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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah SENTIA 2017 SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Lectura : Jurnal Pendidikan Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Infotekmesin Buletin Ilmiah Sarjana Teknik Elektro International Journal of Visual and Performing Arts Generation Journal Jurnal Mnemonic Frontier Energy System and Power Engineering Masyarakat Berdaya dan Inovasi SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Science in Information Technology Letters International Journal of Engineering, Science and Information Technology International Journal of Robotics and Control Systems ALINIER: Journal of Artificial Intelligence & Applications Ilmu Komputer untuk Masyarakat SinarFe7 Jurnal Maklumatika Applied Engineering and Technology Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Jurnal Inovasi Teknologi dan Edukasi Teknik PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO Bulletin of Social Informatics Theory and Application Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal Informatika Polinema (JIP) ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat International Journal of Mechanical, Industrial and Control Systems Engineering Journal of Engineering and Technological Sciences Jurnal ilmiah teknologi informasi Asia Jurnal Elektronika dan Telekomunikasi
Anik Nur Handayani
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Determining Quality of Service (QoS) of End-User Internet Networks with Data Sniffing and Classification Algorithms Rosyidin, Zulkham Umar; Muladi, Muladi; Handayani, Anik Nur
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1444

Abstract

The development of telecommunications technology in this world has changed very rapidly. Changes are made to access technology using the transmission media, which uses fiber optic technology, which has the advantage of being free from interference, large and fast data delivery capacity. An Internet Service Provider (ISP) is a provider of construction services and management of network infrastructure that always meets customer needs. Customer satisfaction with the services provided by ISP is also important in the era of increasingly tight market competition. Quality of Service (QoS) testing in internet networks needs to be done so that customers get optimal service. This study analyzes the quality of internet networks with fiber optic media on the end user side with the data sniffing method using Wireshark software that records video data traffic on the YouTube platform. The results of the data recording are processed using the QoS method with Throughput, Packet Loss, Delay, and Jitter parameters. The QoS assessment index is divided into Excellent, Good, Fair, and Poor classes according to the TIPHON standard. Data from these parameters is classified using the Naive Bayes, KNN, and Decision Tree methods. The results of applying the algorithms show the highest Accuracy value in the Decision Tree algorithm of 97%, while the highest Precision and Recall are in the KNN algorithm with values of 94% and 85%.
Yoga Posture Recognition and Classification Using YOLOv5 Maqbullah, Afwatul; Handayani, Anik Nur; Kurniawan, Wendy Cahya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.228

Abstract

Yoga, a centuries-old health practice from India, has gained global recognition for its benefits to physical, mental, and emotional well-being. However, incorrect execution of yoga poses can lead to injuries or diminished results. This research develops an automated system for recognizing and classifying yoga postures using YOLOv5, a state-of-the-art deep learning algorithm. YOLOv5, part of the YOLO (You Only Look Once) series, is designed for real-time object detection and offers enhanced performance through features like anchor-free detection and adaptive training strategies. The study collects a dataset of 1,000 images across 20 yoga pose categories, followed by manual annotation and training using transfer learning. Validation results show strong performance, achieving an accuracy of 90% with precision and recall scores of 0.942 and 0.941, respectively, and mAP@50 and mAP@50-95 values of 0.976 and 0.866. Despite challenges with certain poses showing lower accuracy due to variations in posture and dataset limitations, the model demonstrates robustness in detecting and classifying yoga postures effectively. This system has potential applications in artificial intelligence-driven yoga education, enabling practitioners to train independently with real-time feedback
Integration of Yolov8 And Instance Segmentation in The Chinese Sign Language (CSL) Recognition System Wijaya, Mikel Ega; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.247

