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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA Jurnal Ilmu Pendidikan Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Jurnal Pendidikan Sains MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Informatika Jurnal Infinity Harmonia: Journal of Research and Education Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Sistem Informasi dan Bisnis Cerdas Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science Jurnal Ilmiah Flash JURNAL MEDIA INFORMATIKA BUDIDARMA Ranah: Jurnal Kajian Bahasa Jurnal Sains dan Informatika Jurnal Inovasi Bisnis (Inovbiz) ILKOM Jurnal Ilmiah at-tamkin: Jurnal Pengabdian kepada Masyarakat SENTIA 2016 SENTIA 2015 Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Educational Research and Evaluation International Journal of Elementary Education Jurnal Ilmiah Sekolah Dasar Gelar : Jurnal Seni Budaya Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Performance PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Antivirus : Jurnal Ilmiah Teknik Informatika Kumawula: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics International Journal of Visual and Performing Arts Journal of Robotics and Control (JRC) Jurnal Mnemonic Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal Teknik Elektro Uniba (JTE Uniba) Frontier Energy System and Power Engineering Food ScienTech Journal Belantika Pendidikan Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International Journal of Engineering, Science and Information Technology International Journal of Robotics and Control Systems Abditeknika - Jurnal Pengabdian Kepada Masyarakat Anjoro : International Journal of Agriculture and Business Journal of Dinda : Data Science, Information Technology, and Data Analytics Indonesian Community Journal International journal of education and learning Buletin Sistem Informasi dan Teknologi Islam Jurnal Sistem Informasi dan Bisnis Cerdas Reflection Journal Applied Engineering and Technology Bulletin of Culinary Art and Hospitality Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit) Journal of Information Technology and Cyber Security KOPEMAS Jurnal Infinity Bioma : Jurnal Ilmiah Biologi Advance Sustainable Science, Engineering and Technology (ASSET) Signal and Image Processing Letters Ranah: Jurnal Kajian Bahasa
Aji Prasetya Wibawa
Univeristas Negeri Malang
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Hybrid deep learning for estimation of state-of-health in lithium-ion batteries Cahyani, Denis Eka; Gumilar, Langlang; Afandi, Arif Nur; Wibawa, Aji Prasetya; Junoh, Ahmad Kadri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp995-1006

Abstract

Lithium-ion (li-ion) batteries have a high energy density and a long cycle life. Lithium-ion batteries have a finite lifespan, and their energy storage capacity diminishes with use. In order to properly plan battery maintenance, the state of health (SoH) of lithium-ion batteries is crucial. This study aims to combine two deep learning techniques (hybrid deep learning), namely convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for SoH estimation in li-ion batteries. This study contrasts hybrid deep learning methods to single deep learning models so that the most suitable model for accurately measuring the SoH in lithium-ion batteries can be determined. In comparison to other methodologies, CNN-BiLSTM yields the best results. The CNN-BiLSTM algorithm yields RMSE, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the following order: 0.00916, 0.000084, 0.0048, and 0.00603. This indicates that CNN-BiLSTM, as a hybrid deep learning model, is able to calculate the approximate capacity of the lithium-ion battery more accurately than other methods.
Load forecasting analysis for estimating transformer capacity of Karangkates Substations using Holt-Winters method in Python Rahmawati, Yuni; Kaki, Gregorius Paulus Mario Laka; Aripriharta, Aripriharta; Sujito, Sujito; Afandi, Arif Nur; Wibawa, Aji Prasetya; Purwatiningsih, Ayu; Bagaskoro, Muhammad Cahyo; Omar, Saodah; Rosmin, Norzanah
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2222-2233

Abstract

In the six years from 2010 to 2015, the peak load in the East Java region increased by an average of 284MW per year. Karangkates Substation is part of an interconnected electrical system that supplies Java Island. To ensure a high level of reliability in its service, it is necessary to prepare for load growth to make sure that it does not exceed its ideal conditions, therefore special analysis of transformer capacity is needed. Using the Holt-Winters (HW) method as a reference for processing the data can be used as a reference in planning and anticipating the growing electricity demand. The results of this study are with the accuracy of the HW method with mean absolute percentage error (MAPE) = 2.645%, while the accuracy of the fuzzy time series (FTS) method = 6.399%. A forecast result done with HW methods shows the transformer at the substation Karangkates reached its normal working capacity in March 2018 at 99.583% of installed capacity and exceeded the maximum capacity in April 2018 at 101.493% of installed capacity.
Dialect Classification of the Javanese Language Using the K-Nearest Neighbor Filby, Brilliant; Pujianto, Utomo; Hammad, Jehad A. H.; Wibawa, Aji Prasetya
Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12213

