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Aji Prasetya Wibawa

Univeristas Negeri Malang

Author-ID : 2489010

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 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Survey and Challenges: Event Extraction of Story Narrative in NLP Approach Erna Daniati; Aji Prasetya Wibawa; Wahyu Sakti Gunawan Irianto; Andrew Nafalski
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15534

Event extraction from story narratives remains a challenging yet underexplored area in natural language processing due to narrative complexity including implicit causality long-range dependencies and temporal ambiguity. This study addresses the research question: How have NLP and deep learning approaches been applied to extract events from story narratives and what gaps persist. Following the PRISMA 2020 guidelines we systematically reviewed 12 peer-reviewed studies published between 2017 and 2024. Our analysis reveals growing adoption of transformer-based models such as BERT alongside emerging architectures like DEEIA and PAIE which leverage prompt-based learning and event-specific contextual aggregation. Commonly used datasets include ROCStories and custom narrative corpora though few are standardized. Key challenges involve handling implicit events limited annotated data cross-domain generalization and integration of commonsense reasoning. The main contribution of this review is the first structured synthesis of event extraction techniques specifically for story narratives using a rigorous systematic methodology. We highlight the need for document-level modeling narrative-aware evaluation metrics and low-resource adaptation strategies. This work provides a foundation for future research aiming to bridge narrative understanding with robust event-centric NLP systems.

Language as the Semantic Bridge in Audio, Music, and Multimodal Artificial Intelligence: A Systematic Review (2021-2025) Novia Ratnasari; Aji Prasetya Wibawa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15564

This study presents a systematic review of research in Audio, Music, and Multimodal Artificial Intelligence published between 2021 and 2025, investigating how language operates as a semantic mediation layer between acoustic signals and high-level meaning. The research addresses the fragmentation of existing surveys by introducing a Domain; Modality; Technique; Task (D-M-T-T) taxonomy that systematically differentiates domain focus, modality configuration, modeling techniques, and task objectives. The research contribution is a structured analytical framework that offers a more granular perspective than architecture-centered surveys of Multimodal Large Language Models. Following the PRISMA 2020 protocol, 2,197 Scopus-indexed publications were screened, yielding 369 eligible studies. Language is defined as a representational layer encompassing natural language and structured symbolic encodings that connect acoustic embeddings to semantic interpretation and generative reasoning. Multimodal systems aligning audio and vision without explicit textual grounding are included and analyzed as non-linguistic alignment architectures within the taxonomy. The findings reveal a shift from recognition-based models toward unified multimodal systems in which language conditions alignment, reasoning, and generative synthesis. For instance, text-conditioned music generation demonstrates how linguistic prompts guide compositional structure and emotional expression. These developments reflect an epistemic transition from signal recognition paradigms to language-mediated generative intelligence. Emerging gaps include limited explainability in generative audio systems and insufficient low-resource cross-modal semantic grounding.

Optimizing Transformer Model FlanT5 for Multi-Question Answering with Context-Aware Learning Rate Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6985

This study investigates the performance of FlanT5-based transformer models in handling Multiple-Question Answering (M-QA) tasks, in which multiple semantically related questions must be addressed with a single cohesive answer. Unlike traditional QA systems that focus on one-to-one question-answer pairs, the M-QA approach challenges the model to understand contextual relationships across several questions tied to the same topic. A custom dataset was developed with shared context, grouped questions, and a unified answer to train and evaluate the model. The FlanT5 architecture was fine-tuned using different learning rates (0.0001, 0.0002, 0.0003) to explore the effect of training configurations on model performance. The evaluation was conducted using the ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum metrics. The results indicate that a learning rate of 0.0003 provides the optimal performance, achieving a ROUGE-Lsum score of 0.7390. This study confirms the capability of instruction-tuned transformers to manage complex summarization scenarios that require contextual coherence. The findings are relevant for real-world applications such as intelligent digital assistants, clinical decision support, and educational chatbots. Furthermore, this study emphasizes the importance of hyperparameter tuning in improving the effectiveness of question-driven summarization systems for scalable and efficient deployment.