Abstract

This research aims to develop an advanced recognition system for Chinese Sign Language (CSL) by integrating YOLOv8 and instance segmentation techniques. Communication through sign language is essential for the deaf community, and although CSL has been standardized in China, recognizing complex hand movements remains a significant challenge. YOLOv8 is employed for real-time object detection, while instance segmentation is used to provide more detailed analysis of hand gestures. This integration seeks to improve hand gesture recognition under varying lighting and background conditions, which is crucial for more effective communication between the deaf community and the wider society. The study evaluates the system’s performance using common metrics such as Mean Average Precision (mAP), precision, recall, and F1-score. The findings indicate that the non-segmentation model performs better than the segmentation model in terms of precision, recall, and mAP, especially when trained with a larger dataset ratio. The non-segmentation model provides faster and more accurate detection, while the segmentation model, despite using the same amount of data, shows potential for more detailed recognition of gestures. Although the segmentation model shows improvements in the F1-score with more detailed accuracy, the non-segmentation model remains superior in overall detection speed and accuracy. This research highlights the importance of integrating YOLOv8 and instance segmentation for improving CSL recognition, with better results on the non-segmentation model for more effective communication for the deaf
Optimization of Nglegena Javanese Script Recognition With Machine Learning Based on Zoning And Normalization of Feature Extraction Graciello, Manuel Tanbica; Handayani, Anik Nur; Wibawa, Aji Prasetya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.256

Abstract

Machine learning offers promising solutions for the recognition of handwritten Javanese Nglegena script, which is crucial for preserving Indonesia's cultural heritage. This study explores the application of several supervised learning algorithms-K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest-for classifying handwritten images of Nglegena Javanese script. Feature extraction is performed using a zoning technique, where each character image is divided into multiple zones (16, 25, 36, and 64) to capture local details. The extracted features are further processed using normalization methods, including Min-Max, Z-Score, and Binary normalization, to ensure uniform data distribution. The dataset, consisting of 600 images representing Javanese Nglegena characters, is split into training and testing sets using various ratios. Experimental results show that the combination of Naïve Bayes classification, 36-zone feature extraction, and Min-Max or Z-Score normalization achieves the highest accuracy of 65%. These findings demonstrate that optimizing zoning and normalization can significantly enhance the accuracy of machine learning models for Javanese script recognition. The research contributes to developing Optical Character Recognition (OCR) technology for Javanese script, supporting the digital preservation of Indonesia's historical and cultural heritage.
Comparative Analysis of OCR Methods Integrated with Fuzzy Matching for Food Ingredient Detection in Japanese Packaged Products Muhammad Zaky Rahmatsyah; Jevri Tri Ardiansah; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.257

Abstract

Advances in digital technology offer a solution to the challenges faced by foreign consumers in understanding ingredient information on Japanese food packaging, especially due to the use of Kanji, Hiragana, and Katakana characters. This study develops and reveals an allergen detection method based on Optical Character Recognition (OCR) and fuzzy match applied to Japanese food packaging. Three OCR methods—Google Vision OCR, PaddleOCR, and Tesseract OCR—were compared and evaluated using Precision, Recall, F1-Score, and Confusion Matrix metrics.The study began with the collection of food product images from bold sources, followed by text extraction using the three OCR methods. The extracted text was then cleaned and normalized before being matched with ground truth data using fuzzy match. Testing was conducted on 10 product image samples with varying quality and lighting conditions. The results showed that Google Vision OCR outperformed the others, achieving an average F1 score of 1.00, followed by PaddleOCR (0.75), and Tesseract OCR (0.30). Google Vision was the most consistent in detecting allergens such as 乳 (milk), 小麦 (wheat), and 卵 (egg). These findings suggest that the integration of OCR and fuzzy matching is effective in detecting allergens, even in the presence of textual variations and recognition errors. This study contributes to the development of automated food recommendation systems for foreign consumers, especially those who have food preferences due to allergies, religious beliefs, or personal preferences.
Comparation Analysis of Otsu Method for Image Braille Segmentation : Python Approaches Wicaksana, Ardi Anugerah; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.268

Abstract

Braille plays a crucial role in supporting literacy for individuals with visual impairments. However, converting Braille documents into digital text remains a technical challenge, particularly in accurately segmenting Braille dots from scanned images. This study aims to evaluate and compare the effectiveness of several classical image segmentation techniques—namely Otsu, Otsu Inverse, Otsu Morphology, and Otsu Inverse Morphology—in enhancing Braille image pre-processing. The methods were tested using a set of Braille image datasets and evaluated based on six quantitative image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Edge Similarity Index (ESSIM). The results show that the Otsu Morphology method achieved the highest PSNR (27.6798) and SSIM (0.5548), indicating superior image fidelity and structural preservation, while the standard Otsu method yielded the lowest MSE (113.3485).These findings demonstrate that applying morphological operations in combination with thresholding significantly enhances the segmentation quality of Braille images, supporting better accuracy in subsequent recognition tasks. This approach offers a practical and efficient alternative to deep learning models, particularly for resource-constrained systems such as portable Braille readers.
YOLOv8 Implementation on British Sign Language System with Edge Detection Extraction Romadlon, Muhammad Rizqi; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.276

