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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pendas : Jurnah Ilmiah Pendidikan Dasar Cetta: Jurnal Ilmu Pendidikan ILKOM Jurnal Ilmiah at-tamkin: Jurnal Pengabdian kepada Masyarakat SENTIA 2016 SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Edunesia : jurnal Ilmiah Pendidikan Discovery : Jurnal Ilmu Pengetahuan Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Decode: Jurnal Pendidikan Teknologi Informasi Emerging Information Science and Technology Bulletin of Community Engagement Journal of Education Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Reflection Journal JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Advance Sustainable Science, Engineering and Technology (ASSET) Systematic Literature Review Journal
Didik Dwi Prasetya
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Student Flowchart Automated Evaluation for Scalable Assessment in Introductory Programming Usman Nurhasan; Didik Dwi Prasetya
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13594

Abstract

This study evaluates the Automated Flowchart Assessment Tool (AFAT) to overcome limitations in semantic sensitivity and layout robustness prevalent in existing tools. Through a quantitative analysis of 312 student submissions, AFAT demonstrated superior diagnostic performance with a Micro-F1 score of 0.92 and substantial inter-rater agreement (Fleiss' Kappa = 0.88), supporting the hypothesis of expert-level accuracy. Key findings reveal that AFAT significantly enhances operational efficiency, reducing evaluation time by 61.2% (averaging 1.87 minutes per flowchart) while decreasing inter-rater variability by 28%. Generalized Linear Model (GLM) analysis confirmed significant time savings, particularly in high-complexity sessions (Wald χ² = 87.44, p < 0.001). Beyond technical efficiency, this research contributes to applied science education by providing a scalable framework for computational science literacy, enabling the rigorous assessment of algorithmic thinking within integrated STEM curricula. These results substantiate AFAT’s potential for large-scale deployment as a robust tool for automated scoring in formal educational settings
Comparative Study of Random Forest and Ordinal Regression in Concept Map Quality Assessment: The Role of TF-IDF, BERT, and SMOTE-based Balancing Rismayanti, Nurul; Prasetya, Didik Dwi; Widiyaningtyas, Triyanna; Hirashima, Tsukasa
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2906.336-345

Abstract

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics
Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping Fadhli Almu'iini Ahda; Aji Prasetya Wibawa; Didik Dwi Prasetya; Danang Arbian Sulistyo; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6511

Abstract

Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which differs according to language. Complex affixation patterns in Indonesian and regional languages such as Minangkabau pose considerable difficulties for traditional algorithms. This research adopts the enhanced fixed-stripping method to tackle these issues by integrating linguistic characteristics unique to Minangkabau. This study has three phases: data acquisition, pseudocode development, and algorithm execution. Testing revealed an average accuracy of 77.8%, indicating the algorithm's proficiency in managing Minangkabau’s intricate morphology. Nevertheless, constraints persist, particularly with irregular affixation patterns. Possible improvements could include adding more datasets, improving the rules for handling affixes, and using machine learning to make the system more flexible and accurate. This study emphasizes the significance of customized solutions for regional languages and provides insights into the advancement of NLP in various linguistic environments. The findings underscore the progress made in processing Minangkabau text while also emphasizing the need for further research to address current issues.
FTFPOS-IDF: A Fuzzy Rule-Based Thematic Term Weighting Scheme for Bloom's Taxonomy Question Classification Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2957

