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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics IJAIT (International Journal of Applied Information Technology) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Abdi Insani Madani : Indonesian Journal of Civil Society JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) JURNAL ILMIAH GLOBAL EDUCATION Prosiding Konferensi Nasional PKM-CSR Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Applied Science eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Abdibaraya: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL Journal of Applied Engineering and Social Science
Sugondo Hadiyoso
Universitas Telkom
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Automatic Leukocytes Classification using Deep Convolutional Neural Network HADIYOSO, SUGONDO; AULIA, SUCI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 1: Published January 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i1.195

Abstract

ABSTRAKSel darah putih atau leukosit adalah salah satu bagian darah yang bertanggung jawab untuk sistem kekebalan tubuh. Penghitungan setiap jenis leukosit merupakan hal yang krusial untuk menentukan status kesehatan. Sel darah dihitung menggunakan hematology analyzer. Namun, perangkat ini hanya tersedia di laboratorium klinik pusat atau rumah sakit. Saat ini masih banyak clinician yang melakukan perhitungan manual dengan memperkirakan jumlah leukosit menggunakan mikroskop. Hal ini berpotensi menimbulkan kesalahan perhitungan yang tinggi. Oleh karena itu, penelitian ini mengusulkan suatu sistem yang dapat mengklasifikasikan jenis-jenis leukosit. Metode convolutional neural network (CNN) dengan arsitektur VGG-19 digunakan dalam klasifikasi leukosit. Beberapa skenario pengujian dengan mengubah parameter epoch dan ukuran batch diterapkan untuk mendapatkan akurasi terbaik. Hasil simulasi model pembelajaran yang digunakan dapat menghasilkan akurasi hingga 100% untuk mengklasifikasikan neutrofil, eosinofil, monosit, dan limfosit. Hasil ini dicapai dengan menggunakan pengoptimal Adam, Epoch=5 dan batch size=60.Kata kunci: leukosit, klasifikasi, CNN, VGG-16 ABSTRACTWhite blood cells or leukocytes are one of the blood components responsible for the body's immune system. Counting each type of leukocyte is a crucial thing to determine the health status. Blood cells were counted using a hematology analyzer. However, this device is only available in central clinical laboratories or hospitals. Currently, there are still many clinicians doing manual calculations by estimating the number of leukocytes using a microscope. This has the potential to generate high errors in calculations. Therefore, this study proposes a system that can classify the types of leukocytes. The convolutional neural network (CNN) method with VGG-19 architecture was employed in leukocyte classification. Several test scenarios by changing the epoch and batch size parameters were applied to obtain the best accuracy. The results of the simulation of the learning model used can generate accuracy up to 100% for classifying neutrophils, eosinophils, monocytes, and lymphocytes. This result was achieved using Adam optimizer, epoch=5 and batch size=60.Keywords: leukocyte, classification, CNN, VGG-16
IoT-Based Early Detection of Cardiovascular Disease with Ankle Brachial Index Measurement for Right and Left Body Simultaneously DEWI, ERVIN MASITA; SETIAWAN, AWAN WAHYU; HADIYOSO, SUGONDO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 4: Published October 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.1032

Abstract

ABSTRAKDeteksi dini penyakit kardiovaskular sangat diperlukan untuk mengurangi risiko kematian. Deteksi dini penyakit kardiovaskular dapat dilakukan dengan bermacammacam metode, salah satunya adalah menggunakan metode Ankle Brachial Indeks (ABI). Metode ini membandingkan tekanan darah antara sistole pada bagian tangan dan kaki secara bersamaan. Pada penelitian ini dibuatlah alat pengukur ABI yang dapat mengukur secara serempak antara bagian tubuh kanan dan kiri, yaitu merupakan pengembangan dari penelitian sebelumnya yang hanya dapat melakukan pengukuran pada satu sisi tubuh saja. Dengan pengukuran secara serempak, diharapkan hasil yang diperoleh lebih akurat dan lebih efektif. Hasil validasi dari alat ini setelah dibandingkan dengan sphygmomanometer memiliki akurasi sebesar 96.6%. Selain itu data riwayat pemeriksaan dapat disimpan dan diakses oleh pasien dan dokter melalui teknologi IoT.Kata kunci: deteksi dini, kardiovaskular, Ankle Brachial Indeks, IoT ABSTRACTEarly detection of Cardiovascular Disease (CVD) is needed to reduce the risk of death. Early detection of cardiovascular disease can be done using various methods, one of which is the Ankle Brachial Index (ABI) method. This method compares blood pressure between systoles on the hands and feet simultaneously. In this study, the ABI measuring instrument was made that could simultaneously measure the right and left parts of the body, a development from previous research that could only take measurements on one side of the body. With simultaneous measurements, the results will be more accurate and effective. The validation results of this tool, when compared with the sphygmomanometer, have an accuracy of 96.6%. Besides, patients and doctors can store and access examination history data through IoT platform.Keywords: early detection, cardiovascular, Ankle Brachial Indeks, IoT
Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network Widadi, Rahmat; Rizal, Achmad; Hadiyoso, Sugondo; Fauzi, Hilman; Said, Ziani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4129-4137