Abstract

This study presents the development and implementation of a deep learning-based system for recognizing static hand gestures in British Sign Language (BSL). The system utilizes the YOLOv8 model in conjunction with edge detection extraction techniques. The objective of this study is to enhance the accuracy of recognition and facilitate communication for individuals with hearing impairments. The dataset was obtained from Kaggle and comprises images of various BSL hand signs captured against a uniform green background under consistent lighting conditions. The preprocessing steps entailed resizing the images to 640 640 pixels, implementing pixel normalization, filtering out low-quality images, and employing data augmentation techniques such as horizontal flipping, rotation, shear, and brightness adjustments to enhance robustness. Edge detection was implemented to accentuate the contours of the hand, thereby facilitating more precise gesture identification. Manual annotation was performed to generate both bounding boxes and segmentation masks, allowing for the training of two model variants: The first is YOLOv8 (non-segmentation), and the second is YOLOv8-seg (segmentation). Both models underwent training over a period of 100 epochs, employing the Adam optimizer and binary cross-entropy loss. The training-to-testing data splits utilized were 50:50, 60:40, 70:30, and 80:20. The evaluation metrics employed included mAP@50, precision, recall, and F1-score. The YOLOv8-seg model with an 80:20 split demonstrated the optimal performance, exhibiting a precision of 0.974, a recall of 0.968, and mAP@50 of 0.979. These metrics signify the model's capacity for robust detection and localization. Despite requiring greater computational resources, the segmentation model offers enhanced contour recognition, rendering it well-suited for high-precision applications. However, the generalizability of the model is constrained due to the employment of static gestures and controlled backgrounds. In the future, researchers should consider incorporating dynamic gestures, varied backgrounds, and uncontrolled lighting to enhance real-world performance.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
A Generalized Deep Learning Approach for Multi Braille Character (MBC) Recognition Widyadara, Made Ayu Dusea; Handayani, Anik Nur; Herwanto, Heru Wahyu; Yu, Tony
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13891

Abstract

Automated visual recognition of Multi Braille Characters (MBC) poses significant challenges for assistive reading technologies for the visually impaired. The intricate dot configurations and compact layouts of Braille complicate MBC classification. This study introduces a deep learning approach utilizing Convolutional Neural Networks (CNN) and compares four leading architectures: ResNet-50, ResNet-101, MobileNetV2, and VGG-16. A dataset comprising 105 MBC classes was developed from printed Braille materials and underwent preprocessing that included image cropping, brightness enhancement, character position labeling, and resizing to 89×89 pixels. A 70:20:10 data partitioning strategy was applied for training and evaluation, with variations in batch sizes (8–128) and epochs (50–500). The results demonstrate that ResNet-101 achieved superior performance, attaining an accuracy of 91.46%, an F1-score of 89.48%, and a minimum error rate of 8.5%. ResNet-50 and MobileNetV2 performed competitively under specific conditions, whereas VGG-16 consistently exhibited lower accuracy and training stability. Standard deviation assessments corroborated the stability of residual architectures throughout the training process. These results endorse ResNet-101 as the most effective architecture for Multi Braille Character classification, highlighting its potential for incorporation into automated Braille reading systems, a tool for translating braille into text or sound for future needs.
Mono Background and Multi Background Datasets Comparison Study for Indonesian Sign Language (SIBI) Letters Detection using YOLOv8 Andriyanto, Teguh; Handayani, Anik Nur; Ar Rosyid, Harits; Wiryawan, Muhammad Zaki; Azizah, Desi Fatkhi; Liang, Yeoh Wen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3462