Abstract

The increasing adoption of Artificial Intelligence (AI) in education has created a growing demand for automated and reliable assessment systems. Existing Bloom’s Taxonomy (BT) question classification approaches commonly rely on TF-IDF-based weighting schemes, which assign static term weights and often fail to capture the varying thematic importance of terms across cognitive levels. To address this limitation, this study proposes a novel Fuzzy Thematic Feature and Part-of-Speech Inverse Document Frequency (FTFPOS-IDF) weighting scheme that integrates fuzzy rule-based reasoning with Natural Language Processing (NLP) to dynamically assign thematic weights according to Bloom’s Taxonomy relevance. The proposed framework combines Machine Learning (ML) and Deep Learning (DL) classifiers with Chi-Square feature selection to reduce irrelevant features and improve classification performance. Experimental results demonstrate that FTFPOS-IDF consistently outperforms conventional TF-IDF variants across multiple classification models. The highest performance was achieved by the Multilayer Perceptron (MLP) classifier with an accuracy of 86.7%. These findings indicate that fuzzy rule-based thematic weighting can effectively enhance Bloom’s Taxonomy question classification and support scalable, reliable, and sustainable digital assessment systems in educational environments.
Paradigma Epistemologis Kompresi Data Teks: Huffman, Arithmetic, dan Neural Language Model Luqman Affandi; Didik Dwi Prasetya; Syaad Patmanthara
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8384

Abstract

This study explores text data compression as an epistemological paradigm through a comparative analysis of three fundamental approaches: traditional methods (Huffman Coding + LZW), bit-based methods (Arithmetic Coding), and machine learning approaches (Neural Language Models). Using the Project Gutenberg dataset comprising 15,000 classical literary works with a total size of 8.5 GB and 2.1-billion-word tokens, the evaluation is conducted based on compression ratio, execution time, and memory usage. The results reveal fundamental trade-offs among the paradigms. Traditional methods achieve the fastest execution (8.3 seconds/GB, 482 MB/s, 52 MB) with a compression ratio of 3.2:1. Arithmetic coding attains near-optimal performance (99.5% of the Shannon bound) with a compression ratio of 3.8:1. Neural language models yield the highest compression ratio of 4.6:1 but require substantially higher execution time and memory. The epistemological analysis highlights distinct conceptions of information—mechanistic, mathematically optimal, and semantic-aware—and provides a conceptual framework for developing adaptive compression systems.
Optimized BiLSTM and GRU Models Using QHBM for Forex Price Prediction Febrianto Alqodri; Triyanna Widiyaningtyas; Didik Dwi Prasetya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6226

Abstract

The foreign exchange market is highly volatile and complex, making accurate price prediction challenging. This study aims to develop an optimized deep learning framework for predicting daily closing prices of seven major currency pairs (AUDUSD, EURUSD, GBPUSD, USDCAD, USDCHF, USDCNY, and USDJPY) by integrating Bidirectional Long Short-Term Memory (BiLSTM) and GatedRecurrent Unit (GRU) models with optimization strategies. Historical data from the Federal Reserve Economic Data were evaluated using Fixed Date Split and Walk Forward Validation (WFV), where WFV consistently achieved better performance than the fixed date. To enhance model performance, hyperparameter optimization was conducted using the Queen Honey Bee Migration (QHBM) algorithm, a metaheuristic approach inspired by the migration behavior of queen bees, divided into two characteristics: high learning rate and low learning rate. The optimized models achieved performance improvements of approximately 10-70% in MAPE and RMSE compared to the baseline models, while maintaining high R2 values. The results indicate that optimal configurations are pair-specific, wheremost currency pairs perform best with a high learning rate and high unit settings, while AUDUSD achieves superior performance with a low learning rate and low unit configuration. This study contributes a novel integration of WFV and QHBM-based optimization. Adaptive deep learning models with proper validation significantly improve forecasting accuracy, robustness, and generalization forfinancial decision-making and algorithmic trading applications.
Comparative Evaluation of Machine Learning Models for Heavy Crude Oil Viscosity Prediction Using Repeated Nested Cross-Validation and Independent Holdout Testing Enggie Hendrawan Saputra; Ilham Ari Elbaith Zaeni; Didik Dwi Prasetya; Azlan Mohd Zain; Welly Antonius; I Made Wirawan
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.455