Abstract

Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.
IMPLEMENTASI KONTAINER KANTOR UNTUK MENDUKUNG PROGRAM SINDULANG NETCONNECT SEBAGAI STASIUN WI-FI PUBLIK DAN RUANG KOMUNITAS DIGITAL Irawati, Indrarini Dyah; Hadiyoso, Sugondo; Munadi, Rendy; Hertiana, Sofia Naning; Istikmal, Istikmal
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 7 (2024): PKMCSR2024: Kolaborasi Hexahelix dalam Optimalisasi Potensi Pariwisata di Indonesia: A
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v7i0.2372

Abstract

The Sindulang Village community faces obstacles in internet access due to uneven network infrastructure. This challenge makes it difficult for residents to obtain the latest information which is a key factor in supporting economic progress, education, and community welfare in the area. These obstacles arise due to geographical conditions that are difficult to access and suboptimal regional structures, which cause significant obstacles in resource management and accessibility. The Telkom University, PT. Tigaresi Bangun Nusaperdana, and SMK Al Amah Sindulang community service teams together overcome these obstacles by building Sindulang NetConnect as a public Wifi station as an innovative answer to provide easy internet access while accelerating the digitalization process for the Sindulang Village Community. In addition, in order to develop the Teaching Factory (TeFa) at SMK Al Amah Sindulang, an office container was implemented as a means of producing a Wifi network that can be used by students to improve their skills and experience in the networking field. This implementation was carried out in 2 stages, where the first stage was the construction of an office container and public Wifi services and in the second year a digital community space would be developed. Based on the results of the questionnaire on 30 respondents, it showed that 93.3% strongly agreed with the implementation of office containers as public Wifi stations and TeFa facilities that provide benefits for the Sindulang Village Community and students of SMK AL Amah Sindulang.
Implementation of Ensemble Machine Learning with Voting Classifier for Reliable Tuberculosis Detection Using Chest X-ray Images with Imbalance Dataset Jauhari, Muhammad I; Wirakusuma, Muhammad P.; Sidqi, Anka; Putra, I Gusti Ngurah R. A.; Wijayanto, Inung; Rizal, Achmad; Hadiyoso, Sugondo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.472

Abstract

Tuberculosis (TB) is an infectious disease caused by bacteria. Tuberculosis is spread through the air and saliva that contain mycobacterium tuberculosis. If not treated immediately, it can spread to other vital organs, such as the heart and liver, and can even lead to death. In this study, we developed a severe tuberculosis detection system using the Tuberculosis (TB) dataset with simple computation. We used 4200 data points (3500 Normal and 700 TB). In other words, this research aimed to create lightweight computation with Machine Learning (Voting Classifier in Ensemble Learning) as the classifier using Imbalance data. Initial experiments used single machine learning with the best-performing models, Support Vector Machine (SVM), and Random Forest as classifiers. With an accuracy of 98.6% and 98%, they were combined using Ensemble Learning without feature extraction; the accuracy, AUC, Recall, Precision, and F1-score using the voting classifier were 99.1%, 99.3%, 99%, 98%, and 98%, respectively.
Pemberdayaan PKK melalui Implementasi Pertanian Hidroponik di RW. 08 Desa Cipagalo, Kabupaten Bandung Guna Mendukung Ketahanan Pangan Anwar, Radial; Hadiyoso, Sugondo; Putri, Hasanah
Abdibaraya Vol 4 No 01 (2025): Abdibaraya: Jurnal Pengabdian Masyarakat
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/abdibaraya.v4i01.1510