Abstract

The research in this paper focuses on the detection of Indonesian Sign Language System (SIBI) letters using the YOLOv8 object detection model. The study compares two datasets, one with mono-background (a simple, uniform background) and another with multi-background (complex and varied backgrounds). The research aims to evaluate how the complexity of image backgrounds affects the performance of the YOLOv8 model in detecting SIBI letters This study uses a dataset consisting of 24 SIBI letters (excluding J and Z due to the complexity of their gestures), sourced from Mendeley. The dataset was processed with and without data augmentation (rotation, brightness adjustments, blur, and noise) to test the model under various conditions. Four models were trained and tested: one using mono-background images, another using augmented mono-background images, a third using multi-background images, and a final model trained on augmented multi-background images. The results showed that the YOLOv8 model performed best with the multi-background dataset, achieving a precision of 0.995, recall of 1.000, F1 score of 0.997, and mAP50 of 0.994Adding to the model made it better at generalizing, but it took longer to train. The study finds that multi-background datasets with augmentation make the model much better at finding SIBI letters in real-world settings. This makes it a promising tool for projects that aim to improve communication for deaf people in Indonesia. The study suggests that more research should be done on augmentation techniques and bigger datasets to make detection more accurate. 
Co-Authors A.N. Afandi Abdullah Iskandar Syah Achmad Hamdan Achmad Safii Achmad Safi’i Achmad Safi’i Adi Izhar Bin Che Ani Adi Prastowo, Nur Kodrad Adib Nur Sasongko Adim Firmansah Afandi, Farrel Candra Winata AFIF, ACHMAD Agung Bella Putra Utama Agusta Rakhmat Taufani Ahmad Dardiri Ahmad Kholish Fauzan Shobiry Ahmad Munjin Nasih Ahmad Nurdiansyah Ahmad Sahru Romadhon Aji Prasetya Wibawa Alifia Fitri Wahyudi Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Aqdam, Yutsabitul Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro Aripriharta Aripriharta - Ariyanta, Nadindra Dwi Asfani, Khoirudin Atmaja, Muhammad Bayu Setya Wahyu Ayu Puspita Azhryl Assagaf Aziz, Faiz Syaikhoni Azizah, Desi Fatkhi Bagaskoro, Muhammad Cahyo Baihaqi, Dimas Imam Baihaqi, Dimas Imam Baskoro Arif Widodo Bayu Prasetyo Bayu Prasetyo, Bayu Bin Che Ani, Adi Izhar Burhanuddin, Mohd Aboobaider Chalista Yulia Hazizah Chandrika, Katya Lindi Chuttur, Mohammad Yasser Damanhuri, Nor Salwa Damayanti, Farradila Ayu Damayanti, Masyita Danang Arengga Danang Arengga Wibowo Dedes, Khen Desi Fatkhi Azizah Devita Maulina Putri, Devita Maulina Dewi Aprilia Lintang Dhiyaurrahman Fakhruddin Didik Dwi Prasetya Difa Hananta Firdaus Am Dika Fikri L Dimas Wahyu Wibowo Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Dolly Indra Dwi Prihanto Dyah Lestari Dyah Rosita Anggraeni Edinar Valiant Hawali Edwin Meinardi Trianto Eka Rahayu Setyaningsih Eko Noerhayati Erwina Nurul Azizah Evania Yafie F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Faqih, Kamil Faradhila Saffa Dhamira Farah Nisa’ Salsabila Fauzi, Juwita Annisa Fauzi, Rochmad Felix Andika Dwiyanto Ferina Ayu Pusparani Fidyah Ajeng Wulandari Fukuda, Osamu Gavyn Rafael Davasco Gianika Roman Sosa Graciello, Manuel Tanbica Gunawan Budi P Guyub Raharjo Gwo-Jiun Horng Haffas Zikri Ariyandi Hakkun Elmunsyah Halimahtus Mukminna, Halimahtus Harits Ar Rasyid Harits Ar Rosyid Hariyono Hariyono Hartarto Junaedi Hary Suswanto Heru