Abstract

Introduction: Accurate prediction of heavy crude oil viscosity is important for reservoir engineering, production planning, and flow assurance because viscosity strongly affects fluid mobility and transport behavior. This study comparatively evaluates established machine learning models under a rigorous validation protocol rather than proposing a new predictive framework. Method: A published Middle Eastern heavy crude-oil dataset containing 196 development measurements and 47 independent holdout measurements was used. Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and the Beggs–Robinson correlation were evaluated using repeated nested cross-validation with five outer folds repeated twice and five inner folds. Preprocessing and hyperparameter selection were embedded within the validation pipeline, while the untouched holdout set was used only for final evaluation. Results and Discussion: Gradient Boosting achieved the best internal performance with R² = 0.99313 and RMSE = 11.41 cP. On the independent holdout set, it achieved R² = 0.99308, RMSE = 8.43 cP, MAE = 6.64 cP, and MAPE = 0.78%, outperforming Random Forest and Support Vector Regression. Residual diagnostics showed no detectable heteroscedasticity, while permutation importance identified temperature and C7+ as the dominant predictors. Conclusion: Gradient Boosting provides highly accurate viscosity predictions within the sampled domain; however, the absence of row-level oil identifiers and external reservoir data limits conclusions regarding oil-disjoint and field-level generalization.
Groundedness in Government Document Chatbots: A Systematic Literature Review and Metric Oriented Analysis Frendy Rumambi; Didik Dwi Prasetya; Triyanna Widiyaningtyas; Abdul Karim
Systematic Literature Review Journal Vol. 2 No. 1 (2026): January : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v2i1.277

Abstract

The application of Large Language Models (LLM) in public services encourages government agencies to adopt Retrieval Augmented Generation (RAG)-based chatbots as interfaces for regulatory knowledge and official documents. Although RAG is designed to increase the supportability of answers to authoritative sources, various studies show that this system is still vulnerable to hallucinations, which have the potential to reduce public trust and pose legal risks. This article presents a Systematic Literature Review (SLR) on the use of RAG in government chatbots with a focus on the definition, mitigation strategies, and evaluation of groundedness. The literature search was conducted in the period 2021–2025 through the SpringerLink, Scopus, and Taylor & Francis databases, resulting in 7,947 articles filtered using the PRISMA framework to obtain 100 articles Q1–Q2. Based on eight research questions, this study maps publication trends, document domains, RAG architecture, retrieval strategies, definitions of groundedness, and evaluation metrics used. The SLR results indicate conceptual fragmentation in the definition and measurement of groundedness, with the dominance of text-similarity-based metrics that are inadequate for regulatory contexts. As a conceptual contribution, this article formulates the Semantic Alignment Score (SAS) as a groundedness metric based on semantic alignment, evidence coverage, and entailment relationships, positioned to support the evaluation and auditing of government document chatbots.
Integrasi Embedding Multiformat untuk Representasi Semantik Big Data Smart City: Analisis Etika Penelitian, AI Ethics, dan Tantangan Publikasi Ilmiah di Era Teknologi Lanjut Sumanti, Endang Sri; Prasetya, Didik Dwi; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 6 No. 2 (2026): June
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/rj.v6i2.4193

