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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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Real-Time BISINDO Alphabet Recognition via Faster R-CNN Incorporating Skin Tone Diversity as a Classification Feature Lilis Nur Hayati; Anik Nur Handayani; Wahyu Sakti Gunawan Irianto; Rosa Andrie Asmara; Dolly Indra; Nor Salwa Damanhuri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.15587

Abstract

Indonesian Sign Language (Bahasa Isyarat Indonesia/BISINDO) enables communication for deaf individuals through hand gestures, yet limited public awareness creates significant barriers between deaf and hearing communities. Existing recognition systems often fail to generalize across diverse skin tones, reducing their effectiveness in inclusive real-world deployment. The contribution of this research is a BISINDO alphabet recognition system that integrates skin color features - extracted via HSV-based skin segmentation - as an additional preprocessing layer within the Faster R-CNN framework, explicitly improving detection robustness across varied skin tones. The dataset consists of 8,000 images from ten adult actors representing light, medium-brown, and dark skin tones, augmented through flipping and brightness variation, with a 90:10 training-to-testing ratio. The model was trained over 15,000 steps with a batch size of 24, selected through empirical validation to balance convergence stability and dataset size. Experimental results show that indoor conditions outperform outdoor settings due to controlled lighting. Light-skinned and dark-skinned participants achieved the highest accuracy of 87.5% and F1-score of 85.71%, while medium-brown-skinned participants showed slightly lower performance, likely attributed to greater variability in reflectance under mixed lighting. The system achieves 24 frames per second, demonstrating potential for real-time communication support. These findings confirm that Faster R-CNN with skin color feature integration is effective for BISINDO alphabet recognition, with skin tone diversity being a critical performance factor. Future work will explore larger participant pools and dynamic gesture recognition under varied real-world lighting scenarios.
Intelligent Weighing Machine untuk Meningkatkan Keakuratan Berat Produk Bubuk Herbal Instan Sujito; Siti Sendari; Anik Nur Handayani; Langlang Gumilar; Imam Tree Utomo; Dhiyaurrahman Fakhruddin
ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Vol. 1 No. 2 (2023): ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Publisher : UPT Publikasi dan Penerbitan Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/abdiunisap.v1i2.112

Abstract

Syarimpon merupakan usaha yang didirikan oleh Afiani Fadiana pada tahun 2017 yang berlokasi di Perumahan Persada Bhayangkara No.G3, Pangetan, Kecamatan Singosari, Kabupaten Malang. yang bergerak dalam bidang produksi minuman herbal instan dan sudah mengembangkan minuman herbal instan dengan kemasan yang menarik dan sudah memiliki berbagai perizinan mulai dari NIB ( Nomor Induk Berusaha), PIRT (Produk Industri Rumah Tangga), Perizinan penetapan produk halal dan produk sudah HAKI (Hak Kekayaan Intelektual) namun Syarimpon ini mengalami permasalahan mengenai proses penimbangan berat produk masih menggunakan cara manual dan memakan waktu pada saat proses penimbangan dan kurangnya keakuratan dari timbangan yang digunakan. Dengan adanya program pengabdian ini diharapkan mampu mengatasi masalah dari mitra dengan mentransfer teknologi Intelligent Weighing Machine guna meningkatkan keakuratan berat produk dengan cerdas dan lebih efisien. Tujuan dari program pengabdian kepada masyarakat ini menghasilkan Intelligent Weighing Machine yang diharapkan mampu mengurangi waktu dalam proses penimbangan dan membantu mengatasi permasalahan selama proses produksi mereka dan dapat mempertahankan kualitas dari minuman herbal instan yang mereka produksi. Hasil dari program PKM ini melakukan pengembangan Intelligent Weighing Machine alat ini didesain untuk memberikan kemudahan untuk pengguna sehingga mempercepat proses penimbangan dan meningkatkan keakuratan berat bersih produk sehingga dapat mengurangi waktu yang digunakan untuk menimbang serta memastikan berat bersih produk sesuai standar yang telah disesuaikan oleh UMKM Syarimpon dan memberikan dampak positif untuk penjualan dan produksi mereka.
PENGENDALIAN BEBAN LISTRIK LABORATORIUM BERDASARKAN DETEKSI MANUSIA DAN JADWAL KULIAH Achmad Safii; Anik Nur Handayani; Dityo Kreshna Argeshwara
TEKNO Vol 34, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v34i1p48-57

