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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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Development of Virtual Laboratory-based Learning Media on Sensor and Actuator Device Elements Nisa, Khoirotun; Handayani, Anik Nur
Lectura : Jurnal Pendidikan Vol. 16 No. 1 (2025): Lectura: Jurnal Pendidikan
Publisher : Fakultas Keguruan dan Ilmu Pendidikan (FKIP), Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/lectura.v16i1.25421

Abstract

Today’s technological advancements can be used to suit the demands of many areas of life, including education. Technology in education enables more flexible medium for learning. Limited laboratory facilities in vocational high schools hamper practicum activities that are very important to achieve the standard competence. Virtual laboratory-based learning media is needed to simulate theory and practicum activities without time and place restrictions. This research aims to develop and calculate the feasibility of virtual laboratory-based learning media in the form of guidebooks and job sheets for sensor and actuator subjects using the Wokwi platform, arduino simulation projects, and sensors. This research uses the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) with data collection through observation, interviews, validation by media experts and material experts, and trials to students. Data were analyzed qualitatively and quantitatively. This study involving 77 students from class XI of Industrial Automation Engineering (Teknik Otomasi Industri/TOI) SMK PGRI Singosari showed results that the teaching materials achieved a combined validity value of 92.41% for the guidebook, 93.88% for the job sheet, and 93.20% for the Wokwi platform. The combined validity value ranges from 85.01% to 100% which indicates that the teaching materials are very valid and suitable for use. In conclusion, this virtual laboratory-based learning media supports the understanding of sensor and actuator subjects in vocational education. This learning media effectively overcomes the limitations of laboratory facilities while increasing student learning motivation.
Hand Keypoint-Based CNN for SIBI Sign Language Recognition Handayani, Anik Nur; Amaliya, Sholikhatul; Akbar, Muhammad Iqbal; Wiryawan, Muhammad Zaki; Liang, Yeoh Wen; Kurniawan, Wendy Cahya
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1745

Abstract

SIBI is less widely adopted, and the lack of an efficient recognition system limits its accessibility. SIBI gestures often involve subtle hand movements and complex finger configurations, requiring precise feature extraction and classification techniques. This study addresses these issues using a Hand Keypoint-based Convolutional Neural Network (HK-CNN) for SIBI classification. The research utilizes Kinect 2.0 for precise data collection, enabling accurate hand keypoint detection and preprocessing. The optimal data acquisition distance between 50 and 60 cm from the camera is considered to obtain clear and detailed images. The methodology includes four key stages: data collection, preprocessing (keypoint extraction and image filtering), classification using HK-CNN with ResNet-50, EfficientNet, and InceptionV3, and performance evaluation. Experimental results demonstrate that EfficientNet achieves the highest accuracy of 99.1% in the 60:40 data split scenario, with superior precision and recall, making it ideal for real-time applications. ResNet-50 also performs well with 99.3% accuracy in the 20:80 split but requires longer computation time, while InceptionV3 is less efficient for real-time applications. Compared to traditional CNN methods, HK-CNN significantly enhances accuracy and efficiency. In conclusion, this study provides a robust and adaptable solution for SIBI recognition, facilitating inclusivity in education, public services, and workplace communication. Future research should expand dataset diversity and explore dynamic gesture recognition for further improvements.
Ensemble learning approaches for predicting heart failure outcomes: A comparative analysis of feedforward neural networks, random forest, and XGBoost Ariyanta, Nadindra Dwi; Handayani, Anik Nur; Ardiansah, Jevri Tri; Arai, Kohei
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1750

Abstract

Heart failure is a leading cause of morbidity and mortality worldwide, and early prediction of outcomes is critical for timely intervention and improved patient care. Accurate prediction models can help clinicians identify high-risk patients, optimize treatment strategies, and reduce healthcare costs. In this study, we developed and evaluated machine learning models to predict mortality in patients with heart failure using a medical dataset of 299 patients with 13 clinical variables collected in 2015. Four models were tested, including a Feedforward Neural Network (FNN), Random Forest, XGBoost, and an ensemble model combining all three models. The experimental process included data preprocessing, feature scaling, and stratified cross-validation to ensure robust evaluation. The results showed that the ensemble model achieved the best performance with an ROC-AUC of 0.9134 and an F1 score of 0.7439, outperforming individual models such as Random Forest (ROC-AUC: 0.9117) and XGBoost (ROC-AUC: 0.9130). FNN, despite having the highest accuracy (0.8455), showed lower performance in terms of recall and precision, likely due to its sensitivity to overfitting on small datasets. These results highlight the effectiveness of ensemble learning in medical prediction tasks, especially for handling complex, high-dimensional health data. The proposed ensemble model has the potential to be integrated into clinical decision support systems, enabling real-time risk assessment and personalized treatment plans for heart failure patients. Future research should explore larger, multicenter datasets, incorporate advanced feature engineering techniques, and investigate the integration of deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to process sequential data such as ECG signals.
Application of Mamdani Fuzzy Logic in Identifying Postpartum Depression Risk Kinasih, Agnes Nola Sekar; Hosen, Moh; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.193