Abstract

Penelitian ini bertujuan menganalisis manfaat representasi semantik dan memetakan risiko etika pada pipeline embedding multimodal dalam pengolahan Big Data Smart City. Penelitian menggunakan desain sintesis literatur yang dipadukan dengan studi kasus konseptual, analisis hermeneutik teknologi, dan pemetaan risiko berbasis pipeline. Analisis mencakup lima komponen utama, yaitu Text Encoder, Visual Encoder, Cross-Modal Alignment, Fusion Layer, dan Semantic Output Layer. Hasil penelitian menunjukkan bahwa integrasi data tekstual dan visual dapat memperkaya konteks semantik, memperkuat hubungan informasi antarmodalitas, serta mempertahankan konsistensi representasi pada dokumen kebijakan yang kompleks. Namun, manfaat tersebut disertai lima risiko utama, yaitu bias representasional, privasi dan indirect disclosure, dual-use, surveillance dan profiling otomatis, serta asimetri kekuasaan informasi. Fusion Layer teridentifikasi sebagai komponen dengan risiko paling tinggi karena menggabungkan bias dan potensi penyalahgunaan dari beberapa modalitas, sedangkan Cross-Modal Alignment menunjukkan mekanisme mitigasi yang masih terbatas. Integrasi prinsip autonomy, beneficence, justice, dan consent dengan fairness, accountability, transparency, explainability, serta manajemen risiko menghasilkan kerangka evaluasi etika yang dapat diterapkan sepanjang siklus hidup sistem. Penelitian merekomendasikan penerapan ethical checkpoints, audit bias, dokumentasi dataset dan model, logging, pembatasan tujuan penggunaan, serta human oversight. Karena berbasis studi kasus konseptual, kerangka ini masih memerlukan validasi ahli dan pengujian empiris menggunakan dataset Smart City aktual. Ethical Risk Mapping in Multimodal Embedding Pipelines for Semantic Representation of Smart City Big Data: A Literature Synthesis and Conceptual Case Study This study aims to analyze the benefits of semantic representation and map ethical risks within multimodal embedding pipelines used to process Smart City Big Data. The study employed a literature synthesis design combined with a conceptual case study, technological hermeneutic analysis, and pipeline-based risk mapping. The analysis covered five main components: the Text Encoder, Visual Encoder, Cross-Modal Alignment, Fusion Layer, and Semantic Output Layer. The findings indicate that integrating textual and visual data can enrich semantic context, strengthen cross-modal information relationships, and maintain representational consistency in complex policy documents. However, these benefits are accompanied by five major risks: representational bias, privacy and indirect disclosure, dual use, automated surveillance and profiling, and information-power asymmetry. The Fusion Layer was identified as the component with the highest risk because it combines bias and the potential misuse of information from multiple modalities, whereas Cross-Modal Alignment still has limited mitigation mechanisms. Integrating the principles of autonomy, beneficence, justice, and consent with fairness, accountability, transparency, explainability, and risk management produced an ethical evaluation framework that can be applied throughout the system lifecycle. The study recommends implementing ethical checkpoints, bias audits, dataset and model documentation, logging, purpose limitation, and human oversight. Because the study is based on a conceptual case scenario, the proposed framework still requires expert validation and empirical testing using actual Smart City datasets.
Comparative Evaluation of BM25–FAISS and Small-LLM–GPT in Retrieval-Augmented Generation Concept Map Assessment Maskur Maskur; Didik Dwi Prasetya; Triyanna Widiyaningtyas; Azlan Mohd Zain
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2594