Abstract

Peningkatan konsumsi energi listrik di seluruh dunia menjadi permasalahan serius karena keterbatasan sumber daya alam dan dampak negatifnya terhadap lingkungan. Sebagai pusat pendidikan dan penelitian, perguruan tinggi memiliki tanggung jawab untuk mengurangi penggunaan energi listrik yang tidak perlu di lingkungan kampus. Salah satu area yang perlu mendapatkan perhatian khusus adalah ruang laboratorium, di mana sering kali peralatan listrik masih aktif meskipun tidak digunakan. Oleh karena itu, diperlukan sistem otomatis yang dapat mengendalikan penggunaan beban listrik di laboratorium. Penelitian ini bertujuan untuk mengembangkan sistem yang dapat mengurangi penggunaan energi listrik yang tidak perlu di laboratorium dengan mengintegrasikan deteksi manusia dan jadwal kuliah sebagai metode pengendalian beban listrik. Metode penelitian melibatkan tahap observasi, analisis masalah, pembuatan sistem, uji coba sistem, dan evaluasi. Hasil penelitian menunjukkan beban listrik di laboratorium dapat dikendalikan secara otomatis. Sistem yang dikembangkan berhasil menghemat energi listrik sebesar 21,6 kWh (33,3%) setiap bulan.
Disain dan Implementasi Pembangkit Listrik Tenaga Surya Off Grid untuk Suplai Energi Listrik untuk CCTV dan Peralatan Listrik di Pos Satpam Dyah Lestari; Sujito Sujito; Anik Nur Handayani; Panji Ageng Timor Pamungkas
TEKNO Vol 34, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v34i1p77-87

Abstract

Penelitian ini bertujuan merancang dan mengimplementasikan sistem Pembangkit Listrik Tenaga Surya (PLTS) off grid untuk mendukung operasional perangkat keamanan seperti CCTV, lampu, dan peralatan listrik lain di pos satpam. Saat ini, peralatan tersebut masih bergantung pada listrik dari rumah warga, yang menimbulkan kendala dalam pembagian biaya dan risiko gangguan pasokan dari PLN. Metode penelitian mencakup analisis kebutuhan energi, disain sistem, perakitan komponen, dan pengujian langsung di lokasi. Total kebutuhan energi harian adalah 4.384 Wh. Dari hasil perancangan diperoleh komponen yang dibutuhkan untuk sistem PLTS meliputi meliputi 3 panel surya 300 Wp, 3 baterai VRLA 12V 100Ah, 1 Solar Charge Controller 100A, dan 1 inverter 5.000 W. Panel surya dipasang di atas tiang setinggi 3 meter untuk efisiensi penyerapan cahaya. Dari pengujian sistem PLTS yang dilakukan pada pukul 07.00 hingga 17.00 WIB diperoleh total daya yang masuk ke baterai sejumlah 859,48 Watt serta rata-rata daya per jam adalah 71,62 Watt. Tegangan luaran baterai juga berada di sekitar 11 V-13 V. Hasil implementasi menunjukkan bahwa sistem PLTS mampu menyediakan daya listrik mandiri, mengurangi ketergantungan pada PLN, mendukung keamanan lingkungan, dan dapat direplikasi di lokasi lain dengan kondisi serupa
Basketball Activity Recognition Using Supervised Machine Learning Implemented on Tizen OS Smartwatch Rosa Andrie Asmara; Nofrian Deny Hendrawan; Anik Nur Handayani; Kohei Arai
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23668

