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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 JUTI: Jurnal Ilmiah Teknologi Informasi Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Jurnal Informatika dan Teknik Elektro Terapan 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 SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer English Language and Literature International Conference (ELLiC) Proceedings Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Seminar Nasional Teknologi dan Multidisiplin Ilmu 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) INOVTEK Polbeng - Seri Informatika
Christy Atika Sari
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Improved imperceptible engagement-based 2D sigmoid logistic maps, Hill cipher, and Kronecker XOR product Lestiawan, Heru; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Purwanto, Purwanto; Sari, Christy Atika; Doheir, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8331

Abstract

Image encryption is a crucial facet of secure data transmission and storage, and this study explores the efficacy of combining sigmoid logistic maps (SLM), Hill cipher, and Kronecker's product method in enhancing image encryption processes. The evaluation, conducted on diverse images such as Lena, Rice, Peppers, Cameraman, and Baboon, unveils noteworthy findings. The Lena image emerges as the most successfully encrypted, as evidenced by the lowest mean squared error (MSE) at 92.81 and the highest peak signal-to-noise ratio (PSNR) at 19.43, reflecting superior fidelity and quality preservation. Additionally, the encryption of 64×64 pixels images consistently demonstrate robustness, with a high number of pixels change rate (NPCR) and unified average change intensity (UACI) values, particularly notable for the Cameraman image. Even for 128×128 pixels images, commendable encryption performance persists across the tested images. The amalgamation of SLM, Hill cipher, and Kronecker's product emerges as an effective strategy for balancing security and perceptual quality in image encryption, with the Lena image consistently outperforming others based on comprehensive metrics. This research provides valuable insights for future studies in the dynamic domain of image encryption, emphasizing the potential of advanced cryptographic techniques in ensuring secure multimedia communication.
Hybrid image encryption using quantum bit-plane scrambling and discrete wavelet transform Rachmawanto, Eko Hari; Susanto, Ajib; Hermanto, Didik; Sari, Christy Atika; Setiarso, Ichwan; Sarker, Md Kamruzzaman
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8887

Abstract

Digital image security is increasingly vulnerable to sophisticated attacks, underscoring the urgent need for robust encryption techniques. Traditional encryption methods often fall short in defending against advanced threats, highlighting the importance of innovative solutions to protect digital images. This study tackles these challenges by incorporating quantum computing into image encryption, employing techniques such as bit-plane scrambling, pixel permutation, and bit permutation. These strategies enhance security by introducing complex, non-linear transformations that make decryption attempts significantly more difficult without the correct cryptographic keys. A key configuration based on r=44, μ=2024 is employed to achieve this. The integration of quantum bit-plane scrambling and quantum pixel permutation results in a highly secure encryption method. Experimental results show substantial improvements in entropy levels, along with strong unified average changing intensity (UACI) and number of pixels change rate(NPCR) values across various images. Notably, the "Peppers" image achieved the best performance, with UACI values of 33.5572 and NPCR values of 99.8301. The method proves highly effective, as repeated tests with incorrect keys failed to decrypt the plain image accurately. Future research could explore the addition of a discrete quantum wavelet transform to further enhance the security and efficiency of quantum-based image encryption methods.
Identifikasi Citra Jenis Rempah-Rempah Menggunakan Arsitektur RestNet50 Sari, Christy Atika; Pradana, Luthfiyana Hamidah Sherly; Rachmawanto, Eko Hari
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10713

Abstract

Indonesia has various types of spices used in culinary and traditional medicine. However, changes in lifestyle and modernization have made it increasingly difficult for the younger generation to recognize spices directly. Conventional identification still relies on manual observation which is prone to errors. Therefore, an artificial intelligence-based solution is needed to improve the accuracy of spice classification. This study applies the Convolutional Neural Network (CNN) method with the ResNet50 architecture, which is part of Deep Learning, to classify digital images of spices. This model utilizes Computer Vision to recognize visual patterns, Transfer learning to improve training efficiency, and Data Augmentation Techniques such as rotation, flipping, and scaling to improve model robustness. Evaluation using Confusion Matrix was carried out with various dataset division scenarios, including ratios of 90:10, 80:20, 70:30, 60:40, and 50:50. The experimental results showed that the model with a ratio of 90:10 provided the best accuracy, reaching 98.04%, with high precision, recall, and F1-score. In conclusion, the CNN method with ResNet50 has proven effective in identifying spices based on digital images. Further development can be done by adding variations of datasets and exploring other Deep Learning architectures to improve model performance.
ALGORITMA COUNTING SORT VS ALGORITMA PENGURUTAN MODERN: ANALISIS EFISIENSI MEMORI DAN WAKTU KOMPUTASI Pujiono, Imam Prayogo; Kamal, Muhammad Rikzam; Prayogi, Arditya; Sari, Christy Atika; Ikhsanuddin, Rohmatulloh Muhamad
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6657