Comparative Analysis of YOLOv8 Segmentation Variants for Indonesian Sign Language (SIBI) Recognition Desi Fatkhi Azizah; Anik Nur Handayani; Aji Prasetya Wibawa; Osamu Fukuda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7500

The Indonesian Sign Language System (SIBI) is the officially recognized communication medium for deaf communities in Indonesia, yet its limited public use continues to create barriers in education, healthcare, and public services. Automatic sign language recognition powered by artificial intelligence provides a promising pathway to reduce these inequities. This study presents a comprehensive comparative evaluation of YOLOv8 segmentation variants for SIBI recognition, aiming to identify models that stabilize accuracy and efficiency for real-time deployment. A mono-background dataset of SIBI alphabet gestures was annotated using instance segmentation, and five YOLOv8-seg models (n, s, m, l, x) were trained and tested across multiple data-split scenarios. Performance was assessed through precision, recall, F1-score, mAP50, mAP50–95, and inference time. Results show that YOLOv8m-seg consistently achieved the best trade-off (F1-score 0.972; mAP50 0.982), while YOLOv8n-seg delivered the fastest inference speed (5.163 ms), making it suitable for resource-constrained devices. Visualization further demonstrated the models’ ability to capture hand contours and distinguish gestures accurately. Beyond technical benchmarking, this research highlights the potential of YOLOv8-based SIBI recognition as an inclusive assistive technology for bridging communication gaps in schools and clinics where interpreters are often unavailable. It also identifies governance challenges, including privacy protection, misclassification risks, and equitable access, which must be addressed for actual adoption. The findings, therefore, provide not only a contribution to computer vision research but also practical guidance for policymakers and service providers, positioning SIBI recognition systems as socially embedded technologies aligned with the goals of disability inclusion and sustainable development.

SQL Logic Error Detection using Start End Mid Algorithm Ardiansyah, Jevri Tri; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Yasuhisa, Okazaki
Knowledge Engineering and Data Science
Publisher : citeus

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Database is an important part of a system and it stores data to be manipulated. SQL (Structured Query Language) is used for manipulating those data to extract information and make decision. There are two types of error which make SQL is challenging to learn, namely syntax error and logic error. Compiler can detect syntax error, but it does not show error warning while logical error occurred. It makes logic error more difficult to understand than syntax error. A web based SQL compiler with errors detection ability by using Start End Mid algorithm is then developed, To help database's user to learn SQL in practical implementation.

High Dimensional Data Clustering using Self-Organized Map Febrita, Ruth Ema; Mahmudy, Wayan Firdaus; Wibawa, Aji Prasetya
Knowledge Engineering and Data Science
Publisher : citeus

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As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

Generating Javanese Stopwords List using K-means Clustering Algorithm Wibawa, Aji Prasetya; Fithri, Hidayah Kariima; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew
Knowledge Engineering and Data Science
Publisher : citeus

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Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.

Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction Pranolo, Andri; Mao, Yingchi; Prasetya Wibawa, Aji; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
Publisher : citeus

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Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Akbari, Ade Kurnia Ganesh; Fadhilla, Akhmad Fanny; Triono, Alfiansyah Putra Pertama; Paramarta, Andien Khansa’a Iffat; Setyaputri, Faradini Usha; Hernandez, Leonel
Knowledge Engineering and Data Science
Publisher : citeus

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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

A Comparative Study of Machine Learning Models for Javanese Wuku Classification: Exploring SVM, Naïve Bayes, and CNN for Cultural Texts Sulistyo, Danang Arbian; Prasetya Wibawa, Aji; Prasetya, Didik Dwi; Ahda, Fadhli Almu'iini; Utama, Agung Bella Putra
Knowledge Engineering and Data Science
Publisher : citeus

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This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) under repeated stratified 5-fold cross-validation (10 repeats; 50 runs) to ensure robust estimates. CNN achieved the best performance with Accuracy = 0.92 ± [SD], Macro-F1 = 0.90 ± [SD], and AUC = 0.93 ± [SD], outperforming SVM (Accuracy: 0.87; F1-score: 0.84) and Naïve Bayes (Accuracy: 0.82; F1-score: 0.78). The results underscore CNN’s strong effectiveness for nuanced, context-rich text classification, offering a vital contribution to cultural heritage preservation and advancing Natural Language Processing (NLP) for under-resourced languages. From a knowledge-engineering perspective, predicted Wuku labels can serve as structured metadata to support computational indexing and retrieval of Wuku narratives in cultural information systems. Methodologically, our CNN is a lightweight, small-corpus design that uses tuned regularization (dropout/early stopping) and multi-scale convolution to capture culturally salient n-gram cues, rather than relying on a fixed default TextCNN configuration. Future work involves expanding the dataset and exploring advanced deep learning architectures.

