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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 Journal of Soft Computing Exploration
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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Orchid Species Classification Using the DenseNet121 Deep Learning Model with a Data Imbalance Handling Approach Akbar, Fadhilah Aditya; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11458

Abstract

For conservation, commercial cultivation, and scientific research, accurate identification of orchid species often requires specialized expertise. In this study, the DenseNet121 deep learning architecture was employed to develop an automated classification system for four popular orchid species. DenseNet121 was selected for its ability to extract complex hierarchical features and its strong performance on limited-scale datasets. The initial dataset comprised 1,935 images of Phalaenopsis, Cattleya, Dendrobium, and Vanda orchids. However, after manual removal of duplicate images, only 1,658 images remained, revealing significant class imbalance. The undersampling method was applied to balance each class to 248 samples. The dataset was then split into 75% training, 15% validation, and 10% testing, and enhanced through data augmentation techniques such as rotation, flipping, brightness variation, width shift, height shift, and zoom. The final model achieved 97.00% accuracy with class-specific performance ranging from 92.59% to 100% accuracy across different orchid species. This research can serve as a foundation for developing mobile or web applications to assist researchers, farmers, and orchid enthusiasts in accurately identifying orchid species, while supporting conservation efforts for orchid biodiversity in Indonesia.
Automatic License Plate Detection System with YOLOv11 Algorithm Kurniawan, Nicholas Alfandhy; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11484

Abstract

The increasing number of motor vehicles in Indonesia demands technological solutions to enhance efficiency and security, particularly in automatic license plate recognition systems. This study aims to develop an automatic license plate detection system using the YOLOv11 algorithm to detect license plates and their characters in real-time. The research methodology includes collecting datasets from Kaggle, RoboFlow, and manual acquisition, followed by annotation, data augmentation, model training, and interface development using Tkinter and OpenCV. The dataset comprises 4000 license plate images and 3000 characters images, divided for training, validation, and testing. Evaluation results demonstrate strong model performance, with precision of 0.891, recall of 0.911, mAP50 of 0.906, and mAP50-95 of 0.631 for license plate detection, and precision of 0.889, recall of 0.912, mAP50 of 0.907, and mAP50-95 of 0.629 for character detection. Real-time testing showed that 12 out of 12 license plates were successfully recognized, influenced by lighting conditions, distance, and plate orientation. This study produced an efficient system for parking security, with potential for further development.
Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques Rizky Damara Ardy; Anny Yuniarti; Christy Atika Sari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1255

Abstract

This study aims to enhance face detection performance in 360-degree videos by utilizing advanced image augmentation techniques with the YOLOv8 algorithm, which is effective for real-time object detection. Acknowledging the unique challenges posed by equirectangular projection, this research introduces a novel equirectangular augmentation method specifically designed for this medium. Our findings demonstrate a remarkable 1.346% improvement in detection accuracy in Equirectangular Projection (ERP) settings compared to default YOLOv8 augmentation strategies. This significant enhancement not only addresses the geometric distortions inherent in panoramic video formats but also emphasizes the critical need for tailored augmentation approaches to improve face detection in complex environments. By showcasing the effectiveness of these customized methods, this research contributes to the growing field of deep learning applications for immersive video technologies, with implications for sectors like security, virtual reality, and interactive media. Ultimately, this work highlights the potential of innovative augmentation techniques to ensure robust face detection in challenging visual contexts.
Quality Improvement for Invisible Watermarking using Singular Value Decomposition and Discrete Cosine Transform Danang Wahyu Utomo; Christy Atika Sari; Folasade Olubusola Isinkaye
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3744

Abstract

Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.
Multi-Level Secure Image Cryptosystem Using Logistic Map Chaos: Entropy, Correlation, and 3D Histogram Validation Anidya Nur Latifa; Christy Atika Sari; Eko Hari Rachmawanto; Md Kamruzzaman Sarker
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.74537

Abstract

This study presents a multi-level image encryption framework that combines password dependent SHA-256 key generation with a Logistic Map-based chaotic mechanism, supporting three operational modes: Speed, Balanced, and Security. The system is designed for scalability and robustness across diverse image sizes, achieving up to 27 percent faster encryption than AES on 1024×1024 images while maintaining high cryptographic strength. Experimental results show strong randomness with entropy reaching up to 7.98 bits per pixel, reduced adjacent pixel correlation below 0.01, and high resistance to differential attacks with NPCR above 99.6 percent and UACI around 33.4 percent. Structural integrity after decryption is also preserved with SSIM scores above 0.98. Compared to existing chaos based methods such as those proposed by Arif et al. and Riaz et al., the proposed system offers superior entropy performance, enhanced flexibility through multi-mode encryption, and broader resolution support up to 2048×2048 pixels. Comprehensive evaluations using entropy, correlation, PSNR, SSIM, XOR, and 3D histogram analysis confirm the method’s effectiveness. These findings highlight the system’s suitability for real-time, secure image transmission in environments such as IoT, medical imaging, and embedded applications.
Kombinasi Least Significant Bit (LSB-1) Dan Rivest Shamir Adleman (RSA) Dalam Kriptografi Citra Warna Christy Atika Sari; Wellia Shinta Sari
Jurnal Masyarakat Informatika Vol 13, No 1 (2022): May 2022
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.1.43314

