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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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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.
Waste Image Classification Using EfficientNet B4 with MD5 and pHash Data Duplication Analysis on Two Datasets Abdul Qohhar; Christy Atika Sari; Hidayah Rahmalan
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.v10i4.13510

Abstract

Waste sorting automation through deep learning is an important approach to support sustainable waste management systems. However, many studies still overlook dataset quality issues, especially exact and near duplicate images that can cause information leakage and overly optimistic evaluation metrics. This study proposes a waste image classification pipeline that integrates an explicit data quality analysis stage using MD5 hashing for exact duplicate detection and perceptual hashing (pHash) for near duplicate detection, followed by fine tuned EfficientNet B4 as the classification backbone. Experiments are conducted on two public datasets with distinct characteristics: Garbage Classification V2 (6 classes, 9,421 images) and RealWaste (9 classes, 4,749 images). With a Hamming distance threshold τ≤2, the pHash cleansing identifies zero duplicates in Dataset 1 and only three near duplicates (0.06%) out of 1,404,077 compared pairs in Dataset 2, confirming no evidence of image-duplication-based information leakage in either dataset. EfficientNet B4 achieves 97.77% test accuracy with a macro F1 Score of 0.9768 on Dataset 1 and 93.26% accuracy with a macro F1 Score of 0.9379 on Dataset 2, demonstrating consistent performance across these two datasets with different numbers of classes, data volumes, and visual heterogeneity. These findings should be interpreted as evidence of robustness within the scope of the two evaluated datasets, rather than as a claim of generalization to unseen, external, or cross-domain waste image datasets.
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.
Support Vector Machine and Multilayer Perceptron Optimization for Banana Leaf Disease Classification Using Hue Saturation Value and Gray Level Co-occurrence Matrix Haris Pujianto; Christy Atika Sari; Musab Iqtait
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.v10i4.13603

Abstract

Banana leaf disease poses a significant threat to the quality and productivity of banana plants. Conventional disease identification relies heavily on expert knowledge and is time-consuming, highlighting the need for an automated and efficient solution. This study presents a classification system for banana leaf diseases by comparing the performance of Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP). The dataset employed was the Banana Leaf Spot Diseases (BananaLSD) dataset, comprising four disease categories: healthy, cordana, pestalotiopsis, and sigatoka. Preprocessing steps included image resizing, data cleaning, feature extraction using Hue Saturation Value (HSV) color features and Gray Level Co-occurrence Matrix (GLCM) texture features, and data normalization via StandardScaler. Experimental results demonstrate that SVM achieved an accuracy of 94.34%, outperforming MLP which reached 93.40%. These findings confirm that the integration of HSV and GLCM features with SVM constitutes an effective approach for automated banana leaf disease classification, offering a promising foundation for intelligent plant health monitoring systems.
Comparative Analysis of MobileNetV2, Xception, and EfficientNet for Batik Pattern Classification Maxentia Kathleen; 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.v10i4.13639

Abstract

Indonesia boasts a rich cultural heritage in the form of batik, which has gained international recognition. However, the wide variety of batik motifs makes visual identification difficult for both locals and tourists. The limitations of manual observation and a lack of understanding regarding the significance behind each design pose a significant barrier to cultural preservation in the digital age. This study aims to conduct a comparative analysis of Deep Learning models to identify the most effective architecture for automatically classifying batik motifs. The method employed involved comparing three Convolutional Neural Network architectures: MobileNetV2, Xception, and EfficientNet. This study was conducted using a dataset containing 3,700 batik images that had been processed through a careful data distribution process. The primary objective of this evaluation is to find the optimal balance between high classification accuracy and efficient use of computational resources, enabling implementation on platforms with limited specifications. The results of this study indicate that these models can recognize complex batik patterns with outstanding validation accuracy rates ranging from 98% to 100%. These findings provide a strong technical foundation for selecting the most appropriate model architecture for developing intelligent systems aimed at preserving traditional batik. This study also shows that MobileNetv2 is the most optimal model architecture because it achieves a perfect balance between 100% accuracy and the fastest total inference time of 36.74 seconds, with an average inference time per sample of 0.0525 seconds. It is hoped that this research will make the batik identification process faster, more accurate, and accessible to the general public, thereby supporting the sustainability of Indonesia’s cultural heritage.
A hybrid VGG16 and random forest model for multi-class ischemic heart disease detection via ecg image analysis Rizka Dian Safitri; Christy Atika Sari; Noorayisahbe Mohd Yaacob
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.121

Abstract

A cardiac illnesses, mainly ischemic cardiac conditions, are a primary factor behind global fatality rates. Clinical diagnosis generally relies on manual interpretation of Electrocardiogram (ECG) signals, which is subjective and time-consuming, compounded by the challenge of limited access to raw ECG data on commercial devices. This research focuses on designing an automated diagnosis system based on ECG image analysis using the VGG16 Convolutional Neural Network (CNN) architecture through a knowledge transfer method. A total of 8,268 ECG images from 689 patients, divided into three categories Normal, Abnormal Heartbeat, and History of Myocardial Infarction (MI) were evaluated in this study. Test results demonstrated that the VGG16 architecture integrated with Random Forest produced the most optimal performance, with a test accuracy of 93.27%, an F1-Score of 93.25%, along with an Area Under the Curve (AUC) value of 0.991. This combined model successfully detected Normal images without any prediction errors. This computational image feature extraction approach has proven effective in reducing diagnostic subjectivity and holds strong potential for application as a fast and consistent clinical decision support system across various healthcare facilities. Nevertheless, multicenter validation on a more diverse population is still required to ensure the model's clinical generalizability.
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
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