Abstract

Penelitian ini bertujuan untuk menganalisis efisiensi memori dan waktu komputasi algoritma Counting Sort dibandingkan dengan algoritma pengurutan modern seperti Heap Sort, Quick Sort, Merge Sort, dan Shell Sort. Fokus penelitian adalah pada dataset numerik acak dengan rentang terbatas, yang relevan untuk aplikasi praktis di bidang informatika. Penelitian ini menggunakan pendekatan eksperimental, dengan dataset berukuran 100, 1.000, dan 10.000 elemen yang dihasilkan dalam rentang 1 hingga 99, dan diimplementasikan dalam bahasa pemrograman Java untuk pengujian performa. Berdasarkan hasil eksperimen, Counting Sort mencatat waktu komputasi yang jauh lebih rendah, terutama pada dataset besar (10.000 elemen), di mana performanya hampir 6-10 kali lebih cepat dibandingkan algoritma lainnya. Namun, dalam hal efisiensi memori, Counting Sort memerlukan penggunaan memori yang lebih tinggi pada dataset kecil (100 elemen) dan sedang (1.000 elemen) dibandingkan algoritma in-place seperti Heap Sort dan Quick Sort. Pada dataset besar, penggunaan memorinya tetap kompetitif, bahkan lebih hemat dibandingkan Merge Sort. Penelitian ini menyimpulkan bahwa Counting Sort merupakan pilihan optimal untuk mengurutkan dataset numerik dengan rentang terbatas, terutama dalam aplikasi yang menuntut pengolahan data cepat dan hemat sumber daya, seperti sistem tertanam atau IoT. Temuan ini memberikan kontribusi pada pemilihan algoritma pengurutan yang lebih tepat berdasarkan karakteristik dataset.
Monk Skin Tone Classification: RMSprop vs Adam Optimizer in MobileNetV2 Aryaputra, Firman Naufal; Sari, Christy Atika; Rachmawanto, Eko Hari
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8886

Abstract

The lack of accurate and accessible skin tone classification systems poses significant challenges in personalized fashion recommendations and inclusive technology development. This study aims to develop a skin tone classification system utilizing the Monk Skin Tone (MST) scale through the implementation of Convolutional Neural Network with MobileNetV2 architecture enhanced by transfer learning techniques. The MST scale encompasses ten distinct categories providing comprehensive representation of human skin color diversity. The methodology leverages efficient MobileNetV2 architecture suitable for web deployment, transfer learning to enhance accuracy despite limited training data, and strategic dataset balancing. A dataset of 1,729 facial photographs representing the complete MST spectrum was utilized. Preprocessing involved scaling images to 224×224 pixels, normalization, and augmentation through various transformations to address class imbalance challenges. The dataset was partitioned using a 70:15:15 ratio for training, validation, and testing respectively. The system was implemented as a web platform called SkinToneAI that enables users to upload facial images for skin tone analysis and receive personalized clothing color recommendations. Evaluation demonstrated classification accuracy of 97.83% on the test dataset with a loss value of 0.1166 when using Adam optimizer, while RMSprop optimizer achieved better performance with 98.26% accuracy and 0.0548 loss value. The implemented web application successfully translates technical capabilities into practical fashion assistance. The system provides users with customized apparel color suggestions based on their identified skin tone category, effectively connecting advanced AI technology with everyday fashion needs.
Lung Segmentation in X-ray Images of Tuberculosis Patients Using U-Net with CLAHE Preprocessing Mabina, Ibnu Farid; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9869