Abstract

Indonesia is rich in ethnic and cultural diversity, each reflected in its unique linguistic characteristics. One way to preserve the Javanese language is by conducting research on its dialects. This study aims to classify three main dialects in Java Island—East Java, Central Java, and West Java—using text data from online sources. The classification process includes preprocessing (tokenizing, case folding, and word weighting), data balancing with the Synthetic Minority Oversampling Technique (SMOTE), and classification using the K-Nearest Neighbor (K-NN) algorithm. This study highlights the importance of dialect recognition in supporting the preservation of the Javanese language and the development of linguistic technology applications. Testing using 10-fold cross-validation showed the best performance at , with an accuracy of 94.05%, precision of 95.83%, and recall of 94.44%. These findings significantly support computational linguistics research and the preservation of regional languages.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Analyzing Audience Sentiments in Digital Comedy: A Study of YouTube Comments Using LSTM Models Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.393

Abstract

The main objective of this paper is to analyze audience sentiment towards stand-up comedy content on the YouTube platform, specifically comments on stand-up comedy videos from Kompas TV, using the Long Short-Term Memory (LSTM) method. This research contributes significantly to a deeper understanding of how audiences engage with humorous content through a sentiment analysis approach that uses the LSTM model, which can capture complex nuances in humorous content, such as sarcasm, irony, and cultural references. The research methodology involves crawling data from YouTube, where user comments are extracted and processed through several stages of data cleaning, such as removing duplicate content, text normalization, and irrelevant comments. Once the data is prepared, the LSTM model is trained to analyze positive, negative, and neutral sentiments with varying accuracy rates of 85% for positive sentiment, 80% for negative sentiment, and 78% for neutral sentiment. The main results show that the LSTM model successfully classifies sentiments, although it needs help handling the more ambiguous neutral sentiments. The figures and tables included in this study illustrate the relationship between the number of views, likes, and the sentiment classification of the comments. One notable finding is a strong positive correlation between the number of views and video likes. The conclusions of this study underscore the need for model improvements to handle neutral sentiment better and capture the complexity of humor content. The implications of this research are useful for content creators and digital marketers in understanding and responding to audience preferences more effectively. They also pave the way for further research in sentiment analysis on more specific content genres on digital platforms.
Design and development of face recognition-based security system using expression game as liveness detection Yusmanto, Yunan; Ar Rosyid, Harits; Prasetya Wibawa, Aji
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.756

Abstract

Face recognition as a security system has undergone significant developments, but challenges in live detection are still a major issue in preventing fraud. Liveness detection is a method that helps face recognition security more resistant to fraud. This research aims to address this issue by developing an innovative security system that integrates face recognition with a facial expression game, enhancing live detection and user engagement. The primary objectives are to ensure seamless integration, maintain a fun and challenging user experience, and demonstrate practical applicability. We applied a Waterfall method in our research to ensure a straightforward approach. We successfully applied this system for the door lock-unlock mechanism, simulating a restricted area. YuNet, a face detection model runs in the web interface and controls the NodeMCU to either lock or unlock the door.  The study concluded 95% success rate from the participants in making facial expressions: Smile, Normal, and Sad. However, expressing Sadness within the 3-second timeframe posed some difficulties. The average duration for completing the mini-game was approximately 16.31 seconds from the start. The integration of a facial expression game as a liveness detection required careful design to balance security and user engagement that is fun to experience. This research underscores the significance of addressing current challenges in biometric security by integrating an interactive element into the live detection process. The developed system contributes to the field by enhancing the robustness and user experience of face recognition security systems, demonstrating potential for broader application in restricted access scenarios.
Ensemble semi-supervised learning in facial expression recognition Purnawansyah, Purnawansyah; Adnan, Adam; Darwis, Herdianti; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1880

Abstract

Facial Expression Recognition (FER) plays a crucial role in human-computer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various label-to-unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study’s reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.
Implementasi Sistem Monitoring Daya Sterilisasi Botol Kemasan Berbasis IoT-App untuk Pengembangan UMKM Aripriharta Aripriharta; Aji Prasetya Wibawa; Mohamad Rodhi Faiz; Markus Diantoro; Suhiro Wongso Susilo; Aditya Wahyu Setiawan
Jurnal Teknik Elektro Uniba (JTE UNIBA) Vol. 9 No. 1 (2024): JTE UNIBA (Jurnal Teknik Elektro Uniba)
Publisher : Lembaga Penelitian Universitas Balikpapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/jteuniba.v9i1.296