Herwanto Heru Wahyu Herwanto Hirashima, Tsukasa Hitipeuw, Emanuel Hosen, Moh I Made Wirawan Ida Ayu Putu Sri Widnyani Ihsan Al-Fikri Imam Tree Utomo Imanuel Hitipeuw Ira Kumalasari Irfan Ramadhani Irham Fadlika Jehad A. H. Hammad Jehad A.H. Hammad Jevri Tri Ardiansah Jevri Tri Ardiansah Julfikar Mawansyah Kamil Faqih Kartika Candra Kirana Kartika Kirana Kasmira, Kasmira Katya Lindi Chandrika Khurin Nabila Kinasih, Agnes Nola Sekar Kirom, M Kohei Arai Kohei Arai Kohei Arai Kohei Arai Korba, Petr Kurniawan, Wendy Cahya Kusumawardana, Arya Laili, Mery Nur Laily, F.ti Ayyu Sayyidul Laistulloh, Dika Fikri Lalu Ganda Rady Putra Langlang Gumilar Larasati, Jade Rosida Leonel Hernandez, Leonel Lestari , Widya Liang, Yeoh Wen Liang, Yoeh Wen lilis nurhayati M. Adib Nursasongko M. Nuzuluddin M. Rodhi Faiz M. Rodhi Faiz Machumu, Paul Igunda Mahamad, Abd Kadir Manga, Abdul Rachman Maqbullah, Afwatul Marga Asta Jaya Mulya Maula Zikri Renaldi Ming Foey Teng, Ming Foey Moch Haris Purwanto Moh Zainul Falah Moh. Zainul Falah Mohammad Agung Rizki Mohammad Muzayyin Amrulloh Mohammad Rizky Kurniawan Mohammad Yussril Asri Mohsen Samadi Mokh Sholihul Hadi Much. Arafat Al Mubarok Muchamad Wahyu Prasetyo Muchamad Wahyu Prasetyo Muhamad Arifin Muhamad Arifin, Muhamad Muhammad Alfan Muhammad Arifin Muhammad Hafiizh Muhammad Holqi Rizki Azhari Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Ridwan Muhammad Ulinnuha Musthofa Muhammad Younas Darvish Muhammad Zaki Wiryawan Muhammad Zaky Rahmatsyah Muladi Mumtaazah, Muhammad Athar Mutiara, Titi Nadindra Dwi Ariyanta Nailah Aliya Putri Nandang Mufti Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Naufal Rizaldi Gunawan Nisa, Khoirotun Nizaar, Roub Nor Salwa Damanhuri Norma Mustika, Soraya Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Eva Nur Halim Nur Rahma, Andika Bagus Nurul Rismayanti Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda Panji Ageng Timor Pamungkas Prasetya Widiharso Prasetya Widiharso Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Pratama, Diaz Octa Priharta, Ari Primadi, Wahyu Purnomo, Purnomo Putra Utama, Agung Bella Putri Galuh Ningtiaz Qomaria, Ulfa Rafli Indar Praja Rahman, Nukleon Jefri Nur Rahmat Samudra Anugrah, Muhammad Ramadhan, Aslan Poetra Ramadhani, Lolita Resty Wulanningrum Reza Setyawan Ria Febrianti Rini Nur Hasanah Rochmawati Rochmawati Rochmawati Rochmawati Romadlon, Muhammad Rizqi Rosa Andrie Asmara Rosyidin, Zulkham Umar Rusdha Aulia Salah Abdullah Khalil Abdulrahman Salsabila, Reni Fatrisna Saodah Omar Selly Handik Pratiwi Seno Isbiyantoro Setyaningsih, Eka Rahayu Sevilla, Felix Rafael Segundo Siti Sendari Slamet Wahyudi Slamet Wibawanto Soraya Norma Mustika Soubin Sisavath Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Suti Mega Nur Azizah Suziyani Mohamed Syaad Patmantara Syaad Patmanthara Syaghlu Natsalam Saputra Syaichul Fitrian Akbar Syamsul Bahri Taiga Haruta Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tony Yu Tran Thi Hao Triyanna Widiyaningtyas Tsukasa Hirashima Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Nur Hidayat Wahyu Primadi Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wibawa, Aji Presetya Wibowo, Kusmayanto Hadi Wicaksana, Ardi Anugerah Widiharso, Prasetya Widyadara , Made Ayu Dusea Wijaya, Mikel Ega Wirawan, Muhammad Zaki Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Yosi Kristian Yu, Tony Yudha Islami Sulistya Yuliana Melita Pranoto Yuni Rahmawati Yusuf Tri Hadi Mulyana Zaeni, Ilham Ari Elbaith Zufida Kharirotul Umma Zulkham Umar Rosyidin Zulkham Umar Rosyidin Zulkifli, Shamsul Aizam