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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Manajemen Informatika Jurnal Kridatama Sains dan Teknologi Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat Variabel Journal of Intelligent Computing and Health Informatics (JICHI) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Advance Sustainable Science, Engineering and Technology (ASSET)
Eko Hari Rachmawanto
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry 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 Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Hybrid Cryptography-Steganography Scheme Based on Camellia-256 and LSB for Enhanced Security and Imperceptibility of Secret Messages Pujiono, Imam Prayogo; Rachmawanto, Eko Hari; Sari, Christy Atika; Ariza, Said Fachri; Kholifatun, Isnaeni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5323

Abstract

The development of digital communications has increased the risk of message interception and manipulation, necessitating robust and multi-layered security solutions. This research designs, implements, and evaluates a multi-layered security scheme that integrates cryptography and steganography. The proposed method first encrypts the secret message using the Camellia-256 algorithm in Electronic Codebook (ECB) mode with PKCS#7 padding. The resulting ciphertext is then embedded into the cover image using the Least Significant Bit (LSB) steganography technique. From a practical standpoint, this design provides defense-in-depth for covert communication: encryption preserves confidentiality even if the hidden payload is detected, while steganography reduces the likelihood that the encrypted content is flagged during transmission. This combination mitigates LSB’s weakness against statistical steganalysis by encrypting the payload into ciphertext, thereby reducing structured bit patterns that may otherwise facilitate statistical detection. System performance is quantitatively evaluated using two primary metrics: the Avalanche Effect to measure cryptographic strength and the Peak Signal-to-Noise Ratio (PSNR) to measure the visual imperceptibility of the stego-image. The experimental results demonstrate excellent cryptographic strength, evidenced by an average Avalanche Rate of 54.37%, indicating that minimal changes to the input result in significant changes to the output. Furthermore, the scheme exhibits excellent visual imperceptibility with an average PSNR of 75 dB, making the stego-image visually indistinguishable from the original cover image. It is concluded that the proposed hybrid scheme offers a robust and validated solution for secure message communication, combining content confidentiality through cryptography and message obfuscation through steganography, thus providing dual protection against cybersecurity threats.
Super Encryption with Hill Cipher and Myszkowski Transposition for Android Multimedia Files Security Aldi Febriayanto; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13293

Abstract

This research aims to design, implement, and evaluate an Android-based data security application called Secure Vault using a super encryption method that combines Hill Cipher (substitution) and Myszkowski Transposition (transposition). The background of this research is motivated by the increasing number of cyberattacks on Android devices, particularly those targeting multimedia data. The method used is Research and Development with the Waterfall model. A total of 300 synthetic multimedia files (images, videos, documents, texts) were encrypted and decrypted using three methods: Hill Cipher alone, Myszkowski Transposition alone, and super encryption. Testing included computational time, Shannon entropy values, black box testing (15 scenarios), User Acceptance Test (UAT) with 30 respondents, and statistical analysis (ANOVA, Pearson correlation, paired t-test). The results showed that super encryption produced an average encryption time of 7.56 seconds and the highest entropy increase (0.92 bits/byte, approaching 92.8% of the maximum 8 bits/byte). The encryption/decryption success rate for valid files reached 100%, with a black box pass rate of 90.3%. The UAT obtained an average score of 3.90/5 (Good category). The Pearson correlation between file size and encryption time was very strong (r = 0.939, p < 0.001). The conclusion of this research is that super encryption effectively enhances multimedia data security on Android with high entropy and perfect functional success, although there is a trade-off in processing time that remains acceptable. The Secure Vault application is feasible for protecting personal data.
Classification of Depression Indication Based on Facial Expression Using MobileNetV2 and Support Vector Muhammad Eswin Bakkar; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13495

