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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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Comparative Analysis of YOLOv8 Segmentation Variants for Indonesian Sign Language (SIBI) Recognition Desi Fatkhi Azizah; Anik Nur Handayani; Aji Prasetya Wibawa; Osamu Fukuda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7500

Abstract

The Indonesian Sign Language System (SIBI) is the officially recognized communication medium for deaf communities in Indonesia, yet its limited public use continues to create barriers in education, healthcare, and public services. Automatic sign language recognition powered by artificial intelligence provides a promising pathway to reduce these inequities. This study presents a comprehensive comparative evaluation of YOLOv8 segmentation variants for SIBI recognition, aiming to identify models that stabilize accuracy and efficiency for real-time deployment. A mono-background dataset of SIBI alphabet gestures was annotated using instance segmentation, and five YOLOv8-seg models (n, s, m, l, x) were trained and tested across multiple data-split scenarios. Performance was assessed through precision, recall, F1-score, mAP50, mAP50–95, and inference time. Results show that YOLOv8m-seg consistently achieved the best trade-off (F1-score 0.972; mAP50 0.982), while YOLOv8n-seg delivered the fastest inference speed (5.163 ms), making it suitable for resource-constrained devices. Visualization further demonstrated the models’ ability to capture hand contours and distinguish gestures accurately. Beyond technical benchmarking, this research highlights the potential of YOLOv8-based SIBI recognition as an inclusive assistive technology for bridging communication gaps in schools and clinics where interpreters are often unavailable. It also identifies governance challenges, including privacy protection, misclassification risks, and equitable access, which must be addressed for actual adoption. The findings, therefore, provide not only a contribution to computer vision research but also practical guidance for policymakers and service providers, positioning SIBI recognition systems as socially embedded technologies aligned with the goals of disability inclusion and sustainable development.
Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification Kartika Candra Kirana; Anik Nur Handayani; Syaad Patmanthara; Nur Eva
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27419

Abstract

Based on an ontological perspective, there is a gap in feature representation and in binary dysgraphia classification using ResNet18, an area that has not been explored simultaneously. Thus, our contribution is an analysis of research on dysgraphia classification using ResNet18 that employs epistemological and axiological approaches. ResNet18 was chosen as the backbone of the proposed framework because it has shortcut connections that can degrade residues into useless features. As a representation of new knowledge, ResNet18 was pre-trained on ImageNet. Classification was tested on challenging word assignments, comprising 145 dysgraphia images and 188 non-dysgraphia images. Epoch trials were conducted to find the best architecture. The results showed that ResNet18 at epoch 10 achieved the best performance in binary classification, with a recall of up to 93.55%. This indicates that ResNet18 is sensitive to recognizing dysgraphia classes. Challenges outlined in this study serve as a foundation for further research.
Development of a CNN-Based Knowledge System for Rupiah Currency Authenticity Detection and Nominal Classification Ahmad Sahru Romadhon; Syaad Patmanthara; Anik Nur Handayani
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27464

Abstract

The circulation of counterfeit money in Indonesia inflicts substantial losses on the public and financial institutions. Manual verification of money is inefficient and error-prone, especially during high transaction volumes, because counterfeit bills exhibit physical characteristics nearly identical to genuine currency. To uncover counterfeit notes, an ultraviolet lamp exposes invisible ink. This research employs the Convolutional Neural Network (CNN) to detect authenticity and classify Indonesian rupiah banknotes. The CNN is trained using images of authentic banknotes captured with a camera and ultraviolet light across various denominations. The system stores the images and trains the model to identify authenticity and denomination features. Experimental results demonstrate that the proposed approach achieves high classification accuracy in distinguishing genuine and counterfeit Rupiah banknotes, as well as in recognising their respective denominations. The testing phase introduces real notes exposed to ultraviolet light, producing images that reveal invisible ink patterns. The authenticity detection achieved a 100% success rate, while the denomination recognition rates were 70% for Rp. 5,000 notes, 80% for Rp. 10,000 and Rp. 20,000 notes, and 90% for Rp. 50,000 and Rp. 100,000 notes. The system’s overall success rate is 82%.
Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction Handayani, Anik Nur; Herwanto, Heru Wahyu; Chandrika, Katya Lindi; Arai, Kohei
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish handwritten Javanese characters. The best accuracy is attained at 77.00%, with the network architecture comprising 64 input neurons, 40 hidden neurons, a learning rate of 0.003, a momentum of 0.03, and an iteration of 5000.
Classification of Indonesian Sign Language (SIBI) Using Data Mining Algorithms K-Nearest Neighbor and Random Forest Wirawan, Muhammad Zaki; Afif, Achmad; Handayani, Anik Nur; Hitipeuw, Imanuel; Fukuda, Osamu
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results show that both algorithms achieved high classification performance, with Random Forest obtaining a superior accuracy of 94.33% under Min-Max normalization compared to KNN’s 93.67%. These findings highlight the potential of Random Forest as an effective method for SIBI recognition. The research contributes to the advancement of automatic sign language recognition technology in Indonesia and supports the development of more inclusive communication tools for individuals with hearing impairments.
Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement Laistulloh, Dika Fikri; Handayani, Anik Nur; Asmara, Rosa Andrie; Taw, Phillip
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.
Multi-Objective Optimization of IoT-Based Hands-On Learning Using NSGA-II and R-NSGA-II Algorithms Muchamad Wahyu Prasetyo; Aripriharta; Anik Nur Handayani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5779

