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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 Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan 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 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 Journal of Engineering and Technological Sciences Jurnal ilmiah teknologi informasi Asia
Anik Nur Handayani
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Revealing Interaction Patterns in Concept Map Construction Using Deep Learning and Machine Learning Models F.ti Ayyu Sayyidul Laily; Didik Dwi Prasetya; Anik Nur Handayani; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4641

Abstract

Concept maps are educational tools for organizing and representing knowledge, enhancing comprehension, and memory retention. In concept map construction, much knowledge can be utilized. Still, concept map construction is complex, involving actions that reflect a user’s thinking and problemsolving strategies. Traditional methods struggle to analyze large datasets and capture temporal dependencies in these actions. To address this, the study applies deep learning and machine learning techniques. This research aims to evaluate and compare the performance of Long Short-Term Memory (LSTM), K-Nearest Neighbors (K-NN), and Random Forest algorithms in predicting user actions and uncovering user interaction patterns in concept map construction. This research method collects and analyzes interaction logs data from concept map activities, using these three models for evaluation and comparison. The results of this research are that LSTM achieved the highest accuracy (83.91%) due to its capacity to model temporal dependencies. Random Forest accuracy (80.53%), excelling in structured data scenarios. K-NN offered the fastest performance due to its simplicity, though its reliance on distance-based metrics limited accuracy (70.53%). In conclusion, these findings underscore the practical considerations in selecting models for concept map applications; LSTM demonstrates effectiveness in predicting user actions and excels for temporal tasks, while Random Forest and K-NN offer more efficient alternatives in computational.
Enhancing Semantic Similarity in Concept Maps Using LargeLanguage Models Muhammad Zaki Wiryawan; Didik Dwi Prasetya; Anik Nur Handayani; Tsukasa Hirashima; Wahyu Styo Pratama; Lalu Ganda Rady Putra
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4727

Abstract

This research uses advanced models, Generative Pre-trained Transformer-4 and Bidirectional Encoder Representations from Transformers, to generate embeddings that analyze semantic relationships in open-ended concept maps. The problem addressed is the challenge of accurately capturing complex relationships between concepts in concept maps, commonly used in educational settings, especially in relational database learning. These maps, created by students, involve numerous interconnected concepts, making them difficult for traditional models to analyze effectively. In this study, we compare two variants of the Artificial Intelligence model to evaluate their ability to generate semanticembeddings for a dataset consisting of 1,206 student-generated concepts and 616 link nodes (Mean Concept = 4, Standard Deviation = 4.73). These student-generated maps are compared with a reference map created by a teacher containing 50 concepts and 25 link nodes. The goal is to assess the models’ performance in capturing the relationships between concepts in an open-ended learning environment. The results show that demonstrate that Generative Pretrained Transformers outperform other models in generating more accurate semantic embeddings. Specifically, Generative Pre-trained Transformer achieves 92% accuracy, 96% precision, 96% recall, and 96% F1-score. This highlights the Generative Pretrained Transformer’s ability to handle the complexity of large, student-generatedconcept maps while avoiding overfitting, an issue observed with the Bidirectional Encoder Representationsfrom Transformer models. The key contribution of this research is the ability of two complex models and multi-faceted relationships among concepts with high precision. This makes it particularly valuable in educational environments, where precise semantic analysis of open-ended data is crucial, offering potential for enhancing concept map-based learning with scalable and accurate solutions.
Grid-Calibrated Patch Learning for Braille Multi-Character Recognition Widyadara, Made Ayu Dusea; Handayani, Anik Nur; Herwanto, Heru Wahyu; Yu, Tony; Mulya, Marga Asta Jaya
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15199

