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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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EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing Hermawan, Arya Tandy; Zaeni, Ilham Ari Elbaith; Wibawa, Aji Prasetya; Gunawan, Gunawan; Hartono, Nickolas; Kristian, Yosi
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1497

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers.

PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Muladi, Muladi; Nafalski, Andrew
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.858

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.

Modelling Naïve Bayes for Tembang Macapat Classification Wibawa, Aji Prasetya; Ningtyas, Yana; Atmaja, Nimas Hadi; Zaeni, Ilham Ari Elbaith; Utama, Agung Bella Putra; Dwiyanto, Felix Andika; Nafalski, Andrew
Harmonia: Journal of Arts Research and Education Vol 22, No 1 (2022): June 2022
Publisher : Department of Drama, Dance and Music, FBS, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/harmonia.v22i1.34776

The tembang macapat can be classified using its cultural concepts of guru lagu, guru wilangan, and guru gatra. People may face difficulties recognizing certain songs based on the established rules. This study aims to build classification models of tembang macapat using a simple yet powerful Naïve Bayes classifier. The Naive Bayes can generate high-accuracy values from sparse data. This study modifies the concept of Guru Lagu by retrieving the last vowel of each line. At the same time, guru wilangan’s guidelines are amended by counting the number of all characters (Model 2) rather than calculating the number of syllables (Model 1). The data source is serat wulangreh with 11 types of tembang macapat, namely maskumambang, mijil, sinom, durma, asmaradana, kinanthi, pucung, gambuh, pangkur, dandhanggula, and megatruh. The k-fold cross-validation is used to evaluate the performance of 88 data. The result shows that the proposed Model 1 performs better than Model 2 in macapat classification. This promising method opens the potential of using a data mining classification engine as cultural teaching and preservation media.

PENERAPAN INTERNET OF THINGS PADA PERTAMINI DI LOKASI WISATABEDENGAN DESA SELOREJO Quota Alief Sias; Soraya Norma Mustika; Aji Prasetya Wibawa; Langlang Gumilar
Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) #5 2024 Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) 2020
Publisher : Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) #5 2024

Show Abstract | Download Original | Original Source | Check in Google Scholar

Indonesia merupakan Negara yang kaya akan sektor pariwisata, karenakeindahan alamnya yang melimpah. Pariwisata sendiri merupakan salah satuaset atau sektor yang dapat membantu mensejaterahkan rakyat secara cepat.Sayangnya, banyak tempat wisata yang belum terkelola dengan baik, dari segiinfrastruktur maupun fasilitas umum. Salah satu tempat wisata yang belummemiliki fasilitas umum yang maksimal adalah kawasan Bedengan KecamatanDau. Keindahan alamnya yang masih asri menjadi daya tarik khusus untukwisatawan dalam negeri mapun luar negeri. Sayangnya akses yang kurangbegitu baik untuk menuju ke tempat wisata ini, menjadi permasalahantersendiri bagi Desa ini untuk menjadi tempat wisata yang berkembang.Jarak yang cukup jauh dari pusat kota Malang, sekitar 25 km membuatpara wisatawan kesulitan untuk mencapai desa wisata. Untuk mencapaikawasan Bedengan biasanya para wisatawan menggunakan transportasipribadi dikarenakan sulitnya atau bahkan tidak adanya transportasi umum.Karena banyaknya wisatawan menggunakan transportasi pribadi, makadiperlukan fasilitas umum seperti SPBU supaya wisatawan maupun warga bisamengisi ulang bahan bakar secara lebih mudah. Sayangnya letak SPBU yangterdekat dari lokasi Bedengan masih jauh dari lokasi wisata. Oleh karena itu,pengabdian ini mengimplementasikan pertamini yang dipasang didekat lokasiwisata Bedengan dengan dapat dipantau menggunakan website atau aplikasismartphone karena berbasis teknologi Internet of Things (IoT). Dengandemikian pengunjung dan pengelola wisata lebih mudah untuk mengisi ulangbahan bakar.