Abstract

Semakin berkembangnya internet dan aplikasi jaringan, membuat seseorang dapat bertukar pesan, informasi maupun data tanpa dibatasi oleh waktu dan jarak. Dengan adanya itu maka aspek keamanan dari data yang ditukarkan melalui internet dan aplikasi jaringan juga meningkat. Salah satu kategori keamanan komputer utama yang mengkonversi informasi dari bentuk normal ke bentuk yang tidak terbaca adalah kriptografi. Algoritma kriptografi yang popular saat ini adalah Chiper Block Chaining (CBC), algoritma ini merupakan metode kriptografi yang cukup handal dan stabil Algoritma ini paling umum digunakan pada protocol internet TLS dan IPsec. Teknik steganografi juga bisa digunakan untuk menjaga keamanan dan kerahasiaan pesan. Salah satu konsep steganografi adalah LSB. Perlunya digunakan metode pendeteksian tepi untuk memperbesar kapasitas penyisipan lebih banyak pada piksel tepi sehingga dapat menampung pesan lebih banyak tanpa terdeteksi, karena konsep LSB masih lemah. Metode Sobel adalah pendeteksian tepi yang paling umum dan merupakan metode yang terbaik untuk mendeteksi tepi pada grey-level. Setelah dilakukan pengujian menggunakan PSNR dan MSE. hasil penggabungan metode CBC dan LSB-Sobel ini dapat merahasiakan pesan dengan baik dan memiliki kualitas stego-image yang cukup tinggi.
A Combination of SHA-256 and DES for Visual Data Protection Aristides Bima Wintaka; Christy Atika Sari; Eko Hari Rachmawanto; Rabei Raad Ali
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.72615

Abstract

This study employs SHA-256 and DES algorithms to safeguard visual data through encryption and decryption processes. Research findings demonstrate that this method provides robust security with image histograms that are difficult to recognize and randomly encrypted. The MSE and PSNR values approximate 105 and 48, indicating that the decryption image quality closely resembles the original due to these relatively high values, which are considered excellent. The SSIM value of 1 which indicates no difference in structure, luminance, or contrast between images. Entropy and N.C values approach 8 and 0.92, respectively, suggesting pixel complexity within image with favorable pixel distribution. This technique prove effective for protecting confidential images and digital documents.
Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position Zahra Ghina Syafira; Christy Atika Sari; Ibnu Utomo Wahyu Mulyono; Feri Agustina; Suprayogi Suprayogi; Mohamed Doheir
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73967

Abstract

Lung cancer is one of the leading causes of death in the world, so early detection is very important to increase the chances of patient recovery. This study aims to develop a method for identifying lung cancer types using Convolutional Neural Network (CNN) combined with Regionprops segmentation technique to determine the position of cancer in CT scan images. The dataset used consists of 1,294 CT scan images classified into three classes, namely Benign, Malignant, and Normal, with variations in the ratio of training and testing data: 80:20, 70:30, 60:40, 50:50, and 40:60. The CNN method is used to perform classification, while the Regionprops segmentation technique is applied to determine the position of the cancer. The results showed that the model with a data ratio of 80:20 achieved the highest accuracy of 99.54%, indicating a very good generalization ability of the model. The Regionprops segmentation technique successfully separated the nodule area in the CT scan image clearly, thus providing more detailed information regarding the position of the cancer. The conclusion of this study shows that the combination of CNN and Regionprops segmentation methods is effective in detecting and analyzing lung cancer and has the potential to be used as a diagnostic tool in the medical field. This study recommends further testing with a larger dataset and optimization of model parameters to improve classification and segmentation performance.
Fitur Esktraksi LBP dan Naive Bayes dalam Klasifikasi Jenis Pepaya Berdasarkan Citra Daun Christy Atika Sari; Eko Hari Rachmawanto
Jurnal Masyarakat Informatika Vol 12, No 2 (2021): November 2021
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.12.2.42222