Abstract

Depression is a mental health disorder that can affect an individual's emotional condition, behavior, and quality of life. Facial expressions can represent a person's emotional state and therefore have the potential to be utilized as a visual source of information in the development of artificial intelligence-based classification systems. This study aims to develop a depression indication classification model based on facial expressions using MobileNetV2 as a feature extractor and Support Vector Machine (SVM) as a classifier. The FER2013 dataset was used and grouped into two classes, namely depression indication and non-depression indication based on predefined facial expression categories used in this study. After the labeling process, a total of 19,275 facial images were obtained, with 3,855 images used as testing data. The proposed method consists of image preprocessing, feature extraction using MobileNetV2, classification using SVM, threshold optimization, and model evaluation. Experimental results show that the proposed model achieved an accuracy of 79.69% with an AUC value of 88.62%. Threshold optimization produced an optimal threshold value of 0.44 and improved the accuracy to 80.34%. The precision, recall, and F1-score values indicate relatively balanced performance across both classes. The results demonstrate that the combination of MobileNetV2 and SVM can provide good classification performance on the FER2013 dataset grouped into depression indication and non-depression indication classes.
Real-Time Facial Emotion Recognition Using Mini-Xception and EfficientNetB4 Gede Pradistya Evan Aryaputra; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13496

Abstract

Facial Emotion Recognition (FER) is an important field within computer vision and human–computer interaction that focuses on the automatic recognition of human emotional expressions through facial images. This study presents a comparative analysis of two Convolutional Neural Network (CNN) architectures, namely Mini-Xception and EfficientNetB4, for real-time facial emotion classification using the RAF-DB (Real-world Affective Faces Database) dataset. Mini-Xception was employed as a lightweight model with lower computational requirements, whereas EfficientNetB4 utilized a transfer learning approach to achieve superior classification performance. The RAF-DB dataset consists of seven primary emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise. The preprocessing stage included facial image resizing, grayscale conversion for Mini-Xception, RGB normalization for EfficientNetB4, and the application of data augmentation techniques to improve model generalization capability. Experimental results demonstrated that Mini-Xception achieved a validation accuracy of 52.12%, while EfficientNetB4 attained a validation accuracy of 86.02%. In real-time implementation using a webcam and OpenCV, Mini-Xception exhibited advantages in inference speed, whereas EfficientNetB4 produced more stable and accurate emotion predictions. The findings indicate a trade-off between computational efficiency and classification performance. Therefore, EfficientNetB4 is more suitable for systems requiring high classification accuracy, while Mini-Xception is more appropriate for real-time applications operating under limited computational resources.
Banana Ripeness Classification Based on Color and Texture Using a CNN with ResNet50 Architecture Raafiandy Wirawan Avicenna; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13508

Abstract

Banana ripeness is one of the main factors affecting fruit quality before distribution and consumption. Conventional ripeness assessment is generally performed through visual observation, which may lead to subjective and inconsistent results. Previous studies have reported promising results in banana ripeness classification. However, distinguishing adjacent ripeness stages remains challenging because of their similar visual characteristics. This study proposes a banana ripeness classification model using a Convolutional Neural Network (CNN) with the ResNet50 architecture. The dataset consisted of four ripeness categories, namely unripe, ripe, overripe, and rotten. Data balancing, preprocessing, and augmentation were applied before model training. A total of 1,120 images were used to train and evaluate the model. Transfer learning with full fine-tuning was employed to adapt pretrained visual features to different banana ripeness levels. The experimental results showed that the proposed model achieved an accuracy of 92.86%, while precision, recall, and F1-score reached 93%. Several misclassifications were observed between adjacent ripeness categories due to similarities in visual characteristics. These results indicate that the proposed ResNet50 model can effectively classify banana ripeness levels on the testing dataset based on color and texture information learned automatically from digital images.
Human Body Posture Classification from Digital Images Using the InceptionV3 Architecture Munandar Rahmat Prayogi; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13571