Abstract

Concept map-based assessment is a practical approach to measure students’ conceptual understanding, but manual assessment still faces challenges such as subjectivity, inconsistency, and limited scalability. This study proposes the application of Retrieval-Augmented Generation (RAG) as an artificial intelligence-based automated assessment solution in an educational context. The objectives of this study are to compare the effectiveness of two retrieval methods, BM25 and FAISS, and to analyse the trade-off between large-scale generative models (GPT) and Small-LLM in assessing concept map propositions. This study uses a quantitative experimental approach by combining a retriever and a generator in the RAG system. Performance evaluation is carried out using the Macro-F1 and QWK metrics to measure agreement with expert judgment, and the Explanation Relevance Score (ERS) to assess explanation quality. The experimental results show that the FAISS–GPT combination achieves the best performance, with a Macro-F1 of 0.338 and a QWK of 0.146, slightly superior to the BM25–GPT combination. In contrast, the use of Small-LLM, both with BM25 and FAISS, showed lower performance with Macro-F1 values in the range of 0.167–0.221 and QWK close to zero. This finding confirms that semantic-based retrieval plays a vital role in improving the accuracy of automated assessment, while large-scale generative models are more effective in representing conceptual relationships in depth. This study contributes through a comparative analysis of retrievers and generators, and by introducing ERS as an additional metric for RAG-based automated assessment in the field of education.
Co-Authors Abdul Karim Abdul Wafi Abdullah Iskandar Syah Achmad Afif Irwansyah Adi Wahyu Wardani Ahmad Fajruddin Syauqi Ahmad Reza Adrian Ahmad Yusuf Setiawan Ainun Nur Baiti Aji P Wibawa Aji Prasetya Wibawa Akbar, Asna Isyarotul Andika Dwiyanto, Felix Andrew Nafalski Anik Nur Handayani Anjar Dwi Rahmawati Arifiati Fitri Anggraini Aripriharta - Aryo Pinandito Ashar, Muhammad Azhar Ahmad Smaragdina Azlan Mohd Zain Bagaskoro Biantoro, Yudhi Bintang Romadhon Cakir, Gulsun Kurubacak Danang Arbian Sulistyo Denis Eka Cahyani Dwi Widiyasari Dyah Ayu Langening Tyas Ella Amelia Widodo Enggie Hendrawan Saputra F.ti Ayyu Sayyidul Laily Fadhli Almu’iini Ahda Fadli Hidayat, M. Noer Fathan Alfariel Adhyaksa Fatrisna Salsabila, Reni Febrianto Alqodri Firdaus, Nabilah Zakiyah Salmaa Frendy Rumambi Gradiyanto Radityo Kusumo Hafid, Ahmad Hairani Hairani Hakkun Elmunsyah Hanif Rifai Adha Hanifah Muslimah Az-Zahra, Hanifah Muslimah Haq, Salsabila Thifal Nabil Hariyanto Hariyanto Hayashi, Yusuke Hirasama, Tsukasa Hirashima, Tsukasa I Made Wirawan I Nyoman Gede Arya Astawa Ilham Ari Elbaith Zaeni Intan Sulistyaningrum Sakkinah Jevri Tri Ardiansyah Kalifatullah, M. Ajie Khoirul Anwar KHOIRUL ANWAR Kusumo, Gradiyanto Radityo Laily, F.ti Ayyu Sayyidul Lalu Ganda Rady Putra Langlang Gumilar Lismi Animatul Chisbiyah Luqman Affandi Lutfi Budi Ilmawan, Lutfi Budi M. Ajie Kalifatullah Marsono Marsono Marsono Marsono Maskur Maskur Maskur Maskur Mayadi Mayadi Mega Oktaviana Moh. Nur Zamzami Moh. Zainul Falah Moh. Zulfiqar Naufal Maulana Muhammad Arief Nugroho Muhammad Aris Ichwanto muhammad hafiizh, muhammad Muhammad Jauharul Fuady Muhammad Mushawwir Muhammad Zaki Wiryawan Muhammad Zidni Ridlo Mukhamad Angga Gumilang Muladi Nadiah Alma Ratnaduhita Nadindra Dwi Ariyanta Nafalski, Andrew Nanscy Evi Wardani Natalina Wahyu Siswijayanti Nena Erviana Nunung Nurjanah Nur Hidayat, Wahyu Nuryakin, Mokhamad Perkasa, Gigih Prasetya, Luhur Adi Prasetyo, Muchamad Wahyu Prihandicha, Adiftya Bayu Putro, Maulana Nur Antoro Ratnaduhita, Nadiah Alma Reni Fatrisna Salsabila Reo Wicaksono Ridlo, Muhammad Zidni Rismayanti, Nurul Rofiudin, Amir Rumambi, Frendy Rocky Ryan Kurniawan Samodra, Joko Setiadi Cahyono Putro Setiawan, Ahmad Yusuf Setyani, Ida Agus Shafelbilyunazra, Alvalen Sigit Perdana Siti Sendari Sofiya Anggraini Sri Sumanti, Endang Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sulistyo, Danang Arbian Sumanti, Endang Sri Syaad Patmanthara Syaichul Fitrian Akbar Syamsul Arifin Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Tsukasa Hirashima Tsukasa Hirashima Tsukasa Hirashima Tuwoso Usman Nurhasan Usman Nurhasan Utama, Agung Bella Putra Utomo Pujianto Wahfi, Muhammad Fikri Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wahyudi, Erlik Prasetyo Wardani, Adi Wahyu Welly Antonius Wibawa, Aji P Wibisono Sukmo Wardhono, Wibisono Sukmo Widiyanti Widiyanti, Widiyanti Yana Andayani Yusril Imamuddin Zaeni, Ilham Ari Elbaith Zainul Falah, Moh.