Abstract

Dalam dunia industri UMKM, kemasan botol menjadi elemen vital yang memerlukan sterilisasi dan pembersihan untuk menghindari kontaminasi bakteri. Untuk mengatasi masalah ini, dikembangkanlah mesin sterilisasi botol menggunakan sinar UV berbasis IoT yang dirancang untuk membantu UMKM meningkatkan efisiensi dan kualitas produk mereka. Proses pengembangan mesin ini melibatkan beberapa metode, termasuk analisis kebutuhan, perancangan, pelatihan, dan pengujian menyeluruh. Mesin ini menggunakan sinar UV untuk membunuh bakteri dan mikroorganisme lainnya secara efektif. Hasil pengujian menunjukkan bahwa mesin ini memiliki akurasi tinggi dalam monitoring daya, dengan tingkat akurasi lebih dari 95% pada mode sterilisasi dan 98% pada mode dryer. Hal ini membuktikan keandalan sistem dalam menjaga kinerja yang optimal. Teknologi IoT yang terintegrasi memungkinkan pemantauan dan pengendalian mesin secara real-time, memberikan kemudahan bagi pengguna dalam mengoperasikan dan memelihara mesin. Dengan adanya mesin sterilisasi berbasis IoT ini, UMKM dapat menghemat waktu dan tenaga, sambil tetap menjaga kualitas produk mereka. Selain itu, penggunaan teknologi ini juga dapat meningkatkan kepercayaan konsumen terhadap produk yang dihasilkan, serta memperkuat daya saing produk di pasaran. Implementasi mesin sterilisasi berbasis IoT ini diharapkan mampu memberikan solusi efektif bagi UMKM dalam menghadapi tantangan produksi yang lebih bersih, efisien, dan aman.
Analyzing event relationships in Andersen's Fairy Tales with BERT and Graph Convolutional Network (GCN) Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Ghosh, Anusua; Hernandez, Leonel
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1810

Abstract

This study explores the narrative structures of Hans Christian Andersen's fairy tales by analyzing event relationships using a combination of BERT (Bidirectional Encoder Representations from Transformers) and Graph Convolutional Networks (GCN). The research begins with the extraction of key events from the tales using BERT, leveraging its advanced contextual understanding to accurately identify and classify events. These events are then modeled as nodes in a graph, with their relationships represented as edges, using GCNs to capture complex interactions and dependencies. The resulting event relationship graph provides a comprehensive visualization of the narrative structure, revealing causal chains, thematic connections, and non-linear relationships. Quantitative metrics, including event extraction accuracy (92.5%), relationship precision (89.3%), and F1 score (90.8%), demonstrate the effectiveness of the proposed methodology. The analysis uncovers recurring patterns in Andersen's storytelling, such as linear event progressions, thematic contrasts, and intricate character interactions. These findings not only enhance our understanding of Andersen's narrative techniques but also showcase the potential of combining BERT and GCN for literary analysis. This research bridges the gap between computational linguistics and literary studies, offering a data-driven approach to narrative analysis. The methodology developed here can be extended to other genres and domains, paving the way for further interdisciplinary research. By integrating state-of-the-art NLP models with graph-based machine learning techniques, this study advances our ability to analyze and interpret complex textual data, providing new insights into the art of storytelling
Retaining humorous content from marked stand-up comedy text Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul; Voliansky, Roman; Cengiz, Korhan
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1812

Abstract

Identifying humor in stand-up comedy texts has distinct issues due to humor's subjective and context-dependent characteristics.  This study introduces an innovative method for humor retention in stand-up comedy content by employing a pre-trained BERT model that has been fine-tuned for humor classification.  The process commences with the collection and annotation of a varied assortment of stand-up comedy writings, categorized as hilarious or non-humorous, with essential comic elements like punchlines and setups highlighted to augment the model's comprehension of humor.  The texts undergo preprocessing and tokenization to be ready for input into the BERT model. Upon refining the model using the annotated dataset, predictions regarding humor retention are generated for each text, yielding classifications and confidence scores that reflect the model's certainty in its predictions.  The criterion for prediction confidence is set to categorize texts as "retaining humor."  The results indicate that prediction confidence is a dependable metric for humor retention, with elevated confidence scores associated with enhanced accuracy in comedy classification.  Nonetheless, the analysis reveals that text length does not affect the model's confidence much, contradicting the presumption that lengthier texts are more prone to comedy.  The findings underscore the significance of environmental and linguistic elements in comedy detection, indicating opportunities for model enhancement.  Future efforts will concentrate on augmenting the dataset to encompass a broader range of comic styles and integrating more contextual variables to improve prediction accuracy, especially in intricate or ambiguous comedic situations
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