Abstract

This study aims to develop a human body posture classification model based on digital images using the InceptionV3 architecture. The dataset used in this study is the MPII Human Pose Dataset, which contains a wide variety of human activities and body postures. The research began with label extraction from a metadata file in .mat format, followed by the selection of the 20 activity classes with the largest number of samples. Subsequently, the dataset was balanced using an undersampling technique, resulting in 140 images for each class. The images were then resized to 224×224 pixels, normalized using the preprocess_input function, and enhanced through data augmentation applied to the training set. The model was developed using a transfer learning approach with InceptionV3 as the base model. Additional layers, including Global Average Pooling, Dropout, and Dense layers, were added to perform multi-class classification. The experimental results showed that the proposed model achieved a test accuracy of 0.8893 with a test loss of 0.5587. Furthermore, the macro-average metrics obtained from the classification report were 0.9018 for precision, 0.8893 for recall, and 0.8877 for F1-score. These results indicate that the model was able to classify most activity classes effectively. However, several classes with similar visual characteristics still caused misclassification, indicating opportunities for further improvement in human posture recognition performance.
Image Classification using DenseNet-121 Based on MediaPipe Face Mesh for Real-Time Drowsiness Detection Raihan Ramadhan Hamzah; Christy Atika Sari; Eko Hari Rachmawanto; Rabei Raad Ali
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13586

Abstract

The high rate of traffic accidents caused by driver drowsiness and microsleep highlights the urgent need for reliable driver monitoring systems. However, conventional Eye Aspect Ratio (EAR) methods often fail due to their high sensitivity to changes in head poses and ambient lighting conditions, while standard Convolutional Neural Network (CNN) models impose heavy computational loads on hardware. This study aims to implement and evaluate a real-time drowsiness detection system by integrating the DenseNet-121 architecture with MediaPipe Face Mesh. The proposed method utilizes MediaPipe Face Mesh to isolate the left and right eye Regions of Interest (ROI) independently, using a proportional padding of 35%, which are then classified using a DenseNet-121 transfer learning model fine-tuned in two stages across its last 30 layers. Evaluation was conducted using a custom dataset of 2,000 source images from five subjects, yielding 3,926 eye-region samples after extraction and quality filtering, assessed using a Subject-Independent Leave-One-Subject-Out (LOSO) cross-validation protocol. Across five folds, the model achieved a mean accuracy of 83.22% (standard deviation 13.22 percentage points) and a mean AUC of 0.879 (standard deviation 0.131), with performance variation across subjects found to correlate with inter-subject differences in eye-closure expressiveness, where the two lowest performing subjects also exhibited the lowest AUC values (0.707 and 0.769). The system achieved an average total latency of 198.00 ms per frame, equivalent to 5.1 FPS. These findings indicate that the integration of MediaPipe Face Mesh and DenseNet-121 shows meaningful potential for real-time drowsiness monitoring, while also highlighting the importance of subject-independent evaluation and cross-domain generalization for reliable real-world deployment.
Implementation of the ML-KEM Protocol for Securing Parameter Exchange in FedAvg-Based Federated Learning Architecture Against Quantum Computing Threats Ariq Arsalan; Eko Hari Rachmawanto; Christy Atika Sari
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13589

Abstract

Federated Learning (FL) enables collaborative machine learning by aggregating local models from decentralized clients without sharing raw data. However, the parameter exchange process in standard FL architectures is vulnerable to emerging quantum computing threats. Specifically, Shor's algorithm, executable on large-scale quantum computers, can break conventional asymmetric cryptography such as RSA and ECDH in polynomial time, thereby threatening the security of FL systems that rely on these traditional public-key infrastructures for parameter exchange. This study addresses this vulnerability by implementing the recently standardized post-quantum cryptographic protocol, ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism), within a FedAvg-based FL architecture. The integration is designed to secure the parameter exchange pipeline without compromising the neural network's performance. Experimental results on a simulated environment utilizing the MNIST dataset demonstrate that the ML-KEM integration preserves 100% of the global model accuracy. Furthermore, the cryptographic latency overhead introduced by encapsulation and decapsulation remains highly efficient, proving its potential as a robust security layer. While current evaluations focus on small-scale deployments, the proposed architecture establishes a foundational post-quantum security framework for future privacy-preserving distributed learning systems.
The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade Fidela Azzahra; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.672

Abstract

The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
Implementation of DenseNet121 Architecture for Waste Type Classification Munis Zulhusni; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.673