Abstract

Introduction: Postpartum depression (PPD) is a common psychological disorder affecting mothers after childbirth, often underdiagnosed due to the subjective nature of its symptoms. Early detection is crucial to prevent adverse effects on maternal and child health. This study aims to develop an early detection system for PPD risk using Mamdani fuzzy logic, which is well-suited to handle vague and imprecise symptom data. Methods: A fuzzy inference system was designed using the Mamdani method to classify PPD risk into Low, Medium, and High categories. The system was built upon a dataset of 1503 questionnaire responses sourced from Kaggle. Subjective symptoms such as sadness, irritability, sleep disturbances, and bonding difficulties were mapped into fuzzy membership functions. A total of 243 fuzzy rules were defined to reflect realistic combinations of symptoms. The system was implemented and validated in both Python and LabVIEW environments. Results: Experimental validation using 10 test inputs showed consistent results between the two platforms, with a deviation of less than ±1%. This consistency confirms the reliability of the fuzzy logic model in interpreting subjective symptom data. The system demonstrated strong potential for classifying PPD risk based on nuanced input variables. Conclusions: The Mamdani fuzzy logic system offers a reliable and flexible tool for assessing postpartum depression risk. By effectively interpreting ambiguous symptoms, it supports healthcare professionals in identifying at-risk individuals for early intervention. Future enhancements should include expanding the dataset and refining the rule base for broader applicability and improved accuracy.
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS TEKA-TEKI SILANG PADA PEMBELAJARAN INFORMATIKA UNTUK SISWA SMP Farah Nisa’ Salsabila; Anik Nur Handayani; Baskoro Arif Widodo
Jurnal Ekonomi, Bisnis dan Pendidikan Vol. 3 No. 12 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pendidikan memegang peranan penting dalam pengembangan individu dan kemajuan suatu bangsa. Namun, tantangan dalam meningkatkan minat dan motivasi siswa di SMP, terutama dalam pembelajaran Informatika, masih menjadi perhatian. Dalam era digital yang cepat berkembang, pembelajaran memerlukan inovasi dalam penyampaian materi. Salah satu tantangan utama adalah kebosanan akibat penyampaian materi yang monoton dan kurang menarik. Dalam konteks ini, pendekatan pembelajaran yang berpusat pada peserta didik, dengan penggunaan teknologi dan metode yang inovatif, menjadi solusi efektif. Penelitian ini mengembangkan Lembar Kerja Peserta Didik (LKPD) berbasis permainan Teka Teki Silang (TTS) untuk pembelajaran informatika di SMP, dengan tujuan memfasilitasi pemahaman konsep siswa dan sebagai bahan ajar tambahan bagi guru. Model ADDIE digunakan sebagai kerangka kerja dalam pengembangan ini, yang mencakup tahapan Analisis, Desain, Pengembangan, Implementasi, dan Evaluasi. Hasil penelitian menunjukkan LKPD berbasis TTS sangat efektif meningkatkan pemahaman siswa terhadap materi informatika dan sangat layak digunakan dilihat dari segi media dan materi. LKPD ini dapat menjadi solusi untuk meningkatkan minat dan motivasi belajar, serta memfasilitasi pemahaman konsep siswa dalam pembelajaran informatika di SMP.
PEMBUATAN MEDIA AJAR INTERAKTIF SEBAGAI BENTUK INOVASI DALAM PEMBELAJARAN INFORMATIKA UNTUK SISWA SMP Reza Setyawan; Anik Nur Handayani; Baskoro Arif Widodo
Jurnal Ekonomi, Bisnis dan Pendidikan Vol. 3 No. 12 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pesatnya perkembangan ilmu pengetahuan dan teknologi mendorong perlunya pengembangan wawasan dan kemampuan dalam bidang pendidikan. Media pembelajaran, terutama yang digital, memiliki peran penting dalam memfasilitasi proses pembelajaran dengan menawarkan konten interaktif dan aksesibilitas yang tinggi. Namun, sebagian besar pendekatan pengajaran masih mengandalkan metode ceramah dan buku paket, mengakibatkan minat belajar siswa menurun dan kurangnya pemahaman konsep. Penelitian ini bertujuan untuk mengembangkan media ajar interaktif sebagai inovasi dalam pembelajaran informatika untuk siswa SMP, dengan fokus pada materi kolaborasi dalam masyarakat digital. Melalui pengembangan media ajar ini, diharapkan dapat meningkatkan pemahaman siswa terhadap materi pelajaran dan mendukung proses pembelajaran di dalam kelas. Media ajar interaktif ini juga diharapkan dapat memfasilitasi siswa untuk belajar secara aktif dan praktis, meningkatkan minat belajar serta mengatasi keterbatasan fasilitas yang tersedia. Penelitian ini diharapkan dapat memberikan kontribusi positif bagi pendidikan dengan memanfaatkan teknologi untuk meningkatkan efektivitas pembelajaran.
Pemodelan Sistem Deteksi Kadar Unsur Hara Tanah Berdasarkan Nilai NPK Menggunakan Metode Fuzzy Mamdani Dityo Kreshna Argeshwara; Zulkham Umar Rosyidin; Aji Prasetya Wibawa; Anik Nur Handayani; Mokh. Sholihul Hadi
Jurnal Sains dan Informatika Vol. 9 No. 1 (2023): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v9i1.523