Abstract

The approach presents a multi braille character (MBC) recognition system for Indonesian syllablesdesigned to address real-world imaging variations. The proposed framework formulates 105-class visual classification task, where each class represents a two-character Braille unit. This design aims to preserve inter-character spatial relationships and reduce error propagation commonly found in single-character segmentation approaches. A carefully constructed dataset undergoes spatial pre-processing stages, including rotation normalization, grid assignment, and multicell cropping, resulting in uniform 89×89 pixel image patches that ensure geometric consistency across samples. To enhance model generalization under varying illumination conditions, single-dimension photometric augmentation is applied exclusively during training, including brightness (±25%), exposure (±20%), saturation (±40%), and hue (±30%). ResNet-101 is adopted as the backbone architecture based on prior comparative studies conducted on the same dataset, demonstrating its effectiveness in capturing fine-grained Braille dot shadow patterns. The network is trained for 300 epochs with a batch size of 32 under consistent experimental settings, and performance is evaluated using a confusion-matrix-based framework with overall accuracy as the primary metric. Experimental results indicate that moderate photometric reductions significantly improve recognition performance by preserving critical micro-contrast cues. In particular, an exposure reduction of −20% achieves the best balance between accuracy (86.13%) and training efficiency (14.12 minutes), outperforming the non-augmented baseline (74.37%, 22.10 minutes). A hue reduction of −30% further improves robustness to ambient color variations, while aggressive positive adjustments degrade performance due to structural distortion. These findings confirm the effectiveness of the proposed MBC framework for practical Braille recognition in real-world environments.
Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification Kirana, Kartika Candra; Handayani, Anik Nur; Patmanthara, Syaad; Eva, Nur
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 Romadhon, Ahmad Sahru; Patmanthara, Syaad; Handayani, Anik Nur
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%.
Comparative Analysis of Speech-to-Text APIs for Supporting Communication of the Deaf Community Anik Nur Handayani; Hariyono Hariyono; Ahmad Munjin Nasih; Rochmawati Rochmawati; Imanuel Hitipeuw; Harits Ar Rosyid; Jevri Tri Ardiansah; Rafli Indar Praja; Ahmad Nurdiansyah; Desi Fatkhi Azizah
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.327

Abstract

Hearing impairment can have a profound impact on the mental and emotional state of sufferers, as well as hinder communication and delay in accessing information directly that relies on interpreters. Advances in assistive technology, especially speech recognition systems that are able to convert spoken language into written text (speech-to-text). However, its implementation faces various challenges related to the level of accuracy of each speech-to-text Application Programming Interface (API), thus requiring an appropriate deep learning model. This study serves to analyze and compare the performance of speech-to-text API services (Deepgram API, Google API and Whisper AI) based on Word Error Rate (WER) and Words Per Minute (WPM), to determine the most optimal API in a web-based real-time transcription system using the JavaScript programming language and Glitch.com. The three API services were tested by calculating their error rates and transcription speeds, then evaluated to see how low the error accuracy rate was and how high the transcription speed was. On average, Whisper AI had a WER of 0% across all word categories, but its speed was lower than the other two APIs. Deepgram API displayed the best balance between accuracy and speed, with an average WER of 13.78% and 67 WPM. Google API performed stably, but its WER value was slightly higher than Deepgram API. In conclusion, based on the results, Deepgram API was deemed the most optimal for live transcription, as it is capable of producing fast and error-free transcriptions, significantly increasing the accessibility of information for the deaf community.
Deep Learning Convolutional Neural Networks on Multi Label Image Classification of Torajanese Buffalo Ramadhan, Aslan Poetra; Handayani, Anik Nur; Zaeni, Ilham Ari Elbaith
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2905.162-169

Abstract

Convolutional Neural Networks (CNNs) represent the primary methodology in the advancement of intelligent systems and technologies. The capacity to transition from prediction to categorization establishes CNNs as the primary benchmark in the advancement of deep artificial intelligence. This study use CNN implementation to categorize photos of Torajanese buffalo. The Torajanese buffalo is a distinctive animal species belonging to the Bos bubalis family, integral to the lives and culture of the Torajanese people residing in northern South Sulawesi. This species is integral to the culture, deeply intertwined with several traditional practices of the community. This renders the species distinctive for more investigation. The distinctiveness of the buffalo's style, coloration, and form differentiates them from one another. This study use Convolutional Neural Networks (CNNs) as the primary method to categorize Torajanese buffalo species using head photos and markers derived from local knowledge. This research employs InceptionV3, DenseNet, and Xception as primary architectures, each with variations corresponding to 10, 50, and 100 epochs, therefore enhancing the study. The findings of this investigation indicate that the InceptionV3 architecture has commendable performance across both versions, achieving an average AUC-ROC score of 0.96, although with increased execution time. Nonetheless, the DenseNet architecture demonstrates superior performance in its optimal configuration, achieving flawless accuracy; nonetheless, it incurs the most processing time for the frontal view of the Torajanese buffalo head test case.
Performance Comparison of Ensemble Learning Models for Brain Tumor Detection on Augmented MRI Datasets Titaley, Gilberth Valentino; Rismayanti, Nurul; Handayani, Anik Nur; Ardiansah, Jevri Tri
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2523.86-97

