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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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Optimizing Text Correction For Voice Based IoT Smart Building Virtual Assistants Shidiqi, Maulana Ahmad As; Hadi, Mokh Sholihul; Wibawa, Aji Prasetya; Mhd. Irvan, Mhd. Irvan
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1085

The integration of Virtual Assistants (VAs) within Smart Building Internet of Things (IoT) ecosystems is increasingly critical, particularly for interpreting user commands via Automatic Speech Recognition (ASR). This paper presents an in-depth performance analysis of text correction algorithms on a Raspberry Pi 4—a cost-effective and widely used computing solution in smart building applications. Due to the absence of GPU acceleration for Python on ARM architecture, a specialized dataset was developed to benchmark algorithmic performance, focusing on correction times and accuracy. Our study utilized a near-real-world experimental setup, deploying Docker containers to simulate IoT MQTT brokers, a Smart Building Platform, and Rasa for dialogue management. Among the algorithms tested—Edit distance, Jaccard, FuzzPartialRatio, FuzzSortRatio, MLE, and Norvig Spell—the Edit distance and Norvig Spell emerged as leaders in accuracy, achieving an 84% success rate in text correction. Notably, the Edit distance algorithm demonstrated superior speed, vital for real-time processing demands. The Fuzz Sort Ratio algorithm distinguished itself with the fastest correction time at 31.6 milliseconds, albeit with a slight compromise on accuracy, attaining a 79% success rate. Consequently, the Edit distance algorithm is recommended for applications where accuracy and response time are paramount, while the Fuzz Sort Ratio is preferable for scenarios where speed is the overriding priority. This research paves the way for future exploration into the computational impacts of these algorithms and the exploration of neural network-based methods to further enhance text correction capabilities in smart building automation systems.

LSTM Model Using Adam’s Optimizer for Indonesian – Bugis Bidirectional Translation System Fajarwati, Erliana; Wibawa, Aji Prasetya; Hernandez, Leonel
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1272

The purpose of this research is to develop a machine translation of Bugis to Indonesian and vice versa in order to preserve the Bugis language. This research utilizes a recent dataset consisting of 30,000 Bugis-Indonesian sentence pairs from the online Bible. This research conducts scraping to compile the corpus which is then followed by manual and automatic pre-processing. The method chosen is Neural Machine Translation (NMT) while for training and testing models Long Short-Term Memory (LSTM) is used. The performance of the model is evaluated by Bilingual Evaluation Understudy (BLEU) score to measure the translation accuracy at various epochs. In addition, this study also compared the use of Adam's optimizer with non-optimizer. The results showed that the use of Adam's optimizer significantly improved the performance of the model where at epoch 2000 the model achieved the highest BLEU score of 0.996261 indicating highly accurate translation quality. In contrast, the model without the optimizer showed lower performance. Other results also found that the translation from Bugis to Indonesian was more accurate than from Indonesian to Bugis. This is due to the more balanced word count difference in the Bugis to Indonesian translation, which makes it easier for the model to match words. In conclusion, the use of NMT with Adam optimizer effectively improves the accuracy of two-way translation from Bugis-Indonesian.

Optimization of Nglegena Javanese Script Recognition With Machine Learning Based on Zoning And Normalization of Feature Extraction Graciello, Manuel Tanbica; Handayani, Anik Nur; Wibawa, Aji Prasetya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.256

Machine learning offers promising solutions for the recognition of handwritten Javanese Nglegena script, which is crucial for preserving Indonesia's cultural heritage. This study explores the application of several supervised learning algorithms-K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest-for classifying handwritten images of Nglegena Javanese script. Feature extraction is performed using a zoning technique, where each character image is divided into multiple zones (16, 25, 36, and 64) to capture local details. The extracted features are further processed using normalization methods, including Min-Max, Z-Score, and Binary normalization, to ensure uniform data distribution. The dataset, consisting of 600 images representing Javanese Nglegena characters, is split into training and testing sets using various ratios. Experimental results show that the combination of Naïve Bayes classification, 36-zone feature extraction, and Min-Max or Z-Score normalization achieves the highest accuracy of 65%. These findings demonstrate that optimizing zoning and normalization can significantly enhance the accuracy of machine learning models for Javanese script recognition. The research contributes to developing Optical Character Recognition (OCR) technology for Javanese script, supporting the digital preservation of Indonesia's historical and cultural heritage.