Abstract

Profil kesuburan tanah merupakan hal yang penting dalam pertanian karena merupakan media utama dalam bercocok tanam. Penggunaan pupuk kimia dan pestisida secara terus menerus dan berlebihan akan dapat menimbulkan perubahan sifat fisika dan kimia tanah yang pada akhirnya akan dapat menyebabkan tanah menjadi kritis. Hal ini akan berpengaruh pada produktivitas hasil panen para petani. Salah satu upaya untuk mengetahui tingkat kesuburan tanah adalah melalui diagnosa unsur hara dalam tanah. Tujuan penelitian ini adalah untuk membuat pemodelan sistem optimalisasi deteksi kadar unsur hara dalam tanah menggunakan fuzzy. Melalui simulasi ini akan didapatkan data kadar unsur hara tanah dengan parameter unsur hara N (Nitrogen), P (Fosfor) , K (Kalium) menggunakan labview. Berdasarkan parameter tersebut kemudian dihasilkan berapa nilai kadar unsur hara NPK dalam tanah apakah rendah, sedang atau tinggi.
Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems Hayati, Lilis Nur; Handayani, Anik Nur; Gunawan Irianto, Wahyu Sakti; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.948

Abstract

This research explores the optimization of YOLO-based computer vision algorithms for real-time recognition of Indonesian Sign Language (BISINDO) letters under diverse environmental conditions. Motivated by the communication barriers faced by the deaf and hearing communities due to limited sign language literacy, the study aims to enhance inclusivity through advanced visual detection technologies. By implementing the YOLOv5s model, the system is trained to detect and classify correct and incorrect BISINDO hand signs across 52 classes (26 correct and 26 incorrect letters), utilizing a dataset of 3,900 images augmented to 10,920 samples. Performance evaluation employs k-fold cross-validation (k=10) and confusion matrix analysis across varied lighting and background scenarios, both indoor and outdoor. The model achieves a high average precision of 0.9901 and recall of 0.9999, with robust results in indoor settings and slight degradation observed under certain outdoor conditions. These findings demonstrate the potential of YOLOv5 in facilitating real-time, accurate sign language recognition, contributing toward more accessible human-computer interaction systems for the deaf community.
Optimized image-based grouping of e-commerce products using deep hierarchical clustering Pranoto, Yuliana Melita; Handayani, Anik Nur; Herwanto, Heru Wahyu; Kristian, Yosi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1979

Abstract

Managing large and constantly evolving product catalogs is a significant challenge for e-commerce platforms, especially when visually similar products cannot be reliably distinguished using text-based methods. This study proposes a product grouping method that combines a fine-tuned EfficientNetV2M model with an adaptive Agglomerative Clustering strategy. Unlike conventional CNN-based approaches, which have limited scalability and a fixed number of clusters, the proposed method dynamically adjusts similarity thresholds and automatically forms clusters for unseen product variations. By linking deep visual feature extraction with adaptive clustering, the method enhances flexibility in handling product diversity. Experiments on the Shopee product image dataset show that it achieves a high Normalized Mutual Information (NMI) score of 0.924, outperforming standard baselines. These results demonstrate the method’s effectiveness in automating catalog organization and offer a scalable solution for inventory management and personalized recommendations in e-commerce platforms.
Comparative Analysis Using Xception and MobileNetV2 Deep Learning Models for Brain Tumor Detection in MRI Images Mumtaazah, Muhammad Athar; Anik Nur Handayani
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15332

Abstract

This study presents a comparative analysis of two deep learning models, Xception and MobileNetV2, for brain tumor detection using MRI images. The selection of these models is based on their respective advantages. Xception is known for its ability to handle large and complex datasets due to its deep architecture and the use of depthwise separable convolutions. It also features a deep structure capable of extracting complex features from high-resolution images, making it well-suited for detailed image recognition tasks. In contrast, MobileNetV2 is designed to be lighter and more computationally efficient, making it ideal for deployment on mobile devices or in resource-constrained environments without significantly compromising performance. These characteristics make both models highly relevant for medical image analysis, particularly in brain tumor detection, which demands both accuracy and efficiency.This study uses a public dataset that has been preprocessed through augmentation and normalization. Both models were trained and evaluated using accuracy, loss, and confusion matrix metrics. The results show that MobileNetV2 achieved higher accuracy (97.8%) compared to Xception (94.9%) with a lower error rate. For precision, recall, and F1-score metrics, the results were identical up to four decimal places, further supporting that MobileNetV2 is more suitable for brain tumor detection in resource-limited settings. Based on the findings, MobileNetV2 demonstrates superior performance compared to Xception, making it the favorable choice.
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