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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 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 Jurnal Mnemonic Frontier Energy System and Power Engineering Masyarakat Berdaya dan Inovasi 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 Jurnal Masyarakat Madani Indonesia 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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Analisis Jaringan Saraf Tiruan Pengenalan Pola Huruf Hiragana dengan Model Jaringan Perceptron Irfan Ramadhani; Selly Handik Pratiwi; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.41

Abstract

Artikel ini membahas mengenai program visual basic untuk mengenali dan menganalisis pola huruf hiraganaMa Mi Mu Me dan Mo Pengenalan pola huruf hiragana tersebut memiliki tujuan untuk pengembangan pengenalan pola huruf hiragana yang dimana dalam pendidikan bahasa jepang sudah banyak masuk ke dalam susunan mata pelajaran muatan lokal dalam tingkat sekolah menengah atas sehingga dapat membantu siswa dalam pengenalan pola-pola huruf hiragana. Selain itu tujuan penelitian ini adalah membandingkan keakuratan perhitunga excel dengan hasil program. Metode yang digunakan dalam penelitian ini adalah model jaringan perceptron. Analisis dilakukan dengan berdasarkan nilai alpha dan threshold dari tiap-tiap pola yang dilakukan menggunakan excel dan implementasinya dalam program visual basicHasil dari penelitian ini adalah keakuratan nilai dari excel dan juga program
Simulasi Kinerja Siswa Dengan Metode Fuzzy Inference Sugeno Menggunakan Aplikasi Matlab Halimahtus Mukminna; Devita Maulina Putri; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.53

Abstract

Tujuan artikel ini adalah membuat simulasi untuk penilaian kinerja siswa menggunakan logika fuzzy untuk mengatasi masalah proses penilaian evaluasi siswa. Disamping itu belum adanya sistem khusus yang dapat mengoptimalkan dalam memberikan dukungan bagi guru dalam melakukan evaluasi yang masih bersifat perhitungan manual. Satu cara penentuan perhitungan hasil evaluasi siswa dapat dipermudah dengan menggunakan bantuan pertimbangan Artifical Intelligence (AI) sebagai optimasinya. Dalam pertimbangan evaluasi kinerja siswa ini menggunakan logika fuzzy dengan metode inference system sugeno. Metode sugeno ini merupakan metode inference fuzzy untuk aturan yang direpresentasikan dalam bentuk IF-THEN, dimana output sistem tidak berupa himpunan fuzzy, melainkan berupa persamaan linier. Kriteria yang digunakan dalam penilaian kinerja siswa meliputi very unsuccesusful, unsuccessful, average, successful, dan very successful. Pada simulasi ini hasil yang ditampilkan dengan perhitungan manual dan perhitungan Matlab sebagai pembandingnya hasil perhitungan secara manual nilai result 45,5 sedangkan pada perhitungan matlab nilai result sebesar 48,5. Sehingga dapat disimpulkan selisih yang disebabkan tingkat akurasi hasil inference rule pada perhitungan manual kurang efektif bahkan terkadang banyak inference rule yang harus disesuaikan.
Analisis Jaringan Saraf Tiruan Model Perceptron Pada Pengenalan Pola Pulau di Indonesia Muhammad Ulinnuha Musthofa; Zufida Kharirotul Umma; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.56