Letter Detection: An Empirical Comparative Study of Different ML Classifier and Feature Extraction Wibawa, Aji Prasetya; Putri, Nastiti Susetyo Fanany; Widiharso, Prasetya
Signal and Image Processing Letters Vol 5, No 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i1.45

Work and communication activities are inextricably linked. Letters are an example of a communication medium that is still widely utilized. When it comes to significant job, however, simply an official letter is required. Official and private letters must be distinguished and classified. Different feature extraction methods, such as the count-vectorizer and TF-IDF vectorizer, are employed to transmit the detection of this official and personal letter. To categorize letters by type, various machine learning (ML) techniques are employed. Nave Bayes, Support vector machine, and AdaBoost are the algorithms. The accuracy measurements used in this study include accuracy scores, F1-mean, recall, and precision. The best working algorithm is Naïve Bayes for two vectorizer methods used, with an accuracy value of 98%.

Few-Shot-BERT-RNN Narrative Structure Analysis for Andersen's Stories Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3932

Event Extraction (EE) is a pivotal task for NLP, where important events in the narrative text need to be detected and recognized. We present an alternative method for extracting events from Hans Christian Andersen's fairy tales, utilizing Few-Shot Learning with BERT (Bidirectional Encoder Representations from Transformers) and RNN (Recurrent Neural Network) in this paper. We selected Andersen's fairy tales because they are characterized by rich narratives and symbolic language, which also often prevents automatic event extraction. To reduce reliance on labeled samples, we utilize the Few-Shot Learning method, which enables the model to learn from a small number of labeled event examples trivially. The BERT model is used to generate deep representations by modeling the context between words and sentences. RNN is essential to capture the sequence of events in the story, which determines the structure of the narrative. The findings demonstrate that the proposed framework significantly improves event extraction, with high values of evaluation metrics such as in accuracy, precision, recall, and F1-score. The proposed method is also effective in extracting non-explicit events while keeping the narrative context. Despite the challenges posed by metaphorical language and subjective events, this work demonstrates that Few-Shot Learning, BERT, and RNNs offer a promising solution to the task of event extraction from complex narratives.

Restricted Boltzmann Machine Approach for Diagnosing Respiratory Diseases Haviluddin, -; Nurhalifah, Siti; Trahutomo, Dinnuhoni; Wibawa, Aji Prasetya; Utama, Agung Bella Putra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3427

Respiratory diseases remain a significant global health challenge, particularly in developing countries where high morbidity and mortality rates persist. This study aims to establish a diagnostic approach for respiratory diseases using the Restricted Boltzmann Machine (RBM) method to support early detection and improve clinical decision-making. The research utilizes 180 medical records from patients at I. A Moeis Samarinda Hospital, East Kalimantan, Indonesia, includes 22 symptom variables associated with six respiratory disease types: sinusitis, pharyngitis, bronchitis, pneumonia, tuberculosis, and asthma. The collected data were preprocessed into binary formats to represent symptomatic and asymptomatic conditions, facilitating practical training in the RBM model. Data splitting was conducted with 70:30, 80:20, and 90:10 ratios for training and testing sets. The RBM architecture was optimized to enhance model performance by tuning key parameters, including the number of epochs, learning rate, and hidden neurons. Experimental results demonstrate that the RBM model achieved high diagnostic accuracy, with an accuracy of 98%, sensitivity of 98%, and specificity of 99% under the configuration of 5000 epochs, a learning rate of 0.1, and 53 hidden neurons. These findings indicate the model’s capability to recognize patterns and accurately classify respiratory diseases based on clinical symptoms. The study highlights the potential of integrating AI-based diagnostic systems like RBM into healthcare services, particularly in resource-limited settings. Future research should explore larger, more diverse datasets and consider environmental and socioeconomic factors to improve the model’s generalizability and practical applicability.

Social informatics and CDIO: revolutionizing technological education Wibawa, Aji Prasetya; Nabila, Khurin; Utama, Agung Bella Putra; Purnomo, Purnomo; Dwiyanto, Felix Andika
International Journal of Education and Learning Vol 5, No 2: August 2023
Publisher : Association for Scientific Computing Electrical and Engineering(ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijele.v5i2.1175

Social informatics is an interdisciplinary area that examines how information and communication technologies (ICT) and the complex web of social and cultural contexts interact and change over time. This study not only helps with the design and use of ICT but also shows how these technologies significantly affect society and culture. It encourages new ideas, collaborations between different fields, and policymaking insights, which drives technological innovation and a better knowledge of how ICT affects society. The Conceive, Design, Implement, operate (CDIO) educational system stands out as a new and innovative teaching method. It emphasizes active learning and gives engineering students both technical and social skills. Its use in social informatics ushers in a new era of education that combines innovation and technology to help students become strong and independent. Future study on CDIO programs in social informatics education has the potential to augment the technical proficiency and social consciousness of graduates, thereby rendering them significant contributors to the field.

Multilingual Parallel Corpus for Indonesian Low-Resource Languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almu’iini; Arya Astawa, I Nyoman Gede; Andika Dwiyanto, Felix
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3412

Indonesia has an extraordinary number of languages, with more than 700 regional languages such as Javanese, Madurese, Balinese, Sundanese, and Bugis. Despite the wealth of languages, digital resources for these languages remain scarce, making the preservation and accessibility of digital languages a significant challenge. Research was conducted to address this gap by building a multilingual parallel corpus consisting of more than 150,000 phrase pairs extracted from Bible translations in five regional languages in Indonesia. Rigorous preprocessing, normalization, and Unicode tokenization were performed to improve data quality and consistency. The encoder-decoder architecture was a key focus in the development of the NMT model. Evaluation focused on forward and backward translation directions, which were measured using BLEU scores. The results show that forward translation consistently outperforms backward translation. The Indonesian Javanese model produced a score of 0.9939 for BLEU-1 and 0.9844 for BLEU-4, indicating a high level of translation quality. In contrast, reverse translation tasks, such as translating from Sundanese to Indonesian, presented significant challenges, with BLEU-4 scores as low as 0.3173. This illustrates the complexity of the translation system from Indonesian to local languages. If future research focuses on transformer-based models and incorporates additional linguistic parameters to enhance the accuracy of natural language processing (NLP) models for Indonesia's underrepresented regional languages, this work provides a dataset that can be utilized for that purpose.

Empowering Low-Resource Languages: Javanese Machine Translation Sulistyo, Danang Arbian; Aji Prasetya Wibawa; Wayan Firdaus Mahmudy; Fadhli Almu’iini Ahda; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6887

This study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese.

Urban Traffic Volume Prediction using LSTM and Bi-LSTM: Performance Evaluation on the Metro Interstate Dataset Pranolo, Andri; Saifullah, Shoffan; Putra, Agung Bella Utama; Dreżewski, Rafał; Wibawa, Aji Prasetya
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.v17i3.3001.227-240

Urban traffic forecasting underpins the mitigation of congestion, enhancement of road safety, and reduction of emissions in intelligent transportation systems. We benchmark Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models on the Metro Interstate Traffic Volume dataset under an identical preprocessing and training pipeline for a fair comparison. Using a 24-hour multivariate input window (temperature, rainfall, snowfall, cloud cover), LSTM delivers the best overall balance of accuracy and efficiency on the full test sequence (RMSE = 0.196, MAPE = 2.36%, R² = 0.480; 7,344 s training). Bi-LSTM achieves competitive short-window accuracy but underperforms on the full sequence (RMSE = 0.231, MAPE = 2.92%, R² = 0.280; 12,672 s training). We attribute the Bi-LSTM gap to prediction "flattening" over long horizons, i.e., over-smoothed peaks from bidirectional averaging, despite its slightly stronger short-segment fit. Compared with prior RNN/GRU/CNN baselines on the same data, LSTM improves variance explanation while remaining deployable for near-real-time use. We also examine seasonality (daily/weekly cycles), weather effects, and data imbalance (peak versus off-peak) as factors that shape model error. These results support LSTM as a practical default for city-scale forecasting and motivate future work with attention/Transformer encoders and richer exogenous signals (incidents, events). The findings inform policy by enabling proactive traffic management that can reduce delays, emissions, and crash risk through earlier, data-driven interventions.