Co-Authors A.N. Afandi Abd. Rasyid Syamsuri Abdullah Sholum Abdur Rohman Achmad Fanany Onnilita Gaffar Adaby, Resnu Wahyu Ade Kurnia Ganesh Akbari Adelia Desyana Eka Putri Adelia Desyana Eka Putri Adelia Khansa Ristiaputri Adelia Khansa Ristiaputri Adhelia Wida Khaidir Adhelia Wida Khaidir Adil Zakaria Aditya Wahyu Setiawan Adjie Rosyidin Adnan, Adam Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agustinus Noertjahyana Ahmad 'Ammar Musyaffa' Ahmad Munjin Nasih Ahmad Naim Che Pee Ahmad Taufiq Aindra, Alifah Diantebes Aji, Bayu Kuncoro Akbari, Ade Kurnia Ganesh Akhimullah Akmal Fattah Akhmad Fanny Fadhilla Akrom Tegar Khomeiny Alamsyah, David Satria Aldy Rahmat Yulianto Alfiansyah Putra Pertama Triono Ali, Martina Alifah Diantebes Aindra Amro, Manar Y Andien Khansa’a Iffat Paramarta Andika Dwiyanto, Felix Andini, Nurul Fajriah Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andri Pranolo Andriansyah, Muhammad Rizal Angeline, Grace Anik Nur Handayani Anton Prafanto Anusua Ghosh Anusua Ghosh, Anusua Ardha Ardhana Putra Agustavada Ardhana Putra Agustavada Ardiansyah, Jevri Tri Ardiansyah, Mohammad Iqbal Firman Arifin, Mohammad Nazir Aripriharta - Arya Tandy Hermawan Ashar, Muhammad Astuti, Wistiani Atmaja, I Made Ari Dwi Suta Atmaja, Nimas Hadi Ba, Abdoul Fatakhou Bagaskoro, Muhammad Cahyo Bahalwan, Lugas Anegah Baitun Nadhiroh Bambang Widi Pratolo Bella Putra Utama, Agung Betty Masruroh Bety Masruroh Bin Abdul Hadi, Abdul Razak Bin Haji Jait, Adam Cahyo Prayogo, Cahyo Cengiz, Korhan Che Pee, Ahmad Naim Chong , Wan Ni Chuttur, Mohammad Yasser Citra Suardi Citra, Hana Rachma Collante, Leonel Hernandez Dafa Fadhilah Hilmi Danang Arbian Sulistyo Daniar Wahyu David Satria Alamsyah Dedes, Khen Dedi Kuswandi Dedy Kuswandi Denis Eka Cahyani Denna Delawanti Chrisyarani, Denna Delawanti Desi Anggreani Desi Fatkhi Azizah Devi Dwi Purwanto Devita, Riri Nada Dewandra, Aderyan Reynaldi Fahrezza Dewi, Popy Maulida Dhani Wahyu Wijaya Dhani Wahyu Wijaya Dhaniyar Dhaniyar Dhia Rafifah Thifal Dhia Rafifah Thifal Didik Dwi Prasetya Didik Nurhadi Didik Suprayogo Dika Fikri L Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Drezewski, Rafał Dwi Jaelani, Mardian dwi yasa, arnelia Dwieb, Mohamed Dwiyanto, Felix Andika Dwiyanto, Felix Andika Dyah Lestari Edinar Valiant Hawali Eka Nurcahya Ningsih Elta Sonalitha Endah Setyo Wardani Erna Daniati Erna Daniati Esther Irawati Setiawan Fachrul Kurniawan Fachrul Kurniawan Fachrul Kurniawan Fadhilah, Farhan Fadhilla, Akhmad Fanny Fadhli Almu’iini Ahda Fadia Irsania Putri Faidzin, Ilham Fajar Purnama Fajarwati, Erliana Faller, Erwin Faradini Usha Setyaputri Farid Miftahuddin Farida Nur Kumala Fauzan Cahya Arifin Fauzan Prasetyo Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdinand, Miftakhul Anggita Bima Ferina Ayu Pusparani Fidyah Ajeng Wulandari Filby , Brilliant Filby, Brilliant Fithri, Hidayah Kariima Fitria, Nimas Dian Fitriana Kurniawati Gianika Roman Sosa Graciello, Manuel