Abstract

Basketball Activity Recognition (BAR) in sports teams, especially in basketball, to make statistical analysis of player activity data is currently a very important thing. BAR is one part of sports science that recognizes the movement of players in each activity, such as dribbling, passing, etc. Sport science in the sports business is used as one of the factors of coaches and management to determine strategy, starter line-up, check the condition of players after injury, etc. the current technology to recognize player activity only depends on the object detection method of players' through video recordings of players is considered lacking because it only sees the perspective of the coach to reduce players as starter line-up and there is no logical calculation of why players are not installed as starter line-up. One method for recognizing player activity is using a wearable device that has an accelerometer and gyroscope sensor with high accuracy. The values from those sensors will be classified and recognize their activity, i.e., Dribbling, Passing, and Shooting. Smartwatch is one of those wearable devices that meet those criteria. For the activity classification process, the use of the K-NN classification method is the most appropriate because it has a low computational level that is in accordance with the smartwatch specifications. The results of the classification using accelerometer sensor data and gyroscopes with K-NN as an activity recognition method have an accuracy of 81.62%, and player activity recognition applications using accelerometer and gyroscope sensors can also record the results of player movements for further analysis by management and coaches. This is the advantage of this BAR application compared to the recognition of player activity using object detection on video recordings.
Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing Wahyu Tri Handoko; Muladi Muladi; Anik Nur Handayani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26395

Abstract

Sunlight is an alternative energy source that can be used as a substitute for fossil fuels. Renewable energy potential has not been widely utilized, especially in Indonesia. Utilization of sunlight, one of which is done indoors to save electricity and the source is not limited. This study aims to predict solar irradiance to determine the value of sunlight intensity in an area as the main source of the utilization of renewable electrical energy through the solar tube system with the LSTM method. This low-cost system offers a renewable way and considers the potential for solar radiation as an energy-efficient alternative based on the intensity of light captured by the solar tube. This research uses two methods. The LSTM method is a recurrent neural network forecasting technique that can study deeply and extract temporal relationships in data because of its large architecture. The exponential smoothing method is part of the time series forecasting technique and is used when the dataset has no cyclic variance and trend. Data collection was carried out in sunny conditions because it represents a stable condition in sunlight. The results obtained from the two methods are evaluated with RMSE and MAE values to choose the optimal approach. Due to lower RMSE and MAE values in this comparison, LSTM performs better than Multiple Repeat and Exponential Smoothing in terms of performance.
Designing a Cloud-Based Smart-EduVerse Management System forPrescriptive Quality Governance in Higher Education: AMixed-Methods Study Rochmawati Rochmawati; Anik Nur Handayani; Tran Thi Hao; Soubin Sisavath; Moch Haris Purwanto; Nailah Aliya Putri
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6647

Abstract

The purpose of this study is to develop a Cloud-Based Smart-EduVerse Management System (S-EMS) architecture as a digital quality mapping framework designed to strengthen Sustainable Competitive Advantage (SCA) in Indonesian State Universities with Legal Entity (PTNBH) status amid the VUCA era. This study employed a mixed-methods approach using a concurrent embedded design with qualitative dominance. The research involved 22 PTNBHs, with qualitative data collected through in-depth interviews, observations, and document analysis, while quantitative data were obtained through questionnaires. Both datasets were integrated during the interpretation stage using constant comparative analysis and descriptive statistics to formulate the system architecture and governance requirements. The results indicate that S-EMS consists of three cloud-based functional layers: (1) a prescriptive datamapping layer integrating institutional quality indicators, (2) a cloud-hosted knowledge asset management module for organizing human and intellectual capital, and (3) an adaptive governance dashboard supporting real-time cross-campus decision-making. Quantitative findings show that 19 of the 22 universities (86.4%) have integrated digital infrastructure, while strategic collaboration and adaptive quality assurance practices were implemented across nearly all participating institutions. These findings demonstrate that the proposed architecture reduces data fragmentation, improves interoperability, and supports evidence-based governance, facilitating a transition from compliance-based to impact-based institutional management. This study contributes a validated cloud-based Smart-EduVerse Management System (S-EMS) framework integrating digital quality mapping, knowledge asset management,and prescriptive governance to strengthen sustainable competitive advantage in higher education.
IoT-Based Light Intensity Control System for Melon Photoperiodism Optimization Using the Fuzzy Sugeno Method Aqdam, Yutsabitul; Norma Mustika, Soraya; Noerhayati, Eko; Nur Handayani, Anik; Hamdan, Achmad
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 3 No. 2 (2026): June : International Journal of Mechanical, Industrial and Control Systems Engi
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v3i2.417

