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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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Implementasi Kriptografi dengan Menggunakan Algoritma Arnold’s Cat Map dan Henon Map Candra Irawan; Eko Hari Rachmawanto
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.13.1.43312

Abstract

Algoritma kriptografi merupakan sebuah seni matematis yang diimplementasikan untuk proses enkripsi dan dekripsi, medkanisme ini mengarah untuk mengenkripsi data asli menggunakan kombinasi berbeda dari kata, angka, atau ekspresi. Keamanan data terenkripsi sepenuhnya bergantung pada dua aspek penting, yaitu : kerahasiaan kunci dan kekuatan algoritma kriptografi. Teori chaos digunakan untuk memanggil bilangan secara acak, sehingga sifat dari gambar seperti redudansi data atau tingkat korelasi tinggi, hubungan antara nilai piksel dan biasanya berukuran besar akan diproses sehingga menghasilkan enkripsi menjadi kecil serta semakin acak bilangan yang dimunculkan semakin baik pula keamanannya. Macam-macam metode pada teori ini antara lain :  Henon Map, Logistic Map, Arnold’s Cat Map, Duffing Map, Tent Map, Beta Map dan sebagainya. Pada penilitian ini akan menggunakan dua metode yaitu Arnold’s Cat Map dan Henon Map untuk proses enkripsi dan dekripsi. Serta untuk menutupi kekurangan Henon Map jika citra awalnya homogen, citra hasil enkripsinya akan sama dengan data awal, maka pada penelitian ini akan di klasifikasikan menjadi citra homogen dan heterogen dengan ekstraksi ciri orde 1 menggunakan standar deviasi dan nilai mean.
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.
Performance Enhancement of Mushroom Species Classification via Modified InceptionV3 Muhammad Khanif Naufal; Christy Atika Sari; Eko Hari Rachmawanto; Musab Iqtait
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.73005

Abstract

Mushrooms encompass a very large number of species, and some of them are toxic to humans. It is very difficult to classify mushroom species quickly and accurately, especially for common individuals who often encounter wild mushrooms in nature. To address this problem, this study envisioned an automated mushroom species classification system using deep learning methods and the InceptionV3 model. This model was chosen because it is highly generalizable, performs well with challenging images, and is precise for most image-based classification tasks. The dataset comprises 18 mushroom species and was created from a Kaggle version. Data balancing, preprocessing, data augmentation, and model training constitute the research work. The dataset has been divided into 70% training, 15% validation, and 15% test. The training results show that the model achieves 81.35% accuracy in identifying mushroom species. The study contributes to the development of AI-based image recognition technology that can help humans find mushrooms more rapidly and securely.
An integration of quantum systems using BB84 for enhanced security in aeroponic smart farming Christy Atika Sari; Purwanto Purwanto; Eko Hari Rachmawanto; Abdul Syukur
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26450

Abstract

Modern aeroponic systems leverage internet of things (IoT) technology for automated control of climate, lighting, and nutrient delivery, rendering them susceptible to unauthorized access and network attacks. Such disruptions can lead to financial losses and impair agricultural productivity by altering essential growth conditions. To mitigate these risks, robust security measures including encryption and firewalls are essential, alongside continuous monitoring and updates to combat evolving threats. Addressing cyber threats in urban aeroponic systems, implementing quantum encryption emerges as a promising solution. Quantum key distribution (QKD) ensures highly secure encryption keys using quantum states that change upon eavesdropping, thereby thwarting intrusion attempts effectively. Integrating quantum encryption in aeroponic control systems safeguards data integrity and operational continuity against cyber threats, bolstering urban agriculture resilience. Our findings demonstrate the efficacy of quantum BB84 protocol integrated with API for Eve’s security. Quantum bit error rate (QBER) measurements revealed minimal interference (0.015) for Alice and Bob, contrasting with higher initial QBER (up to 1.0) for Eve, indicative of intrusion attempts. Histogram analysis further underscored quantum security’s effectiveness in identifying and mitigating breaches. For future research, enhancing quantum encryption protocols and integrating advanced detection mechanisms will be essential.
A high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification Ibnu Utomo Wahyu Mulyono; Eko Hari Rachmawanto; Christy Atika Sari; Md Kamruzzaman Sarker
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26017