Tanbica Gülsün Kurubacak Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hammad, Jehad A. H. Hammad, Jehad A.H Hari Putranto Haris Anwar Syafrudie Harits Ar Rasyid Harits Ar Rosyid Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hartono, Nickolas Hary Suswanto Hasanuddin, Tasrif Hashim, Ummi Raba’ah Hasihi, Cholisah Erman Haviluddin Haviluddin Haviluddin, - Hechmi SHILI Hendrawan, William Hartanto Heri Pratikto Herman Herman Herman Santoso Pakpahan Herman Thuan To Saurik Heru Nurwarsito Heru Wahyu Herwanto Hery Widijanto Hidayah Kariima Fithri Hidayah, Laily Hidayatul Ma'rifah Hitipeuw, Emanuel Hong, Yeap Chi I Made Wirawan I Nyoman Gede Arya Astawa Idris Idris Ilham Mulya Putra Pradana Imansyah, Pranadya Bagus Imro’aturrozaniyah, Imro’aturrozaniyah Inggar Tri Agustin Mawarni Irsyada, Rahmat Islam, Noorul Islami, Pio Arfianova Fitrizky Islami, Pio Arfianova Fitrizky Islami, Pio Arfianova Fitrizky Ismail, Amelia Ritahani Istiqlal, Adib Izdihar, Zahra Nabila Jabari, Nida Jehad A. H. Hammad Jehad A.H. Hammad Jehad Hammad Jevri Tri Ardiansah Julfikar Mawansyah Junoh, Ahmad Kadri Juwita Annisa Fauzi Juwita Annisa Fauzi Kaki, Gregorius Paulus Mario Laka Kartika Candra Kirana Kasturi Kanchymalay, Kasturi Kelvin Wong Khafit Badrus Zaman Khoiruddin Asfanie Khurin Nabila Kirya Mateeke Moses Kohei Arai Kohei Arai Kurniawan, Fachrul Kurniawan, Novian Candra Kurniawati, Fitriana Kuswandi, Dedy Laily Hidayah Langlang Gumilar Lauretta, Giovanny Cyntia Lazuardi Noorca Rachmadi Leonel Hernandez Leonel Hernandez Leonel Hernandez, Leonel Lestari, Muqodimah Nur Lestari, Muqodimah Nur Lestari, Muqodimah Nur Liang, Yoeh Wen Lisa Ramadhani Harianti Lisa Ramadhani Harianti Ludovikus Boman Wadu Luther Latumakulita M Zainal Arifin, M Zainal M. Alfian Mizar M. Zainal Arifin Mairi, Vitrail Gloria Mansoor Abdul Hamid Mantony, Oslida Mao, Yingchi Marchena, Piedad Marida, Tyas Agung Cahyaning Marji Marji Markus Diantoro Masruroh, Bety Mazarina Devi Meiga Ayu Ariyanti Mhd. Irvan, Mhd. Irvan Mifta Dewayani Miftahul Qiki Winata Miladina Rizka Aziza Ming F. Teng Ming Foey Teng, Ming Foey Mochamad Hariadi Moh. Safii Moh. Zainul Falah Mohamad Rodhi Faiz Mokh Sholihul Hadi Moses, Kirya Mateeke Moses, Kirya Mateeke Moses, Kirya Mateeke Mudakir, Mudakir Muh. Aliyazid Mude Muhamad Arifin Muhammad Busthomi Arviansyah Muhammad Ferdyan Syach Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuadi Muhammad Nu’man Hakim Muhammad, Abdullahi Uwaisu Muladi Munir Munir Muntholib Muqodimah Nur Lestari Mursyit, Mohammad Musyaffa', Ahmad 'Ammar Nabila Izdihar, Zahra Nabila, Khurin Nada, Anita Qotrun Nadhiroh, Baitun Nadia Roosmalita Sari Nafalski, Andrew Nastiti Susetyo Fanany Putri Naufal, Ayyub Naziro Nedic, Zorica Ningsih, Eka Nurcahya Ningtyas, Yana Novia Ratnasari Noviani, Erina Fika Novrindah Alvi Hasanah Nugraha, Agil Zaidan Nur Eva Nur Hidayatullah Nurfadila, Piska Dwi Nurhalifah, Siti Nuril Anwar, Nuril Nurroby Wahyu Saputra