Abstract

This community service activity aims to implement hydroponic farming in the PKK group of RW 08, Cipagalo Village, Bandung Regency, as an effort to support local food security. Hydroponics is a farming method that does not use soil but instead relies on water enriched with essential nutrients required by plants. This method was chosen due to its efficiency in land and water usage, as well as its ability to produce faster and higher-quality harvests. The training and implementation of hydroponic farming involved the women of the PKK group as the primary participants, with the goal of enhancing their knowledge and skills in modern farming techniques. This activity also aims to empower the local community, especially women, to support family and community food security. In the context of the Sustainable Development Goals (SDGs), this initiative contributes to several objectives, including zero hunger, decent work and economic growth, and sustainable cities and communities..The results of this activity demonstrate that hydroponic farming can be successfully applied in the PKK environment of RW 08, Cipagalo Village. The training participants were able to master basic hydroponic techniques and independently produce fresh vegetables. Additionally, this program successfully raised community awareness about the importance of food security and environmental sustainability
Security System for Door Locks Using YOLO-Based Face Recognition Putri, Hasanah; Hadiyoso, Sugondo; Putri Fatoni, Salwa Berliana; Octaviany, Vany; Wulandari, Astri; Aprilina, Riska; Rosmiati, Mia
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2410

Abstract

Di era kemajuan teknologi dan algoritma canggih yang memudahkan hidup manusia, kunci pintar pengenalan wajah merupakan sistem yang menggunakan salah satu algoritma tersebut dan mengatasi masalah keamanan dalam teknologi rumah pintar. Kunci pintar ini dapat dipasang di dekat pintu untuk memantau rumah, perusahaan, dan universitas. Masalah dengan solusi kunci pintar pengenalan wajah saat ini adalah bahwa kunci pintar tersebut kurang cepat dan tepat. Pintu merupakan salah satu komponen bangunan yang perlu diperhatikan keamanannya untuk mencegah upaya pencurian. Bangunan yang memiliki banyak ruang harus memiliki pintu dengan sistem keamanan yang kuat, salah satunya adalah hotel. Alat yang sering digunakan untuk mengakses kamar hotel adalah RFID. Mobil RFID memiliki banyak kekurangan, antara lain tamu sering meninggalkan kartu RFID mereka di kamar sehingga mereka tidak dapat lagi memasuki kamar dan harus melapor ke resepsionis terlebih dahulu, kartu RFID juga mudah hilang sehingga tamu yang kehilangan kartu RFID akan didenda sebagai biaya penggantian kartu. Oleh karena itu, dibuatlah sistem keamanan pintu menggunakan pengenalan wajah dengan algoritma YOLO. Algoritma YOLO digunakan untuk mendeteksi wajah siapa saja yang ingin mengakses pintu. Hasil pengujiannya adalah sistem dapat mendeteksi wajah dengan tingkat akurasi 94,4%.
Application of Hybrid Metaheuristic Algorithms for Feature Selection in Event-Related Potential Classification in Problematic Gamers Using Electroencephalograph Signal Wijayanto, Inung; Hadiyoso, Sugondo; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.638

Abstract

Online games have become a popular form of entertainment, particularly for relieving stress, and the rise in online gaming has led to an increase in problematic gaming behaviors. Excessive use of the internet for gaming has raised concerns about its neurophysiological impact, particularly on cognitive and emotional functions. Electroencephalogram signal and Event-Related Potential analysis are valuable tools for monitoring these effects. Given the vast amount of features that can be extracted from EEG signals, it is crucial to apply efficient feature selection methods to identify the most informative ones. This study utilizes the Go/No-Go Association Task combined with the recording of 16-channel EEG signals, chosen as the data-recording method to observe the response of individuals who are problematic online gamers to several stimulus themes. In this context, metaheuristic algorithms like Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization are employed to enhance feature selection. A hybrid approach, combining one of these methods with Binary Stochastic Fractal Search is proposed to improve classification accuracy and optimize feature selection. The results demonstrate that the hybridization of the best algorithm with B-SFS successfully selects the optimal features, achieving perfect classification performance, with an accuracy, sensitivity, and specificity of 1.00 for all respondents. This emphasizes the effectiveness of B-SFS, particularly its diffusion process, where Gaussian distribution facilitates the search for the best solution, thereby improving the reliability of feature selection for detecting problematic gaming behavior.
DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification Lutvi Murdiansyah Murdiansyah; Gelar Budiman; Indrarini Dyah Irawati; Sugondo Hadiyoso; A. V. Senthil Kumar
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.4506

Abstract

Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that integrates signal acquisition via compressed sensing (CS) and machine/deep learning for inference tasks directly on a small number of measurements, On the other hand, in real-world high-resolution (HR) data, where the image dataset is very limited CL, has the drawback of reduced accuracy under conditions of aggressive compression ratio. Here, a reconstruction method is necessary to maintain high levels of accuracy. To address this, we proposed a framework Deep Learning (DL) and Compressive Sensing that processing a small dataset of 92 images maintaining high accuracy. The framework developed in this paper employs processing sensing matrix A in compressive sensing with two transformation methods: DCT CL with Multi Neural Networks and the SVD method with GoogleNet framework. To maintain the same computation efficiency as DCT Compressive learning, SVD with GoogleNet framework provides a solution for object recognition, achieving accuracy values ranging from 89.47% to 63.15% for compression ratios of 3.97 to 31.75. This performance shows a linear tendency concerning the PSNR level, an index of signal reconstruction quality, and demonstrates an efficient process in the S matrix.
Advanced Smart Bracelet for Elderly: Combining Temperature Monitoring and GPS Tracking Sugondo Hadiyoso; Indrarini Dyah Irawati; Akhmad Alfaruq; Tasya Chairunnisa; Muhamad Roihan; Suyatno Suyatno
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.6182

Abstract

Indonesia is entering an aging population period, marked by an increase in the number of elderly individuals, accompanied by a rise in dementia cases. This situation leads to higher dependency among the elderly on others for assistance or long-term care. Dementia can cause elderly people to lose their sense of direction, often wandering aimlessly, making them difficult to track. To address this issue, a wearable smart bracelet is proposed to monitor the location and a vital body parameter such as body temperature. The system is equipped with a tracking application that can send an alert if the user is outside a designated area. It automatically sends a warning message to the caregiver's or family member's smartphone when abnormal signs are detected. The bracelet is designed like a wristwatch, to be worn on the wrist. It is small, lightweight, and battery-operated. Temperature and location data can be transmitted in real-time using an internet network to mobile devices. The device can notify when the user is outside the specified area. Test results indicate that the device has high accuracy and reliability in monitoring location and body temperature with accuracy around 98.5%, as well as sending notifications through a Telegram bot when certain thresholds are exceeded. This device can work properly for up to 5 hours on a single battery charge. With this device, it is expected to help monitor and support the care of the elderly so that they can improve their quality of life. This device can also provide an emergency alarm if the elderly are outside the area.
Co-Authors A. V. Senthil Kumar A.A. Ketut Agung Cahyawan W Aaron Abel Abi Hakim Amanullah Achmad Rizal Achmad Rizal ADIANGGIALI, ANYELIA Adisaputra, Rangga Adiwijaya, Agustinus Aldian Adjie Gery Ramadhan Adnan Azhary Afandi, Mas Aly Agung Muliawan Ahmad Hilmi Ahmad Muammar Agusti Akhmad Alfaruq Akhmad Alfaruq Alfaruq, Akhmad Alfaruq, Akhmad Aliffansyah, Lingga Alvinas Deva Sih Illahi Ana Durrotul Isma Anatasya Bella Andhita Nurul Khasanah Andri Juli Setiawan Andro Harjanto Anggit Syorgaffi Anggun Fitrian Isnawati ANGGUNMEKA LUHUR PRASASTI Arfianto Fahmi Arif Indra Irawan ARIS HARTAMAN Ashshiddiqqi, Muhammad Arhizal Asril Ibrahim Astri Wulandari Ayu Chellsya, Ananda Azahra, Yasmin Azriel Gilbert Samuel Rogito Azzahra, Salwa Bagus Tri Astadi Balova , Fathrurrizqa Bambang Hidayat Bandiyah Sri Aprillia Barus, Exal Deo Jayata Bayu Erviga Yulanda Setiawan Bayuaji Kurniadhani Bimo Rian Tri Nugroho Budhi Irawan Budi Prasetya Budiyawan Naztin Burhanuddin D. Burhanuddin Dirgantoro Cucu Fitri Dadan Nur Ramadan Dadan Nur Ramadhan Dadan Nur Ramadhan Denny Darlis Dewi Rahmaniar, Thalita Dharu Arseno Didin Bramastya Dieny Rofiatul Mardiyah Diliana, Faizza Haya Efri Suhartono Ema ERVIN MASITA DEWI Exal Deo Jayata Barus Ezi Rohmat Fadiaga Omar Michlas Fairuz Azmi FAJRI, SETIO EKA FARDAN FARDAN Farrel Fahrozi Fathrurrizqa Balova FATURRAHMAN, RAIHAN Fauzia Anis Sekar Ningrum Fony Ferliana Widianingrum Gadama, Melsan Gelar Budiman Ghilman Hafizhan Gifari, