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Analisis Kritis terhadap Kebijakan dan Praktik Etika pada ChatGPT Purwanto, Devi Dwi; Wibawa, Aji Prasetya; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 5 No. 2 (2025): Desember
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/xv4mqp17

Abstract

Perkembangan pesat dalam teknologi Generative AI berbasis teks melalui model seperti ChatGPT membawa dampak yang signifikan di berbagai sektor. Namun, penerapan teknologi ini juga memunculkan tantangan etika yang mendalam, khususnya terkait dengan transparansi, keadilan, akuntabilitas, dan pengelolaan bias algoritmik. Artikel ini mengkaji secara kritis penerapan kebijakan etika dalam desain dan pengembangan ChatGPT, dengan fokus pada prinsip-prinsip etika fundamental seperti keadilan, non-diskriminasi, dan transparansi. Metode analisis yang digunakan dalam artikel ini adala Ex Post Facto, dimana analisis dilakukan setelah peristiwa atau kebijakan diterapkan, guna menilai dampaknya terhadap pengelolaan bias algoritmik dan risiko disinformasi. Pendekatan etika normative juga diterapkan untuk mengevaluasi sejau mana kebijakan diterapkan oleh OpenAI sejalan dengan nilai-nilai keadilan. Penelitian ini juga mengidentifikasi tantangan besar yang dihadapi oleh pengembang dalam memastikan bahwa Generative AI dapat digunakan secara adil dan bertanggung jawab. Selain itu, artikel ini memberikan rekomendasi untuk meningkatkan transparansi dan akuntabilitas dalam penggunaan AI, guna menciptakan ekosistem yang lebih inklusif dan dapat dipertanggungjawabkan. Keunikan artikel ini terletak pada analisis mendalam terhadap kebijakan etika yang diterapkan oleh OpenAI, serta focus pada prinsip-prinsip etika fundamental dalam konteks pengembangan Generative AI, yang belum banyak dibahas dalam penelitian sebelumnya. Kesimpulannya, keberhasilan AI yang etis bergantung pada penerapan kebijakan yang komprehensif dan sistematis, yang tidak hanya efisien secara teknis tetapi juga menghormati nilai-nilai sosial dan hak-hak individu. A critical analysis of Ethical Policies and Practices at ChatGPT The rapid advancement of text-based Generative AI technologies through models such as ChatGPT has had a significant impact across various sectors. However, the adoption of this technology also raises profound ethical challenges, particularly with regard to transparency, fairness, accountability, and the management of algorithmic bias. This article critically examines the implementation of ethical policies in the design and development of ChatGPT, with a focus on fundamental ethical principles such as fairness, non-discrimination, and transparency. The analytical method employed in this study is an ex post facto approach, in which analysis is conducted after the implementation of events or policies to assess their impact on the management of algorithmic bias and the risks of misinformation. A normative ethical approach is also applied to evaluate the extent to which OpenAI’s policies align with principles of justice. This study identifies major challenges faced by developers in ensuring that Generative AI is used fairly and responsibly. In addition, the article offers recommendations to enhance transparency and accountability in AI deployment in order to foster a more inclusive and accountable ecosystem. The novelty of this article lies in its in-depth analysis of the ethical policies implemented by OpenAI and its focus on fundamental ethical principles in the context of Generative AI development, an area that has received limited attention in prior research. In conclusion, the success of ethical AI depends on the implementation of comprehensive and systematic policies that are not only technically efficient but also respectful of social values and individual rights.
Optimizing Transformer Model FlanT5 for Multi-Question Answering with Context-Aware Learning Rate Suryanto, Tri Lathif Mardi; Wibawa, Aji Prasetya; Hariyono, Hariyono; Nafalski, Andrew
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

Abstract

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.
Survey and Challenges: Event Extraction of Story Narrative in NLP Approach Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Nafalski, Andrew
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

Abstract

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) Ratnasari, Novia; Wibawa, Aji Prasetya
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

Abstract

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.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin; Hasihi, Cholisah Erman; Teng, Ming Foey; Darwis, Herdianti
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
An Enhanced Mean Value Theorem with Bisection Technique to Elevate User Focus Metrics in Talent Finder Applications Arifin, M Zainal; Wibawa, Aji Prasetya; Safii, Moh; Noertjahyana, Agustinus; Che Pee, Ahmad Naim
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2822.140-149

Abstract

Contemporary digital workplaces face pervasive distractions (e.g., notifications, multitasking), yet talent-assessment systems rarely quantify their impact on attention. To address this gap, we integrate the classical Mean Value Theorem (MVT) with an adaptive bisection algorithm to model user-focus dynamics in talent-matching applications. MVT’s limit-based formulation captures continuous attentional shifts, while the iterative bisection method focus metrics by capturing dynamic attentional shifts through the mean toward optimal focus equilibrium, ensuring temporal continuity and rapid convergence. A controlled experiment involving Universitas Negeri Malang undergraduate students tested the Enhanced Mean Value Theorem–Bisection (EMVT-B) method in four simulated workplace scenarios. Participants selected Focus-oriented options over alternative strengths (Communication, Input, Relator, Adaptability) in approximately 65% of decisions, highlighting moderate yet improvable attentional commitment. Sensitivity analysis indicated that increasing the mean-shift threshold by 0.05 could raise Focus-oriented selections to 72%, emphasizing the method's practical impact. These findings establish EMVT-B as both a diagnostic and prescriptive tool, quantifying attentional stability while providing personalized strategies to enhance user focus. Future research should examine longitudinal applications and broader talent portfolios.
PENERAPAN METODE EXACT MATCH PADA APLIKASI ALQUR’AN DAN HADITS BERBASIS ANDROID Suardi, Citra; Wibawa, Aji Prasetya; Hasanuddin, Tasrif; Mude, Muhammad Aliyazid; Cokrowibowo, Sugiarto
ILKOM Jurnal Ilmiah Vol 11, No 3 (2019)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v11i3.438.184-190