MATHEMATICAL ANXIETY AMONG ENGINEERING STUDENTS Rully Charitas Indra Prahmana; Tri Sutanti; Aji Prasetya Wibawa; Ahmad Muhammad Diponegoro
Jurnal Infinity Vol 8 No 2 (2019): Volume 8 Number 2, Infinity
Publisher : IKIP Siliwangi and I-MES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22460/infinity.v8i2.p179-188

Mathematical anxiety has a negative relationship with mathematics performance and achievement. Further explained, mathematics anxiety has an indirect effect on mathematics performance. This research explores sources or factors related to mathematics anxiety among engineering students at a private university in Indonesia. A total of 47 engineering students participated in this survey that randomly chosen based on gender, major, and age. Two main factors are affecting the mathematics anxiety of engineering students, namely internal and external factors. The results show that mathematics anxiety among engineering students is manifested into three aspects. Firstly, the home aspects are talking about the influence of parents and sibling. Secondly, society's issues are discussing self-efficacy, social reinforcement to hate mathematics, and social stereotypes. Lastly, the classroom aspects are talking about the traditional mathematics learning process and classroom culture, namely the experience of learning mathematics in classrooms and relationships between friends during learning. The details of the statements under the aspects also highlight unique problems and are not covered by previous research in mathematical anxiety. Next, differences in mathematics anxiety by gender and faculty were examined.

KOLABORASI UNIVERSITAS NEGERI MALANG DENGAN PERHIMPUNAN PELAJAR INDONESIA (PPI) TAIPEI DALAM PELATIHAN DIGITAL MARKETING UNTUK BURUH MIGRAN DI TAIWAN Aripriharta, Aripriharta; Wibawa, Aji Prasetya; Andriansyah, Muhammad Rizal; Nasih, Ahmad Munjin; Purwatiningsih, Ayu; Faidzin, Ilham; Bagaskoro, Muhammad Cahyo
Kumawula: Jurnal Pengabdian Kepada Masyarakat Vol 7, No 3 (2024): Kumawula: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kumawula.v7i3.51062

The collaboration between PPI Taipei and UM aims to equip migrant workers in Taiwan with digital marketing skills that are essential in the global era. The challenges of fierce competition in the job market demand an understanding of business technology. However, limited access and resources are obstacles for them to acquire these skills. To address this, "Smart Apps" was created specifically to help migrant workers and Muslim students in Taiwan. The main objective of this activity is to provide quality digital marketing training for migrant workers in Taiwan through the "Smart Apps" application. It is hoped that an understanding of digital marketing strategies will provide independence to migrant workers in finding job opportunities and making a positive contribution to Taiwan's economy. This overseas service activity is designed using an implementative method, starting from the design and implementation of applications in Taipei city. Random participation from PPI Taipei students, migrant workers, and travelers will be involved in the training and application development process.Kolaborasi antara Perhimpunan Pelajar Indonesia (PPI) Taipei dan Universitas Negeri Malang (UM) bertujuan membekali buruh migran di Taiwan dengan keterampilan digital marketing yang penting dalam era global. Tantangan persaingan ketat di pasar kerja menuntut pemahaman akan teknologi bisnis. Namun, keterbatasan akses dan sumber daya menjadi hambatan bagi mereka dalam memperoleh keterampilan tersebut. Untuk mengatasi hal ini, diciptakan aplikasi "Smart Apps" yang secara khusus ditujukan untuk membantu buruh migran dan pelajar Muslim di Taiwan. Tujuan utama kegiatan ini adalah memberikan pelatihan digital marketing yang berkualitas bagi buruh migran di Taiwan melalui aplikasi "Smart Apps". Diharapkan, pemahaman tentang strategi digital marketing akan memberikan kemandirian kepada para buruh migran dalam mencari peluang kerja serta memberi kontribusi positif pada ekonomi Taiwan. Kegiatan pengabdian luar negeri ini dirancang menggunakan metode implementatif, dimulai dari perancangan dan implementasi aplikasi di kota Taipei. Partisipasi secara acak dari pelajar PPI Taipei, buruh migran, dan pelancong akan terlibat dalam proses pelatihan dan pengembangan aplikasi. Berdasarkan hasil pengabdian, program pelatihan digital marketing untuk buruh migran Indonesia di Taiwan yang diselenggarakan melalui kolaborasi antara Universitas Negeri Malang dan PPI Taipei berhasil meningkatkan pengetahuan dan keterampilan peserta dalam pemasaran digital, mengembangkan aplikasi "Smart Apps" untuk membantu mencari informasi restoran halal, serta menunjukkan sinergi positif antara universitas dan organisasi mahasiswa dalam memberikan kontribusi kepada masyarakat.

