Setelah pelatihan dan pendampingan, hasil post-test menunjukkan peningkatan kemampuan signifikan, yaitu 80% peserta mampu memainkan gending dengan baik dan hanya 20% yang masih membutuhkan pendampingan. Temuan ini membuktikan bahwa pendampingan profesional efektif dalam meningkatkan keterampilan karawitan dan menjadi upaya strategis dalam pelestarian budaya lokal.
MELESTARIKAN BUDAYA TARI KEPADA MASYARAKAT DALAM PERINGATAN HARI TARI DUNIA MELALUI EVENT SOLO MENARI 2025: PRESPEKTIF PENDIDIKAN KARAKTER. Salsabelia Ainaning, Nariswari; Suyahman
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 2, Juni 2026 Publish
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.46688

Abstract

This study aims to determine the functions of the Solo Menari 2025 event and the challenges faced in introducing dance culture to the community while instilling character education values to celebrate World Dance Day. The research method applied is descriptive qualitative. The study was conducted in Surakarta at the Solo Menari 2025 event, with research subjects comprising participating dancers, event organizers, and the general public attending the event. Meanwhile, the research object focuses on the functions of Solo Menari 2025 in preserving dance culture and instilling character values, as well as the challenges encountered. Data collection techniques include interviews, literature analysis, and field observations. The research findings indicate that Solo Menari 2025 not only serves as a platform for preserving traditional dance arts but also successfully instills character values such as discipline, responsibility, self-confidence, tolerance, cooperation, patriotism, and orderliness. These values are internalized through practice sessions, performances, and social interactions between participants and the community. These results align with previous studies affirming that dance arts education can function as a comprehensive, relevant, and contextual means of character education. However, this study also identified several obstacles, including the event's frequency of only once a year, limited budget, transportation access issues, and competition from digital entertainment. Therefore, Solo Menari 2025 needs further development to become a sustainable medium for cultural preservation and character education. The implication of this study is the need to integrate dance arts into formal and non-formal education as an effort to preserve culture and build national character.
Co-Authors Agus Wantoro Ahmad Aufar Ribhi Ahmad Jurnaidi Wahidin Albadri Albadri Albadri, Albadri Amri, Miftachul Anggiratih, Endang Antoni Pribadi Anwar Fauzi, Muhammad Ardy Wicaksono Ardy Wicaksono Ardy Wicaksono Arfiani Nur Khusna Arfiani Nur Khusna Ayun Hapsari Bagas Dwi Yulianto Baso Intang Sappaile Bucky Wibawa Karya Guna Ciptandriyo, Petrus Andi Dalimawaty Kadir Darmiati, Made Dedi Setiadi Deny Prasetyo Deny Prasetyo Deny Prasetyo Deny Prasetyo Dewi Maharani Rachmaningsih Dwi Utari Iswavigra Dwi Utari Iswavigra Dwi Utari Iswavigra Egi Dio Bagus Sudewo Enos Lolang Faruq, Muhamad Febriyanti, Ainnur Gayatri, Elisabet Anita Guna, Bucky Wibawa Karya Hadi Widodo Hersiyati Palayukan Heru Widoyo Heru Widoyo Ika Murtiningsih Iswanto Iswanto Iswanto Iswanto Iswavigra, Dwi Utari Kadir, Dalimawaty Karimullah, Suud Sarim Kartini Marzuki Kurniawan, Yogiek Indra Made Darmiati Mahmudah, Himmatunnisak Mar'atullatifah, Yulaikha Marzuki, Kartini Mar’atullatifah, Yulaikha Miftachul Amri Mohammad Edy Nurtamam Mohammad Edy Nurtamam, Mohammad Edy Muh. Akbar Fhad Syahril Muhamad Furqon Muhamad Wisnu Pangestu Muhammad Adi Pratama Muhammad Rifzal Alief Ramadhan Murinto Murinto Murinto Mursalim Mursalim Mursalim Mursalim Murtiningsih, Ika Mustofa Mustofa Normansyah Normansyah Pardede, Ranat Mulia Prasetyo, Deny Prasticha, Oktaviana Dita Pujiyana Pujiyana Pujiyana Puspasari, Novela Putri Qaedi Zihni, Faris Hazmi Rahayu, Maria Helena Sri Rahmah, Andi Ranat Mulia Pardede Salsabelia Ainaning, Nariswari Santosa, Tomi Apra Saut Parulian, Onesinus Sawitri Sawitri Sawitri setiawan, very dwi Siska Narulita Siti Fatimah Stevano Wahyu Al'fandi Sumardi, Mar'ah Sholikhah Sunardi Sunardi Sunardi Sutarto Sutarto Sutarto Sutarto Taufik Iqbal Ramdhani Tomi Apra Santosa Tomi Apra Santosa Trisnawan, Ahmad Budi ulfa, mutia Utari Iswavigra, Dwi Very Dwi Setiawan Very Dwi Setiawan Vinsensius Singgih Wahyu Putri Hasanah Waskito, Yohanes Saing Wicaksono, Ardy Wicaksono, Nicky Gilang Widiastuti, Rosalina Yani Widodo, Hadi Widoyo, Heru Wijaya, Anastasya Putri Yulaikha Mar'atullatifah Yulaikha Mar'atullatifah