Abstract

Tuberculosis (TB) is an infectious disease that commonly affects the lungs and remains one of the leading causes of death from infectious diseases. Early detection is essential to prevent further spread and organ damage. Chest X-ray images are one of the main methods for diagnosing TB, but image quality is often affected by low contrast and noise. This study proposes the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve X-ray image quality, combined with U-Net deep learning architecture for lung segmentation in X-ray images of tuberculosis patients. U-Net was chosen due to its excellent capability in medical image segmentation, thanks to its architectural structure that has encoder-decoder with skip connections, which allows the model to retain detailed information on high-resolution images, even on complex and noisy data. Experimental results using the Shenzhen and Montgomery datasets show that the U-Net model with CLAHE achieves Pixel Accuracy 97.96%, Recall 94.93%, Specificity 98.97%, Dice Coefficient 95.87%, and Jaccard Index (IoU) 92.07%.
A Banana Disease Detection Using MobileNetV2 Model Based on Adam Optimizer Aryanta, Muhammad Syifa; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9870

Abstract

The main objective of this study is to develop a deep learning-based disease detection system for banana plants using the MobileNetV2 architecture through a comprehensive comparison with VGG16. This study utilizes a dataset of 3,653 images categorized into 12 classes, including Aphids, Bacterial Soft Rot, Bract Mosaic Virus, Cordana, Insect Pest, Moko, Panama, Fusarium Wilt, Black Sigatoka, Yellow Sigatoka, Pestalotiopsis, and healthy specimens. The methodological framework includes architecture comparison, data balancing, preprocessing techniques, and performance evaluation. The dataset was divided with a distribution ratio of 75% for training, 15% for validation, and 10% for testing. Comparative analysis shows excellent performance of MobileNetV2 with an accuracy of 96.21% compared to 90.15% for VGG16, while maintaining a significantly smaller model size of 10.0 MB compared to 57.8 MB for VGG16. Statistical validation through the McNemar test confirms significant superiority with a p-value of 0.008. The findings of this study contribute positively to the development of agricultural technology, particularly in the development of automated systems for disease detection in banana plants.
Real-Time Braille Letter Detection System Using YOLOv8 Himawan, Reyshano Adhyarta; Rachmawanto, Eko Hari; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10060

Abstract

The purpose of this research is to create a system capable of detecting and recognizing Braille letters in real-time using the YOLOv8 algorithm for object detection, integrated with image processing technology and a user interface based on Tkinter. This system is developed to support visually impaired individuals in reading Braille text through the use of a webcam that captures and identifies Braille letters from images. The identification process is carried out by comparing the obtained images with a precompiled database of Braille letters. This research utilizes a dataset consisting of images of Braille code from letters A to Z, collected through public and private methods, with a total of 6013 images that comprehensively represent Braille letters. The model training is done using YOLOv8 to recognize Braille letter objects, with model performance evaluation using the Mean Average Precision (mAP) metric.The results of the tests show a very satisfactory model performance, with a mAP50 score of 0.98 and a mAP50-95 score of 0.789, as well as a high accuracy rate for almost all Braille letters tested. In addition, the system is equipped with a Tkinter-based graphical user interface (GUI) that allows users to operate the Braille letter detection process interactively and easily. This research proves that the YOLOv8-based object detection approach has significant potential for Braille letter recognition applications, which is expected to enhance accessibility and the independence of visually impaired individuals in reading text effectively.
A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification Kumala, Raffa Adhi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9958

Abstract

Effective waste management represents a global challenge with significant environmental and public health impacts. Despite existing waste classification systems achieving high accuracy rates, a critical research gap exists in determining optimal CNN architectures for real-world deployment constraints, particularly regarding computational efficiency versus classification accuracy trade-offs. We compared two Convolutional Neural Network (CNN) architectures MobileNetV2 and VGG16 for classifying ten types of waste using image-based analysis. Using transfer learning approach, both models were modified for waste classification tasks by adding custom layers to pre-trained models. The dataset contained 19,762 images balanced to 9,440 samples through under-sampling techniques and enhanced with data augmentation to increase variation. Results demonstrated that MobileNetV2 achieved 95.6% test accuracy with precision 0.93, recall 0.93, and F1-score 0.93, significantly outperforming VGG16's 89.13% accuracy with precision 0.91, recall 0.90, and F1-score 0.90. Beyond superior accuracy, MobileNetV2 also demonstrated higher computational efficiency with 350ms/step training time compared to VGG16's 700ms/step, and more consistent performance across all waste categories.
Optimized Visualization of Digital Image Steganography using Least Significant Bits and AES for Secret Key Encryption Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Astuti, Erna Zuni; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Yaacob, Noorayisahbe Mohd
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2252

