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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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Pelatihan Penyusunan Proposal Penelitian dan Pengabdian untuk Dosen STIEBI Syaikhona Kholil Sidogiri Pasuruan ST. Ulfawanti Intan Subadra; Sri Rahmawati; Aji Prasetya Wibawa; RR. Poppy Puspitasari; Nurul Hidayat; Ahmad Taufiq
I-Com: Indonesian Community Journal Vol 5 No 2 (2025): I-Com: Indonesian Community Journal (Juni 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i2.6888

Research and community service are part of the Tri Dharma of Higher Education that must be carried out by a lecturer. In fact, the Government has provided funds for lecturers to carry out research and community service. However, low motivation, interest, and knowledge in writing are some of the reasons why lecturers do not write proposals. Therefore, the community service team conducted training on writing research and community service proposals for lecturers at the Syaikhona Kholil Islamic Economics and Business College, Pasuruan. The training was conducted using lecture and direct practice methods, and was divided into three sessions. The evaluation results showed that 100% of participants stated that the material presented was in accordance with the needs of the participants and the strategies used in the training were very effective. This activity had a positive impact on lecturers' motivation in writing proposals. In addition, knowledge and ability in using the Zotero reference manager also increased, making it easier for STEIBI Syaikhona Kholil lecturers to write references both in proposals and in scientific articles.

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

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.

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

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

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

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

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

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.

Fixed sherwood duel optimization for time series imputation Agung Bella Putra Utama; Aji Prasetya Wibawa; Anik Nur Handayani; Andrew Nafalski
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2396

Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.

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

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.

IoT-Based Water Quality Monitoring and Sustainable Modeling for Smart Campuses Fachrul Kurniawan; Miladina Rizka Aziza; Novrindah Alvi Hasanah; Fadia Irsania Putri; Aji Prasetya Wibawa; Jehad Hammad; Yuhefizar
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.v10i2.7360

This study proposes an IoT-based water quality monitoring framework integrated with a continuous sustainable modeling approach for smart campus applications. A total of 404 sensor observations were collected, including pH, turbidity, temperature, and Total Dissolved Solids (TDS). A continuous water suitability score ranging from 0 to 1 was constructed based on WHO drinking water standards, and Multiple Linear Regression was employed to model the relationship between water quality parameters and the suitability score. The main contribution of this study lies in the development of a lightweight analytical framework that combines continuous regression modeling with threshold-based classification to support real-time decision-making in resource-constrained environments. The dataset was divided into 90% training and 10% testing data. The results show that the proposed framework achieved a classification accuracy of 88.5% based on threshold mapping of regression outputs, with a misclassification rate of 11.5%. These findings demonstrate the effectiveness of integrating IoT-based monitoring with interpretable and computationally efficient analytical models for sustainable campus water management.

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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