Abstract

Tujuan dari penulisan artikel ini adalah menganalisa sistem jaringan saraf tiruan menggunakan model perceptron pada pengenalan pola pulau di Indonesia. Model jaringan perceptron biasa digunakan untuk pengenalan pola, karakter, maupun simbol, termasuk pola pulau-pulau di Indonesia. Analisis dilakukan berdasarkan nilai alpha (α) dan threshold (θ) pada setiap pola masukan pada perhitungan manual dengan excel dan diimplementasikan menggunakan program visual basic. Selanjutnya analisis dilakukan dengan cara membandingkan nilai alpha (α) dan threshold (θ) pada excel maupun visualbasic dan hasil pengenalan pola pulau-pulau besar di Indonesia didapatkan hasil yang sama, sehingga dapat disimpulkan keakurasian antara perhitungan keduanya. Analisis juga dilakukan terhadap laju pemahaman yang dimodifikasi mempengaruhi kecepatan iterasi, hal ini dilihat dari perubahan nilai net pada setiap perubahan nilai alpha (α).Hasil analisis perubahan laju pemahaman pada tabel modifikasi laju pemahaman menunjukkan bahwa semakin besar laju pemahaman semakin besar pula respon unit keluaran sehingga proses pemahaman menjadi lambat, begitu pula sebaliknya.
Comparative Analysis of Yolov-8 Segmentation for Gait Performance in Individuals with Lower Limb Disabilities Wulanningrum, Resty; Handayani, Anik Nur; Herwanto, Heru Wahyu
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1731

Abstract

This research aims to develop an example of gait pattern segmentation between normal and disabled individuals. Walking is the movement of moving from one place to another, where individuals with physical limitations on the legs have different walking patterns compared to individuals without physical limitations. This study classifies gait into three categories, namely individuals with assistive devices (crutches), individuals without assistive devices, and normal individuals. The study involved 10 subjects, consisting of 2 individuals with assistive devices, 3 individuals without assistive devices, and 5 normal individuals. The research process was conducted through three main stages, namely: image database creation, data annotation, and model training and segmentation using YOLOv8. YOLOv8-seg is the platform used to segment the data. The test results showed that the YOLOv8L-seg model achieved convergence value at the 23rd epoch with the 4th scenario in recognizing the walking patterns of the three categories. However, research on walking patterns of people with disabilities faces several obstacles, such as the lack of confidence or emotion of the subject during the data collection process, which is conducted at the location of the subject's choice. In addition, YOLOv8-seg showed consistent performance across the five models used, obtaining a maximum mAP50 value of 0.995 for mAP50 box and mAP50 mask.
Comparative analysis of decision tree and random forest classifiers for structured data classification in machine learning Kinasih, Agnes Nola Sekar; Handayani, Anik Nur; Ardiansah, Jevri Tri; Damanhuri, Nor Salwa
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1746

Abstract

This study explores the application of machine learning techniques, specifically classification, to improve data analysis outcomes. The primary objective is to evaluate and compare the performance of Decision Tree and Random Forest classifiers in the context of a structured dataset. Using the Elbow Method for optimal clustering alongside decision tree and random forest for classification algorithms, this research investigates the effectiveness of each method in accurately categorizing data. The study employs K-Means clustering to segment the data and Decision Trees and Random Forests for classification tasks. Dataset used in this research was obtained from Kaggle consisting of 13 attributes and 1048575 rows, all of which are numeric. The key results show that Random Forest outperforms Decision Trees in terms of classification accuracy, precision, recall, and F1 score, providing a more robust model for data classification. The performance improvement observed in Random Forest, particularly in handling complex datasets, demonstrates its superiority in generalizing across varied classes. The findings suggest that for applications requiring high accuracy and reliability, Random Forest is preferable to Decision Trees, especially when the dataset exhibits high variability. This research contributes to a deeper understanding of how different machine learning models can be applied to real-world classification problems, offering insights into the selection of the most appropriate model based on specific data characteristics.
Development of Embedded System Learning Module Using Project-based Learning Method for Industrial Electronics Department Pratama, Diaz Octa; 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.v16i1.25415