Abstract

Data hiding is a technique used to embed secret information into a cover medium, such as an image, audio, or video, with minimal distortion, ensuring that the hidden data remains imperceptible to an observer. The key challenge lies in embedding secret information securely while maintaining the original quality of the host medium. In image-based data hiding, this often means ensuring the hidden data cannot be easily detected or extracted while still preserving the visual integrity of the host image. To overcome this, we propose a combination of AES (Advanced Encryption Standard) encryption and Least Significant Bit (LSB) steganography. AES encryption is used to protect the secret images, while the LSB technique is applied to embed the encrypted images into the host images, ensuring secure data transfer. The dataset includes grayscale 256x256 images, specifically "aerial.jpg," "airplane.jpg," and "boat.jpg" as host images, and "Secret1," "Secret2," and "Secret3" as the encrypted secret images. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Unified Average Changing Intensity (UACI), and Number of Pixels Changed Rate (NPCR) were used to assess both the image quality and security of the stego images. The results showed low MSE (0.0012 to 0.0013), high PSNR (58 dB), and consistent UACI and NPCR values, confirming both the preservation of image quality and the effectiveness of encryption for securing the secret data.
Co-Authors AA Sudharmawan, AA Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abiyyi, Ryandhika Bintang Ahmad Salafuddin Ajib Susanto Akbar, Fadhilah Aditya Akbar, Ilham Januar Alfany, Fauzan Maulana Ali, Rabei Raad Alifia Salwa Salsabila Alvian Ideastari, Nukat Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anggraeny, Tiara Anidya Nur Latifa Annisa Sulistyaningsih Anny Yuniarti Antonius Erick Handoyo Arditya Prayogi Ardyani, Salma Shafira Fatya Arfian, Aldi Azmi Ariska, Ratih Aristides Bima Wintaka Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Auni, Amelia Gizzela Sheehan Azzahra, Fidela Bambang Sugiarto Briliantino Abhista Prabandanu Budi Harjo Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Cinantya Paramita D.R.I.M. Setiadi Danang Krismawan, Andi Danang Wahyu Utomo Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Didik Hermanto Doheir, Mohamed Doheir, Mohamed Doheir, Mohamed A S Dwi Puji Prabowo Edi Faisal Egia Rosi Subhiyakto Egia Rosi Subhiyakto Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erika Devi Udayanti Erlin Dolphina Erna Daniati Erna Zuni Astuti Ery Mintorini Etika Kartikadarma Farrel Athaillah Putra Feri Agustina Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Folasade Olubusola Isinkaye Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Gusta, Muhammad Bima Hadi, Heru Pramono Haqikal, Hafidz Hartono, Matthew Raymond Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Hayu Wikan Kinasih Heru Lestiawan Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ifan Rizqa Ihya Ulumuddin, Dimas Irawan Ikhsanuddin, Rohmatulloh Muhamad Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Iqtait, Musab Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Krismawan, Andi Danang Kumala, Raffa Adhi Kurniawan, Nicholas Alfandhy Kusuma, Edi Jaya Kusuma, Mohammad Roni Kusumawati, Yupie L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Mabina, Ibnu Farid Maulana Malik Ibrahim Al-Ghiffary Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mehta Pradnyatama Meitantya, Mutiara Dolla Mohamed Doheir Mohamed Doheir Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Rikzam Kamal Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Munis Zulhusni Musfiqur Rahman Sazal Muslih Muslih Nabila, Qotrunnada Naufal, Muhammad Khanif Neni Kurniawati Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yacoob Nova Rijati Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Oktaridha, Harwinanda Oktayaessofa, Eqania Ozagastra Caluella Prambudi Parti Astuti, Yani parti astuti, yani Parti Astuti, Yani Parti Astuti1, Yani Parti Astuti1, Yani Permana langgeng wicaksono ellwid putra Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Pulung Nurtantio Andono Purwanto Purwanto Puspa, Silfi Andriana Putri Mega Arum Wijayanti Rabei Raad Ali Rabei Raad Ali Rahmalan, Hidayah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Rizky Damara Ardy Robert Setyawan Sabilillah, Ferris Tita Saifullah, Zidan Salma Shafira Fatya Ardyani Salsabila, Alifia Salwa Sania, Wulida Rizki Santoso, Bagus Raffi Saputra, Danar Bayu Adi Sari, Wellia Shinta Sari Shinta Sarker, Md Kamruzzaman Sarker, Md. Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Shelomita, Viki Ari 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 Tiara Anggraeny Titien Suhartini Sukamto Umah Nur, Raisul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yani Parti Astuti Yupie Kusumawati Zaenal Arifin Zahra Ghina Syafira Zulhusni, Munis