Nurul Falah Hashim Nurul Hidayat Nuryana, Zalik Oakley, Simon Okazaki Yasuhisa Okazaki Yasuhisa, Okazaki Oki Dwi Yuliana Omar, Saodah Osamu Fukuda Paramarta, Andien Khansa’a Iffat Patrya, Sukma Paul Igunda Machumu Pio Arfianova Fitrizky Islami Praherdhiono, Hendy Prananda Anugrah Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Puji Santoso Puji Santoso Puji Santoso Punaji Setyosari Pundhi Yuliawati Pundhi Yuliawati Purnawansyah Purnawansyah Purnomo Purnomo Purnomo Purnomo Purwatiningsih, Ayu Putra Utama, Agung Bella Putra, Agung Bella Utama Putri Syarifa, Dhea Fanny Putri, Desy Pratiwi Ika Putri, Fadia Irsania Putri, Nastiti Susetyo Fanany Qonita, Adiba Rahiddin, Rahillda Nadhirah Norizzaty Rahmadhani, Nur Aini Syafrina Raja, Roesman Ridwan Ratnasari, Novia Rendy Yani Susanto Resty Wulanningrum Ridho, Faiz Mohammad Ridwan Shalahuddin Ridwan Shalahuddin Riri Nada Devita Rizal Kholif Nurrohman Rizqini, Fajriwati Qoyyum Roni Herdianto Rosmin, Norzanah RR. Poppy Puspitasari Rully Charitas Indra Prahmana Ruth Ema Febrita Saifullah, Shoffan Salahuddin, Lizawati Salsabila, Reni Fatrisna Santoso, Priyo Aji Saputra, Anggie Wahyu Saputra, Irzan Tri Sarni Suhaila Rahim Seno Isbiyantoro Setiawan, Ariyono Setyadi, Hario Jati Setyaputri, Faradini Usha Setyawan P. Sakti Shahrul, Azzhan Shalahuddin, Ridwan Shiddiqy, Jabar Ash Shidiqi, Maulana Ahmad As Sias, Quota Alief Simbolon, Triyanti Sisca Rahmadonna Siti Helmyati Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Rahmawati ST. Ulfawanti Intan Subadra Stamen Gadzhanov Sucahyo, Cornaldo Beliarding Sugiarto Cokrowibowo Sugiyanto - Suhiro Wongso Susilo Sujito Sujito Sularso Sularso, Sularso Sulistyo, Danang Arbian Sunu Jatmika, Sunu Supeno Mardi Susiki Nugroho, Supeno Mardi Supriadi Supriadi Supriyono Supriyono Suryani, Ani Wilujeng Susilo, Suhiro Wongso Suyono Suyono Suyono Suyono Suyono Syaad Patmantara Syaad Patmanthara Syabani, Muhiban Tantri Hari Mukti Trahutomo, Dinnuhoni Tri Andi, Tri Tri Kuncoro Tri Lathif Mardi Suryanto Tri Lathif Mardi Suryanto Tri Saputra, Irzan Tri Sutanti Tri Sutanti, Tri Triono, Alfiansyah Putra Pertama Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Tsukasa Hirashima Tuatul Mahfud Ummi Rabaah Hasyim Uriu, Wako Utama , Agung Bella Putra Utama, Agung Bella Putra Utomo Pujianto Vira Setia Ningrum Vira Setia Ningrum Voliansky, Roman Wadu, Ludovikus Boman Wahyu Arbianda Yudha Pratama Wahyu Nur Hidayat Wahyu Sakti Gunawan Irianto Wahyu Tri Handoko Wako Uriu Wardani, Endah Setyo Wayan Firdaus Mahmudy Wibowo, Danang Arengga Wibowo, Fauzy Satrio Wibowo, Nur Cahyo Widiharso, Prasetya Widiyanintyas, Triyanna Yandratama, Hengky Yasa, Arnelia Dwi Yingchi Mao Yongen Susman Yosi Kristian Yuhefizar Yuhefizar Yuliana, Oki Dwi Yulianto, Aldy Rahmat Yuliawati, Pundhi Yuni Rahmawati Yusmanto, Yunan Zaeni, Ilham Ari Elbaith Zakaria, Adil Zhou, Xiaofeng Zulkham Umar Rosyidin Zulkham Umar Rosyidin

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