Abstract

The problem faced in the Industrial Electronics Department of Vocational High School (Sekolah Menengah Kejuruan/SMK) PGRI 3 Malang is the unavailability of systematic teaching materials in the learning process of embedded systems. The module is one of the teaching materials used to improve students’ quality and produce independent and creative students. This research aims to develop Embedded System Learning Module using Project-based Learning method to overcome the urgent need for teaching materials. The research methodology used is R&D with the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The steps of developing this module include: 1) Analysis: identifying the needs of teaching materials; 2) Design: designing the elements needed in the learning module; 3) Development: making and validating the module by material and media experts; 4) Implementation: Small and large group trials in class XI of the Industrial Electronics department of SMK PGRI 3 Malang; 5) Evaluate: module feasibility analysis. Data collection in this study used interviews, observations, and questionnaire instruments. Product validation results were obtained from media experts, with a percentage of 87.5% (very valid), and material experts, with a rate of 95% (very valid). After the developed product received feasible criteria from the experts, a small group trial (10 students) and a large group trial (24 students) were conducted. The results of the small group trial were 85.7% (very valid), and the results of the large group trial were 87.8% (very valid). Therefore, this module is feasible for teaching material in learning embedded systems.
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.
Revealing Interaction Patterns in Concept Map Construction Using Deep Learning and Machine Learning Models Laily, F.ti Ayyu Sayyidul; Prasetya, Didik Dwi; Handayani, Anik Nur; Hirashima, Tsukasa
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM 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.
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.
Co-Authors A.N. Afandi Abdul Rachman Manga' 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 Adipura, Laksamana Afandi, Farrel Candra Winata Agusta Rakhmat Taufani Ahmad Dardiri Aji Prasetya Wibawa Al-Jabbar, Habib Muhammad Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro Aripriharta - Ariyanta, Nadindra Dwi Arwani, Wafiq Nur Muhamamd Asfani, Khoirudin Atmaja, Muhammad Bayu Setya Wahyu Ayu Puspita Azhryl Assagaf Aziz, Faiz Syaikhoni Azizah, Desi Fatkhi Azizah, Devi Nur 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 Devita Maulina Putri, Devita Maulina Dewi Aprilia Lintang Dewi, Ellya Kusna Aura 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 F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Fakhruddin, Dhiyaurrahman Faqih, Fauziah Nur 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 Hartarto Junaedi Hary Suswanto Hasriani Hasriani, Hasriani Hermansyah Hermansyah Heru Herwanto Heru Wahyu Herwanto Hirashima, Tsukasa Hitipeuw, Emanuel Hosen, Moh I Made Wirawan Ida Ayu Putu Sri Widnyani Ihsan Al-Fikri 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 Kirana Kasmira Kasmira Katya Lindi Chandrika Khasanah, Elok Rosyidatul Khumairoh, Fidyah Nur 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. Kirom, M. 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 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 Arifin Muhammad Hafiizh Muhammad Holqi Rizki Azhari Muhammad Iqbal Akbar Muhammad Ridwan Muhammad Ulinnuha Musthofa Muhammad Younas Darvish Muhammad Zaky Rahmatsyah Muladi 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 Ningrum, Gres Dyah Kusuma Nisa, Khoirotun Nizaar, Roub Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Halim Nur Rahma, Andika Bagus Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda Prasetya Widiharso Prasetya Widiharso Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Pratama, Diaz Octa Pratama, Wahyu Styo Priharta, Ari Primadi, Wahyu Purnomo, Purnomo Putra Utama, Agung Bella Putri Galuh Ningtiaz Qomaria, Ulfa Rahman, Nukleon Jefri Nur Rahmat Samudra Anugrah, Muhammad Ramadhani, Lolita Ratnasari, Diah Ayu Resty Wulanningrum Reza Setyawan Rini Nur Hasanah Rismayanti, Nurul Romadlon, Muhammad Rizqi Rosa Andrie Asmara Rosa Andrie Asmara Rosyidin, Zulkham Umar Rusdha Aulia Salah Abdullah Khalil Abdulrahman Salsabila, Reni Fatrisna Saodah Omar Saputra, Ismed Eko Hadi Selly Handik Pratiwi Seno Isbiyantoro Setyaningsih, Eka Rahayu Setyawan, Wahyu Dwi Sevilla, Felix Rafael Segundo Siti Sendari Slamet Wahyudi Slamet Wibawanto Soraya Norma Mustika Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Syaad Patmantara Syaichul Fitrian Akbar Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tsukasa Hirashima Ulum, Khoirul Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Utomo, Imam Tree Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Primadi Wahyu Sakti Gunawan Irianto 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