Abstract

This research paper provides a detailed examination of different convolutional neural network (CNN) structures used in Covid-19 image classification tasks. The study thoroughly investigates the performance of classic CNN, visual geometry group (VGG), and ResNet-50 architectures across a variety of datasets. The analysis focuses on evaluating the efficacy of each architecture by considering metrics such as accuracy, precision, recall, and F1-Score. The experimental results reveal that the ResNet-50 architecture achieves the highest performance with an accuracy rate of 96.63%, outperforming both VGG and classic CNN models. This finding emphasizes the importance of architectural choices and hyperparameter selection in achieving optimal performance in image classification tasks. The combination of the ResNet-50 architecture with the Adam optimizer demonstrates its effectiveness in improving classification accuracy. These findings contribute to the field of deep learning by providing valuable insights into the performance analysis of CNN architectures and highlighting the significance of selecting appropriate hyperparameters for optimal model performance. The selection of VGG and ResNet-50 architectures was based on their strong feature extraction capabilities, proven state-of-the-art performance, and their suitability for transfer learning. VGG and ResNet-50 also have widely available pre-trained models, facilitating their usage and experimentation.
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization Heru Agus Santoso; Eko Hari Rachmawanto; Adhitya Nugraha; Akbar Aji Nugroho; De Rosal Ignatius Moses Setiadi; Ruri Suko Basuki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14744

Abstract

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
Brain tumor segmentation using multi-level Otsu thresholding and Chan-Vese active contour model Heru Pramono Hadi; Edi Faisal; Eko Hari Rachmawanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.21679

Abstract

Research on brain tumor segmentation has been developed, ranging from threshold-based methods to the use of the deep learning algorithm. In this study, we proposed a region-based brain tumor segmentation method, namely the active contour model (ACM). Tumor segmentation was carried out using fluid attenuated inversion recovery (FLAIR) modality magnetic resonance imaging (MRI) image data obtained from the multimodal brain tumor image segmentation benchmark (BRATS) 2015 dataset of 86 images. The initial stage of our segmentation method is to find the initial initialization point/area for the ACM algorithm using multi-level Otsu thresholding, with the level used in this study is 3 levels. After the initial initialization area has been obtained, the segmentation process is continued with ACM which explores the tumor area to obtain a full and accurate tumor area result. The results of this study obtained dice similarity (DS) for our study of 0.7856 with a total time required of 28.080722 seconds, which better than other method that we also compared with ours, 0.75 compared to 0.78 in term of DS.
Optimation of image encryption using fractal Tromino and polynomial Chebyshev based on chaotic matrix Elkaf Rahmawan Pramudya; Moch. Arief Soeleman; Cahaya Jatmoko; Eko Hari Rachmawanto; Aris Marjuni; Pulung Nurtantio Andono; Folasade Olubusola Isinkaye
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26080

Abstract

Image encryption is a critical process aimed at securing digital images, safeguarding them from unauthorized access, tampering, or viewing to ensure the confidentiality and integrity of sensitive visual information. In this research, we integrate polynomial Chebyshev, fractal Tromino, and substitution S-box methods into a comprehensive image encryption approach. Our evaluation focuses on standardized 256×256-pixel images of Lena, Peppers, and Baboon, assessing key performance metrics like mean squared error (MSE), peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel changes rate (NPCR), and entropy. The results reveal varying encryption quality across images, with Lena exhibiting the highest MSE (4702) and the lowest PSNR (12.89 dB). However, UACI, NPCR, and entropy values remain consistent across all images, indicating the proposed method’s stability concerning changing intensity, pixel alterations, and entropy levels. These findings contribute valuable insights into the effectiveness of the proposed encryption method, providing a foundation for further exploration and optimization in the field of cryptographic research. For future research direction, it is recommended to explore the impact of varying image sizes and types on the proposed method’s performance. Additionally, by focusing on the area of cryptographic threats, further analysis of the algorithm’s resistance against advanced attacks and its computational efficiency would be beneficial.
A good result of brain tumor classification based on simple convolutional neural network architecture Eko Hari Rachmawanto; Christy Atika Sari; Folasade Olubusola Isinkaye
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25863

Abstract

Brain tumor disease has become a topic of research whether it is in the case of segmentation or classification. For the case of classification, the types of brain tumors that are grouped generally consist of high-grade glioma (HGG) and low-grade glioma (LGG) tumors. In this research we are doing, we propose a method for classifying 2 types of tumors, namely HGG and LGG, using the convolutional neural network (CNN) algorithm which is trained and will be tested against the 2018 and 2019 brain tumor segmentation (BRATS) datasets which have 4 modalities, namely fluid-attenuated inversion recovery (FLAIR), T1, T1ce, and T2 totaling 2048 images. The CNN algorithm was chosen because it can directly receive input in the form of a magnetic resonance image (MRI) with the feature extraction process as well as the classification algorithm. By forming a simple CNN algorithm architecture with only 3 convolutional layers which have an input layer in the form of a full MRI image with dimensions of 240×240×3, we obtained a relatively high accuracy result of 94.14%, it can even be said to be better than similar methods but with more complicated architecture.
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