Abstract

This study aims to optimize Internet of Things-based hands-on learning using a multi-objective approach with Non-dominated Sorting Genetic Algorithm II and Reference Point–based Non-dominated Sorting Genetic Algorithm II. The optimization targets three objectives: learning efficiency, learner engagement, and practical skill improvement. A modeling-based approach is employed, and simulations are conducted to evaluate the effects of key parameters, including the number of Internet of Things devices, practicum duration, and task complexity, on learning outcomes. The results show that Reference Point–based Non-dominated Sorting Genetic Algorithm II achieves higher learning efficiency (0.571) and learner engagement (0.090), producing more balanced solutions across objectives, whereas Non-dominated Sorting Genetic Algorithm II performs better on skill improvement (0.184), particularly for high-complexity tasks. Pareto front visualizations illustrate the distribution of optimal solutions, with Reference Point–based Non-dominated Sorting Genetic Algorithm II demonstrating faster convergence and more consistent solution quality. This study contributes to the design of more efficient, effective, and adaptive Internet of Things-based learning models and provides guidance for educational institutions in selecting optimization methods aligned with specific learning priorities.
DEVELOPMENT OF A CAREER READINESS ASSESSMENT TOOL BASED ON INTERESTS, POTENTIAL, AND SOFT SKILLS FOR VOCATIONAL HIGH SCHOOL STUDENTS RIA FEBRIANTI; Wahyu Sakti Gunawan Irianto; Anik Nur Handayani
SOSIOEDUKASI Vol 15 No 2 (2026): SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universaitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/sosioedukasi.v15i2.7767

Abstract

This study aims to develop a career readiness instrument for vocational high school students in the Software Engineering and Computer and Network Engineering programs. The instrument was developed by adapting three main constructs related to career readiness: career interests, individual potential, and soft skills. This study employs an instrument development approach that includes the stages of literature review, formulation of indicators and item statements, expert validation, as well as testing the validity and reliability of the instrument. Interest indicators were adapted from the RIASEC theory proposed by Holland, potential indicators were adapted from Gardner’s Multiple Intelligences concept, while soft skills indicators were adapted from work competency concepts relevant to industry needs. The developed instrument was validated by three experts, consisting of one guidance and counseling expert and two industry practitioners serving as Chief Executive Officer (CEO) and Human Resources (HR) professionals. After undergoing a revision process, the instrument was pilot-tested on 30 vocational high school students in the Software Engineering and Computer and Network Engineering programs. The validity test results showed that most items met the validity criteria, while several items were eliminated because they had correlation values below the specified threshold. The reliability test results showed a Cronbach’s Alpha value of 0.946 for the interest instrument, 0.929 for the potential instrument, and 0.982 for the soft skills instrument, indicating a very high level of internal consistency. The results of this study indicate that the developed instruments are valid and reliable for measuring the career readiness of vocational high school students and can be used as tools in career guidance services in the field of information technology.
Incremental Effects of Augmentation Strategies on Pre-Augmented Biomedical Waste Detection Using YOLOv11n Jevri Tri Ardiansah; Ahmad Kholish Fauzan Shobiry; Anik Nur Handayani; Mohammad Muzayyin Amrulloh; Taiga Haruta
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16531

Abstract

Biomedical waste carries infectious and hazardous risk that makes accurate automated sorting valuable, and object detection offers a path toward automation, yet published studies rarely measure how much online data augmentation contributes once training images carry offline augmentation and mean Average Precision alone can conceal how augmentation shifts the balance between missed detections and false alarms.This study measures the incremental effect of Mosaic, MixUp, and Copy-Paste augmentation on YOLOv11n trained for biomedical waste detection, and verifies whether each augmentation executes as configured. We designed a 2×2×2 factorial ablation across eight configurations, trained YOLOv11n three times per configuration with different random seeds on a 14-class biomedical waste dataset and evaluated each run's best checkpoint on a held-out test set using mean Average Precision, precision, recall, and a corrected confusion matrix retaining the background class. We verified framework behavior through prediction-level identity, loss-level comparison, and instrumented tracing. Configurations combining Mosaic and MixUp raised mean Average Precision by 0.0053 over baseline, MixUp alone raised recall from 0.8950 to 0.9071, and Mosaic with MixUp raised precision to 0.9650. False negatives outnumbered false positives three to one across every configuration, identifying class-versus-background rather than inter-class confusion as the dominant failure mode. Copy-Paste showed zero measurable effect once verified, consistent with its restriction to segmentation tasks in the underlying framework. Online augmentation produces modest but distinguishable, metric-specific gains on pre-augmented biomedical waste data, and verifying framework behavior before attributing results to an augmentation strategy is necessary for reliable reporting.
Efficiency and Comparative Performance of LBP-Based Random Forest and SVM for Toraja Buffalo Classification Abdul Rachman Manga'; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Syamsul Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7536

Abstract

Toraja buffalo holds significant cultural and economic value, yet automated classification remains challenging due to subtle visual differences between types. This study evaluates the efficiency of Local Binary Pattern (LBP) combined with Random Forest (RF) and Support Vector Machine (SVM) for classifying six Toraja buffalo types: Balian, Lotong Boko, Pudu, Saleko, Todi, and Ulu. Unlike complex deep learning approaches, this research focuses on a computationally efficient framework by extracting texture features from multiple body parts: head, eyes, horns, body, and tail to capture distinctive patterns. The methodology involves multi-part feature fusion and a comparative analysis of ensemble versus kernel-based learners. Experimental results demonstrate that the LBP-Random Forest model significantly outperforms SVM, achieving a superior accuracy of 92.08% compared to 64.17%. The findings highlight that the proposed LBP-RF integration provides a robust and resource-efficient alternative for livestock image classification, balancing high diagnostic accuracy with lower computational requirements.
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