Abstract

Melon cultivation requires appropriate light intensity and photoperiod management because light directly affects photosynthesis, vegetative growth, and plant productivity. In conventional cultivation, additional lighting is often controlled manually, making it less adaptive to changes in sunlight intensity, temperature, and daily photoperiod. This study develops an Internet of Things (IoT)-based automatic light intensity control system using the Fuzzy Sugeno method to optimize photoperiodism in melon plants during the vegetative phase. The system integrates an ESP32 microcontroller, BH1750 light intensity sensor, DHT21 temperature sensor, real-time clock, AC light dimmer, LED grow light, LCD I2C, and Blynk application. The Fuzzy Sugeno method was implemented to determine the percentage of LED grow light output based on light intensity, temperature, and daily time period. Experimental results showed that the BH1750 sensor calibration reduced the average measurement error from 10.13% to 2.753%, while the DHT21 temperature sensor calibration reduced the average error from 3.95% to 0.31%. The AC light dimmer responded proportionally to PWM input, producing lamp outputs from 0 lux to approximately 28,000 lux. Data transmission to Blynk was successful with an average delay of about 1.01 seconds. Plant testing for 14 days showed that the IoT-based automatic lighting system produced better vegetative growth than the conventional method, with plant height reaching 19 cm and five leaves. These findings indicate that the developed system can support adaptive lighting control for melon photoperiodism optimization.
Adaptive Feature Selection using Fisher-Based Supervised Hill Climbing for Dysgraphia Handwriting Classification Kartika Candra Kirana; Anik Nur Handayani; Nur Eva; Aji Prasetya Wibawa; Wahyu Nur Hidayat; Kohei Arai
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14983

Abstract

Dysgraphia features selection remains a challenge. Fisher’s criterion excels at highlighting the discriminative features of dysgraphia but lacks guidance for choosing the optimal number of features. Whereas Hill Climbing shows robust feature selection but often gets trapped in local optima. This study aims to avoid the Hill Climbing trap in local optima when selecting the best dysgraphia feature. Thus, the Fisher-Based Supervised Hill Climbing (FSHC) method is introduced. The contribution of this study is an optimized machine-learning-guided hill-climbing method that uses a classifier on a validation set as the objective function. A plateau mechanism also guided Hill Climbing exploration, not by a single Fisher point but by the neighboring subsets. The dataset used contains the graphomotor slant line task from 119 children aged 8-15 years (47.5% diagnosed with dysgraphia), with 10000 to 50000 data points per user. It is organized into kinematic, spatial, dynamic, and temporal features, yielding 117 sub-features. A stratified 5-fold cross-validation is set for training and testing, reaching 21 features. Comparative test—Linear SVM, SVM RBF, Sigmoid SVM, Polynomial SVM, Random Forest, AdaBoost, KNN, Decision Tree, Gradient Boosting, Gaussian Naive Bayes, and Gaussian Classifier—showed that linear SVM achieves the best performance with a weighted average precision, recall, and F1 score of 0.93. Linear SVM also outperformed the three approaches: no feature selection, the traditional Fisher, and machine-learning-based feature selection (weighted KNN and SVM). It can be concluded that the proposed method is more robust than the state of the art by highlighting key points for avoiding overfitting.
Grid-Calibrated Patch Learning for Braille Multi-Character Recognition Made Ayu Dusea Widyadara; Anik Nur Handayani; Heru Wahyu Herwanto; Tony Yu; Marga Asta Jaya Mulya
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.
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