Abstract

Brain tumors are highly fatal diseases, making early detection a critical factor in improving patient survival rates. Magnetic Resonance Imaging (MRI) has become a primary tool in brain tumor diagnosis; however, manual analysis processes are often time-consuming and prone to subjective errors. This study employs a machine learning-based classification model to detect four categories of brain tumors—glioma, meningioma, pituitary, and healthy—with high accuracy. The methods include image segmentation using the U-Net model, which excels in medical image analysis due to its encoder-decoder architecture with skip connections, allowing efficient integration of spatial and contextual information. Features are extracted using HuMoments, known for their invariance to rotation, translation, and scale, ensuring robust spatial pattern representation. Data normalization is conducted using Robust Scaling and L2 Normalization to address outliers and harmonize feature scales, enhancing model performance. The MRI dataset, originally comprising 7,023 images, was augmented to 8,000 images using techniques such as rotation, flipping, and contrast adjustments to improve class balance and minimize overfitting. Three ensemble algorithms—Random Forest, XGBoost, and Stacking—were employed to train the models, with performance evaluation based on accuracy, ROC-AUC, F1-score, and confusion matrix. The results demonstrate that Random Forest achieved the best performance with an accuracy of 72% and an ROC-AUC of 0.91. This study illustrates the potential of machine learning approaches for automated brain tumor diagnosis, with further improvement possible through model optimization and the use of more diverse datasets.
Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset Manga', Abdul Rachman; Handayani, Anik Nur; Herwanto, Heru Wahyu; Asmara, Rosa Andrie; Sulistya, Yudha Islami; Kasmira, Kasmira
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1357.186-192

Abstract

Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.
DEVELOPMENT OF A CAREER READINESS ASSESSMENT TOOL BASED ON INTERESTS, POTENTIAL, AND SOFT SKILLS FOR VOCATIONAL HIGH SCHOOL STUDENTS FEBRIANTI, RIA; 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.
Co-Authors A.N. Afandi Abdullah Iskandar Syah Achmad Hamdan Achmad Safi’i Achmad Safi’i Adi Izhar Bin Che Ani Adi Prastowo, Nur Kodrad Adib Nur Sasongko Adim Firmansah Afandi, Farrel Candra Winata Agung Bella Putra Utama Agusta Rakhmat Taufani Ahmad Dardiri Ahmad Munjin Nasih Ahmad Nurdiansyah Ahmad Sahru Romadhon Aji Prasetya Wibawa Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro 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 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 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 Erwina Nurul Azizah Eva, Nur Evania Yafie F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Fakhruddin, Dhiyaurrahman Faqih, Kamil Faradhila Saffa Dhamira Farah Nisa’ Salsabila Fauzi, Juwita Annisa Fauzi, Rochmad Felix Andika Dwiyanto Ferina Ayu Pusparani Gianika Roman Sosa Graciello, Manuel Tanbica Gunawan Budi P Guyub Raharjo Gwo-Jiun Horng Haffas Zikri Ariyandi Hakkun Elmunsyah Halimahtus Mukminna, Halimahtus Handoko, Wahyu Tri 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 Imanuel Hitipeuw Ira Kumalasari Irfan Ramadhani Irham Fadlika Jehad A. H. Hammad Jehad A.H. Hammad Jevri Tri Ardiansah Jevri Tri Ardiansah Joumil Aidil Saifuddin 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 Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahamad, Abd Kadir Manga', Abdul Rachman Maqbullah, Afwatul Ming Foey Teng, Ming Foey Moh Zainul Falah Moh. Zainul Falah Mohammad Agung Rizki Mohammad Rizky Kurniawan Mohammad Yussril Asri Mohsen Samadi Mokh Sholihul Hadi Much. Arafat Al Mubarok 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 Mulya, Marga Asta Jaya Mumtaazah, Muhammad Athar Mutiara, Titi Nadindra Dwi Ariyanta Nandang Mufti Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Naufal Rizaldi Gunawan Nisa, Khoirotun Nizaar, Roub Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Halim Nur Hidayat, Wahyu Nur Rahma, Andika Bagus Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda 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 Rismayanti, Nurul 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 Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Suti Mega Nur Azizah Suziyani Mohamed Syaad Patmantara Syaad Patmanthara Syaichul Fitrian Akbar Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tsukasa Hirashima Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Utomo, Imam Tree Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Prasetyo, Muchamad Wahyu Primadi Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wibawa, Aji Presetya Wibowo, Kusmayanto Hadi Wicaksana, Ardi Anugerah Widiharso, Prasetya Wijaya, Mikel Ega Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Yosi Kristian Yu, Tony Yudha Islami Sulistya Yuliana Melita Pranoto Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zufida Kharirotul Umma Zulkham Umar Rosyidin Zulkham Umar Rosyidin Zulkifli, Shamsul Aizam