Abstract

Exact Match is used to bring up sentences only on certain keywords. This study aims to apply the Exact Match method in the Al-qur'an and Hadith applications to find conformity between verses and hadiths. This research simplify for the people to increase their knowledge of the Qur'an and Hadith. This study uses interview techniques for some Muslims regarding the need to learn the quran and the hadith, observation technique is to do a comparison of several hadiths by asking for recommendations from the ustadz/ustazah, identify what are the shortcomings of existing hadith applications, and literature study techniques that are collecting data obtained from the Qur'an and the Hadith. The results of this study are (1) Created an application to match Hadiths that are in accordance the verse with the Exact Match method (2) Showing hadith related to the contents of the selected verse (3) Speed to produce a match in the Application of the Qur'an and Hadith depends on the internet network and the amount of data (4) The number of hadiths that appear is influenced by the number of available hadiths.
LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting Saputra, Anggie Wahyu; Wibawa, Aji Prasetya; Pujianto, Utomo; Putra Utama, Agung Bella; Nafalski, Andrew
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1106.57-62

Abstract

Forecasting is the process of predicting something in the future based on previous patterns. Forecasting will never be 100% accurate because the future has a problem of uncertainty. However, using the right method can make forecasting have a low error rate value to provide a good forecast for the future. This study aims to determine the effect of increasing the number of hidden layers and neurons on the performance of the long short-term memory (LSTM) forecasting method. LSTM performance measurement is done by root mean square error (RMSE) in various architectural scenarios. The LSTM algorithm is considered capable of handling long-term dependencies on its input and can predict data for a relatively long time. Based on research conducted from all models, the best results were obtained with an RMSE value of 0.699 obtained in model 1 with the number of hidden layers 2 and 64 neurons. Adding the number of hidden layers can significantly affect the RMSE results using neurons 16 and 32 in Model 1.
Machine Learning-Based Allergen Risk Detection in Food Recipes Using K-Means Clustering and Support Vector Machine Zakaria, Adil; Wibawa, Aji Prasetya; Musyaffa', Ahmad 'Ammar; Alamsyah, David Satria; Yulianto, Aldy Rahmat; Utama, Agung Bella Putra
JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit) Vol. 15 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jmmr.v15i1.705

Abstract

Errors in identifying food allergens in hospital menus may pose serious risks to patient safety. This study proposes a machine learning approach for automated allergen risk classification using food recipe data. A dataset of 9,986 Indonesian recipes was collected from an online recipe platform via web scraping and mapped to 14 major allergen attributes in accordance with international food safety standards. To represent ingredient variability, a rule-based data augmentation strategy was applied, generating recipe variations from optional ingredients, yielding 15,031 additional records after filtering out unrealistic combinations. Because ground-truth clinical labels were unavailable, K-Means clustering was used to generate pseudo-labels that capture similarity patterns in allergen composition. These cluster assignments were then used as target classes for classification using Support Vector Machine (SVM) with Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid kernels. Model performance was evaluated using 10-fold cross-validation with accuracy, precision, recall, and F1-score metrics, and additional hyperparameter tuning was performed to optimize model parameters. The results show that Linear, Polynomial, and RBF kernels consistently achieve high performance (0.99–1.00), whereas the Sigmoid kernel yields lower, less stable performance. However, these findings should be interpreted cautiously, as the dataset originates from a recipe platform and the labeling structure is derived from clustering rather than direct clinical annotation.
Comparative Analysis of YOLOv8 Segmentation Variants for Indonesian Sign Language (SIBI) Recognition Azizah, Desi Fatkhi; Handayani , Anik Nur; Wibawa, Aji Prasetya; Fukuda, Osamu
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

Abstract

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.
Co-Authors A.N. Afandi Abd. Rasyid Syamsuri Abdullah Sholum Abdur Rohman Achmad Fanany Onnilita Gaffar Adaby, Resnu Wahyu Ade Kurnia Ganesh Akbari 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 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 Ardiansyah, Mohammad Iqbal Firman Aripriharta - Arya Tandy Hermawan Ashar, Muhammad Astuti, Wistiani Atmaja, I Made Ari Dwi Suta Atmaja, Nimas Hadi Azizah, Desi Fatkhi 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 Darwis, Herdianti David Satria Alamsyah Dedes, Khen Dedi Kuswandi Dedy Kuswandi Denis Eka Cahyani Denna Delawanti Chrisyarani, Denna Delawanti Desi Anggreani Devi Dwi Purwanto 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 Eva, Nur 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 Felix Andika Dwiyanto 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 Fukuda, Osamu Gianika Roman Sosa Graciello, Manuel Tanbica Gülsün Kurubacak Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hammad, Jehad A. H. Hammad, Jehad A.H Handayani , Anik Nur 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 Hasihi, Cholisah Erman Haviluddin Haviluddin Haviluddin, - 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 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 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 Hidayat, Wahyu 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 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 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 Shili, Hechmi 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 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 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