Co-Authors A.N. Afandi Abd. Rasyid Syamsuri Abdur Rohman Achmad Fanany Onnilita Gaffar Adaby, Resnu Wahyu Ade Kurnia Ganesh Akbari 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 Agus Purnomo 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 Alfiansyah Putra Pertama Triono Ali, Martina Alifah Diantebes Aindra Amro, Manar Y Anak Agung Istri Sri Wiadnyani 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 Anggreani, Desi Anik Nur Handayani Anton Prafanto Anusua Ghosh Anusua Ghosh, Anusua Arbian Sulistyo, Danang Ardiansyah, Mohammad Iqbal Firman 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 Cholisah Erman Hasihi Chong , Wan Ni Chuttur, Mohammad Yasser Citra Suardi Citra, Hana Rachma Collante, Leonel Hernandez Daniar Wahyu Darwis, Herdianti Dedes, Khen Dedi Kuswandi Dedy Kuswandi Denis Eka Cahyani Denna Delawanti Chrisyarani, Denna Delawanti Desi Anggreani Devita, Riri Nada Dewandra, Aderyan Reynaldi Fahrezza Dewi, Popy Maulida Dhani Wahyu Wijaya Dhani Wahyu Wijaya Dhaniyar Dhaniyar 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 Esther Irawati Setiawan Fachrul Kurniawan Fachrul Kurniawan Fadhilah, Farhan Fadhilla, Akhmad Fanny Fadhli Almu’iini Ahda Faidzin, Ilham Fajar Purnama Fajarwati, Erliana Faller, Erwin Faradini Usha Setyaputri Farid Miftahuddin Farida Nur Kumala Fauzan Cahya Arifin Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdinand, Miftakhul Anggita Bima Ferina Ayu Pusparani Filby , Brilliant Filby, Brilliant Fitria, Nimas Dian Fitriana Kurniawati Gianika Roman Sosa Graciello, Manuel Tanbica Gülsün Kurubacak Gunawan Gunawan Gwinny Tirza Rarastri 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 Hartono, Nickolas Hary Suswanto Hasanuddin, Tasrif Hashim, Ummi Raba’ah Haviluddin Haviluddin Haviluddin, - Hendrawan, William Hartanto Herdianti Darwis Heri Pratikto Herman Herman 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 Jevri Tri Ardiansah Junoh, Ahmad Kadri Juwita Annisa Fauzi Juwita Annisa Fauzi Kaki, Gregorius Paulus Mario Laka Kasturi Kanchymalay, Kasturi Kelvin Wong Khafit Badrus Zaman Khoiruddin Asfanie Khurin Nabila Kirya Mateeke Moses 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. 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 Ming F. Teng Ming Foey Teng Ming Foey Teng, Ming Foey Mochamad Hariadi 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 Nabila Izdihar, Zahra Nabila, Khurin Nada, Anita Qotrun Nadhiroh, Baitun Nadia Roosmalita Sari Nadia Roosmalita Sari Nafalski, Andrew Nastiti Susetyo Fanany Putri Naufal, Ayyub Naziro Nedic, Zorica Ningsih, Eka Nurcahya Ningtyas, Yana Novia Ratnasari Noviani, Erina Fika Nugraha, Agil Zaidan Nur Cahyo Wibowo Nur Hidayatullah Nurfadila, Piska Dwi Nurhalifah, Siti Nuril Anwar, Nuril Nurroby Wahyu Saputra Nurul Falah Hashim Nurul Hidayat Nuryana, Zalik Oakley, Simon Okazaki Yasuhisa Oki Dwi Yuliana Omar, Saodah Osamu Fukuda Pakpahan, Herman Santoso Paramarta, Andien Khansa’a Iffat 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 Raden Mohamad Herdian Bhakti 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, Rr. Poppy Rully Charitas Indra Prahmana Ruth Ema Febrita Saifullah, Shoffan Salahuddin, Lizawati Salsabila, Reni Fatrisna Santoso, Priyo Aji Saputra, Irzan Tri Sari, Nadia Roosmalita 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 Shili, Hechmi Sias, Quota Alief Simbolon, Triyanti Sisca Rahmadonna Siti Helmyati Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Stamen Gadzhanov Subadra, ST. Ulfawanti Intan 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 Syaad Patmantara Syaad Patmanthara Syabani, Muhiban Tantri Hari Mukti Tasrif Hasanuddin 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 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 Yuliana, Oki Dwi Yuliawati, Pundhi Yuni Rahmawati Yusmanto, Yunan Zaeni, Ilham Ari Elbaith Zhou, Xiaofeng Zulkham Umar Rosyidin Zulkham Umar Rosyidin

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