Abstract

The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abu Salam Adhitya Nugraha Adiyah Mahiruna Agustina, Feri Ahmad Salafuddin Ajib Susanto Akbar Aji Nugroho Akbar, Ilham Januar Al-Ghiffary, Maulana Malik Ibrahim Aldi Febriayanto Ali, Rabei Raad Alifia Salwa Salsabila Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anidya Nur Latifa Annisa Sulistyaningsih Antonio Ciputra Antonius Erick Handoyo Aqsel, Aryasatya Muhammad Ardika Alaudin Arsa Arfian, Aldi Azmi Ariq Arsalan Aris Marjuni Aristides Bima Wintaka Ariza, Said Fachri Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Asyari, Fajar Husain Aulia, Lathifatul Auni, Amelia Gizzela Sheehan Bijanto Bijanto Briliantino Abhista Prabandanu Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Christy Atika Sari Cinantya Paramita Ciputra, Antonio D.R.I.M. Setiadi Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga De Rosal Ignatius Moses Setiadi Deddy Award Widya Laksana Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Destriana, Rachmat Didik Hermanto Dila Ananda Oktafiani Doheir, Mohamed Doheir, Mohamed Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Edi Faisal Egia Rosi Subhiyakto Egia Rosi Subhiyakto Elkaf Rahmawan Pramudya Ellen Proborini Erna Daniati Erna Zuni Astuti Ery Mintorini Faisal, Edi Farrel Athaillah Putra Fazlur Rahman Hafidz Fida Maisa Hana Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Gede Pradistya Evan Aryaputra Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Hadi, Heru Pramono Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Herman Yuliansyah, Herman Heru Agus Santoso Heru Lestiawan Heru Pramono Hadi Hidayat, Muhammad Taufiq Hidayati, Ulfa Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ihya Ulumuddin, Dimas Irawan Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiawan, Deden Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Kholifatun, Isnaeni Krismawan, Andi Danang Kumala, Raffa Adhi Kunio Kondo Kurniawan, The, Obed Danny Kusuma, Edi Jaya L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Lungido, Joshua Mabina, Ibnu Farid Mahadika Pradipta Himawan Mahiruna, Adiyah Maulana Malik Ibrahim Al-Ghiffary Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mehta Pradnyatama Meitantya, Mutiara Dolla Moch. Arief Soeleman Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Eswin Bakkar Muhammad Khanif Naufal Muhammad Mahdi Mulyono, Ibnu Utomo Wahyu Munandar Rahmat Prayogi Munis Zulhusni Musab Iqtait Musfiqur Rahman Sazal Muslih Muslih Muslih Muslih Nabila, Qotrunnada Nanna Suryana Herman NGATIMIN, NGATIMIN Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yacoob Nova Rijati Novi Hendriyanto, Novi Nugroho, Dicky Anggriawan Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Nuri Nuri Oktaridha, Harwinanda Oktayaessofa, Eqania Oleiwi, Ahmed Kareem Parti Astuti, Yani Parti Astuti, Yani parti astuti, yani Parti Astuti1, Yani Parti Astuti1, Yani Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Proborini, Ellen Pulung Nurtantio Andono Purwanto Purwanto Purwanto Purwanto Putra, Ifan Perdana Putri, Ni Kadek Devi Adnyaswari Raafiandy Wirawan Avicenna Rabei Raad Ali Rabei Raad Ali Rabei Raad Ali Rabei Raad Ali Raihan Ramadhan Hamzah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Reza Arista Pratama Ruri Suko Basuki Safitri, Melina Dwi Saifullah, Zidan Sania, Wulida Rizki Santoso, Bagus Raffi Saputro, Fakhri Rasyid Sarker, Md Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Setiawan, Tan Valencio Yobert Geraldo Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sofyan, Ega Adiasa Solichul Huda, Solichul Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Sumarni Adi, Sumarni Suprayogi Suprayogi Suprayogi Suprayogi Sutrisno, Hendra Syabilla, Mutiara Tan Samuel Permana Tan Samuel Permana Titien Suhartini Sukamto Tri Esti Rahayuningtyas Umah Nur, Raisul Umam, Choerul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wahyu Dwy Permana Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Winarsih, Nurul Anisa Sri Winaryanti, Hida Sekar Yaacob, Noorayisahbe Bt Mohd Yaacob, Noorayisahbe Mohd Yani Parti Astuti