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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 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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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)

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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.
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.
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.
ANALISIS TINGKAT KESADARAN HUKUM BERLALU LINTAS SEBAGAI PILAR KESELAMATAN DI JALAN RAYA (STUDI KASUS DI POLRES SUKOHARJO) Febriyanti, Ainnur; Suyahman
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 Nomor 02, Juni 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.46907

Abstract

This study aims to analyze the level of legal awareness among road users and the factors contributing to traffic violations in Sukoharjo Regency. Legal awareness is examined through four indicators proposed by Soerjono Soekanto, namely legal knowledge, legal understanding, legal attitude, and legal behavior. This research employs a qualitative approach using interviews, observations, and documentation, supported by an interactive data analysis model by Miles and Huberman. The findings show that although road users possess adequate knowledge and understanding of traffic regulations, such awareness is not fully reflected in their actual driving behavior. Violations remain prevalent, particularly not wearing helmets, running red lights, failing to use license plates, and using modified exhausts. These violations are influenced by low self-awareness, environmental and social factors, limited supervision, and insufficient enforcement facilities such as ETLE. Efforts to address these issues—such as education, socialization, law enforcement, infrastructure improvement, and routine police monitoring—have been implemented but have not yet produced optimal results. The study concludes that increasing legal awareness requires a comprehensive strategy combining consistent education, strengthened enforcement, and the development of a culture of traffic discipline.
Comparative Analysis of Trademark Class Identification Using IndoBERT and Multilingual BERT Ayun Hapsari; Suyahman Suyahman
Journal of Artificial Intelligence and Legal Technology Vol. 1 No. 1 (2025): August 2025
Publisher : Sah Publisher

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Abstract

The rapid growth of trademark registrations in Indonesia has increased the demand for efficient and accurate classification into the internationally recognized NICE system. Manual assignment of classes remains time-consuming and prone to human error, motivating the need for an automated approach. This study investigates the application of Transformer-based language models for trademark class identification based solely on product and service descriptions. Two models were evaluated: the Multilingual BERT (mBERT) and the monolingual IndoBERT, both fine-tuned for sequence classification across 45 NICE classes using 59,948 trademark entries collected from the Directorate General of Intellectual Property (DGIP) database. The research methodology encompassed data preprocessing, stratified train-test splitting (80:20), and tokenization with a maximum sequence length of 64 tokens. Both models were trained for two epochs using the AdamW optimizer, and evaluated with accuracy, precision, recall, F1-score, and per-class accuracy (one-vs-all). Experimental results reveal that IndoBERT significantly outperforms mBERT, achieving an overall accuracy, precision, recall, and F1-score of 0.90, compared to 0.85 for mBERT. IndoBERT demonstrated particularly robust performance in low-support classes, indicating its superior ability to capture domain-specific linguistic nuances in Indonesian trademark descriptions. The findings underscore the potential of monolingual Transformer models for automating trademark classification in national intellectual property systems. The integration of such models can accelerate trademark registration, reduce examiner workload, and enhance consistency in class assignment. These results contribute to advancing the deployment of AI in legal and administrative contexts, while providing a foundation for future work involving multimodal features and explainable AI for comprehensive trademark management solutions.
Semantic Analysis of Trademark Names Using Large Language Models Muhammad Adi Pratama; Suyahman Suyahman
Journal of Artificial Intelligence and Legal Technology Vol. 2 No. 1 (2026): February 2026
Publisher : Sah Publisher

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Abstract

This study introduces a novel framework for trademark similarity analysis that integrates large language models (LLMs) to assess lexical, phonetic, and semantic relationships between trademark names without reliance on large precompiled databases or retraining. The primary motivation is to address the growing need for efficient and transparent preliminary trademark screening, which is often constrained by the limitations of traditional rule-based or string-matching approaches. To achieve this, a web-based system was developed using the Gemini API, allowing users to input trademark pairs for automated analysis. The workflow includes text normalization, phonetic conversion, multi-dimensional similarity computation, and the generation of interpretative explanations for each pair. A test dataset of ten diverse trademark name pairs was designed to capture variations in lexical overlap, phonetic similarity, and semantic association. The system’s outputs were evaluated both in terms of processing efficiency and expert assessment. Quantitative results show that the system can process a pair in under a second on average, handling 3,229 tokens across ten pairs with minimal computational overhead. Qualitative evaluation by five trademark and intellectual property experts using a 5-point Likert scale yielded mean scores of 4.4 for relevance, 3.8 for explanation quality, and 4.2 for practical usefulness, confirming strong alignment between the LLM outputs and expert intuition. The novelty of this research lies in demonstrating that LLMs can provide not only accurate similarity assessments but also human-readable interpretative reasoning, bridging the gap between automation and expert judgment in trademark evaluation. This approach offers a transparent and scalable solution for early-stage brand screening, significantly reducing the reliance on extensive databases and manual effort. The findings indicate a clear potential for integration into industrial-scale trademark examination workflows, paving the way for future developments in batch processing, recommendation systems, and enhanced interpretability in AI-assisted intellectual property management.
Co-Authors Abdul Azis Abdurrahman Abdurrahman Agus Wantoro Ahmad Aufar Ribhi Ahmad Jurnaidi Wahidin Albadri Albadri Albadri, Albadri Anggiratih, Endang Antoni Pribadi Anwar Fauzi, Muhammad Ardy Wicaksono Ardy Wicaksono Ardy Wicaksono Ardy Wicaksono Arfiani Nur Khusna Arfiani Nur Khusna Ayun Hapsari Bagas Dwi Yulianto Baso Intang Sappaile 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 Edwar Ali Egi Dio Bagus Sudewo Enos Lolang Erlita Sulistiati Faruq, Muhamad Febriyanti, Ainnur Guna, Bucky Wibawa Karya Guna, Buky Wibawa Karya Hadi Jayusman Hadi Widodo Hersiyati Palayukan Heru Widoyo Heru Widoyo Ika Murtiningsih 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 Mustofa Mustofa Nimas Ratna Sari Normansyah Normansyah Oktaviana Dita Prasticha Pardede, Ranat Mulia Prasetyo, Deny Pujiyana Pujiyana Pujiyana Puspasari, Novela Putri Qaedi Zihni, Faris Hazmi Rahayu, Maria Helena Sri Rahmah, Andi Ranat Mulia Pardede Salsabelia Ainaning, Nariswari Samsinar Samsinar 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 Vinsensius Singgih Wahyu Putri Hasanah Waskito, Yohanes Saing Wicaksono, Ardy Wicaksono, Nicky Gilang Widiastuti, Rosalina Yani Widodo, Hadi Widoyo, Heru Yulaikha Mar'atullatifah Yulaikha Mar'atullatifah