Refining the Performance of Indonesian-Javanese Bilingual Neural Machine Translation Using Adam Optimizer Putri, Fadia Irsania; Wibawa, Aji Prasetya; Collante, Leonel Hernandez
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2467.271-282

This study focuses on creating a Neural Machine Translation (NMT) model for Indonesian and Javanese languages using Long Short-Term Memory (LSTM) architecture. The dataset was sourced from online platforms, containing pairs of parallel sentences in both languages. Training was performed with the Adam optimizer, and its effectiveness was compared to machine translation (MT) conducted without an optimizer. The Adam optimizer was utilized to enhance the convergence speed and stabilize the model by dynamically adjusting the learning rate. Model performance was assessed using BLEU (Bilingual Evaluation Understudy) scores to evaluate translation accuracy across different training epochs. The findings reveal that employing the Adam optimizer led to a significant enhancement in model performance. At epoch 2000, the model using the Adam optimizer achieved the highest BLEU score of 0.989957, reflecting very accurate translations, whereas the model without the optimizer showed lower results. Furthermore, translations from Indonesian to Javanese were found to be more precise than those from Javanese to Indonesian, largely due to the intricate structure and varying speech levels of the Javanese language. In summary, the implementation of the LSTM method with the Adam optimizer significantly improved the accuracy of bidirectional translations between Indonesian and Javanese. This research contributes notably to the advancement of local language translation technologies, supporting language preservation in the digital age and holding promise for applications in other regional languages.

Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques Pranolo, Andri; Setyaputri, Faradini Usha; Paramarta, Andien Khansa’a Iffat; Triono, Alfiansyah Putra Pertama; Fadhilla, Akhmad Fanny; Akbari, Ade Kurnia Ganesh; Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Uriu, Wako
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2333.210-220

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models

Analysis of battery energy storage system (BESS) performance in reducing the impact of variable renewable energy generation intermittency on the electricity system Mudakir, Mudakir; Aripriharta, Aripriharta; Wibawa, Aji Prasetya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1032

Indonesia has set a target to achieve a 23 % share of new and renewable energy (NRE) in its national energy mix by 2025. Sulawesi island has significant wind and solar energy potential, but the integration of these variable renewable energy (VRE) sources, such as solar photovoltaic (PV) and wind turbines (WT), poses challenges due to their fluctuating output. The aim of this study is to analyze the impact of battery energy storage systems (BESS) in reducing the intermittency of solar power generation and improving grid stability in North Sulawesi and Gorontalo. The study uses a combination of various technical simulations to assess the performance of BESS in stabilizing voltage and frequency fluctuations within the electricity system. The approach includes power flow analysis, transient stability testing, and short-circuit studies, with and without the integration of BESS. The results show that implementing a 10 MW/5 MWh BESS can significantly reduce frequency deviations, limiting frequency drops to 49.82 Hz during disturbances, compared to 49.67 Hz without BESS. In addition, BESS helps maintain voltage stability at critical substations by reducing voltage fluctuations by up to 40 %. This research demonstrates that BESS integration can enable a more stable and reliable grid, supporting the development of renewable energy without compromising power quality.

Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Chuttur, Mohammad Yasser
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.

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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