Abstract

Tanaman merupakan bagian terpenting dalam kehidupan makhluk hidup sebagai oksigen untuk bernafas, selain itu juga digunakan sumber makanan, bahan bakar, obat-obatan dan masih banyak lagi manfaatnya. Salah satunya tanaman buah pepaya, bisa digunakan untuk bahan makanan maupun obat-obatan. Tanaman buah pepaya ini memiliki banyak jenis dan bisa diklasifikasikan berdasarkan bentuk daunnya. Jenis daun buah papaya yang digunakan dalam penelitian ini, yaitu : daun buah pepaya Sumatera, daun buah pepaya California, daun buah pepaya Hawai, daun buah pepaya cibinong dan daun buah pepaya Bangkok. Jumlah dataset yang digunakan adalah 150 citra dan akan dibagi menjadi 5 kelas yang terdiri dari 25 data training dan 5 data testing masing-masing kelas. Proses klasifikasi ini menggunakan metode Local Binary Pattern untuk ektraksi fitur dan metode Naïve Bayes Classifier sebagai metode klasifikasinya. Metode Local Binary Pattern operator sederhana dan efisien untuk menggambarkan pola gambar local dan mendapatkan hasil yang baik dalam tekstur pengambilan gambar. Sedangkan metode Naïve Bayes Classifier adalah metode yang paling sederhana dengan menggunakan peluang yang ada, dimana tempatnya mengasumsikan bahwa setiap variabel adalah independensi. Berdasarkan hasil pengujian yang dilakukan, penggunaan Naïve Bayes Classifier ditambah dengan ekstraksi fitur Local Binary Pattern didapatkan nilai akurasi 96% pada percobaan pertama dan 93% pada percobaan kedua.
A Comparative Analysis of Convolutional Neural Network (CNN): MobileNetV2 and Xception for Butterfly Species Classification Mehta Pradnyatama; Christy Atika Sari; Eko Hari Rachmawanto; Hussain Md Mehedul Islam
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.72957

Abstract

This study aims to compare the effectiveness and efficiency of two convolutional neural network architectures, MobileNetV2 and Xception, for automated butterfly species classification. As biodiversity monitoring gains significance, effective species identification technologies are crucial for conservation. The research utilized a dataset of 100 butterfly species with 12,594 training images and 1,000 validation and test images. Transfer learning with pre-trained ImageNet weights was implemented, and both models were enhanced with custom classification layers. Data augmentation and class weighting mitigated dataset imbalance issues. Experimental results show Xception attained 93.40% test accuracy compared to MobileNetV2's 93.20%. These high accuracy rates were achieved through effective transfer learning that preserved general feature extraction capabilities, comprehensive class balancing techniques, and carefully tailored learning rate strategies for each architecture. Despite minimal performance difference, MobileNetV2 offers significant computational efficiency advantages with 4.15M parameters compared to Xception's 25.27M, while Xception provides marginally better classification. This study contributes to entomological research and highlights trade-offs between model complexity and performance in fine-grained classification tasks, supporting implementation decisions for butterfly identification systems in practical applications.
Co-Authors AA Sudharmawan, AA Abdul Qohhar Abdul Syukur 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 Arfian, Aldi Azmi Ariq Arsalan Aris Marjuni Aristides Bima Wintaka Ariza, Said Fachri Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Auni, Amelia Gizzela Sheehan 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 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 Gede Pradistya Evan Aryaputra Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Gusta, Muhammad Bima Hadi, Heru Pramono Haqikal, Hafidz Haris Pujianto Hartono, Matthew Raymond Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Hayu Wikan Kinasih Heru Lestiawan Hidayah Rahmalan Hidayah Rahmalan Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono 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 Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Kholifatun, Isnaeni 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 Maxentia Kathleen Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Megan Febriana Putri Johana Mehta Pradnyatama Meitantya, Mutiara Dolla Mohamed A. S. Doheir Mohamed Doheir Mohamed Doheir Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Eswin Bakkar Muhammad Khanif Naufal Muhammad Rikzam Kamal Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Munandar Rahmat Prayogi Munis Zulhusni Musab Iqtait Musab Iqtait Musfiqur Rahman Sazal Muslih Muslih Nabila, Qotrunnada Neni Kurniawati Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yaacob Noorayisahbe Mohd Yacoob Nova Rijati Nova Rijati Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Oktaridha, Harwinanda Oktayaessofa, Eqania Ozagastra Caluella Prambudi 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 Purwanto Purwanto Puspa, Silfi Andriana Putri Mega Arum Wijayanti Raafiandy Wirawan Avicenna Rabei Raad Ali Rabei Raad Ali Rahmalan, Hidayah Raihan Ramadhan Hamzah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Rizka Dian Safitri Rizky Damara Ardy Robert Setyawan Sabilillah, Ferris Tita Saifullah, Zidan Salma Shafira Fatya Ardyani Sania, Wulida Rizki Santoso, Bagus Raffi 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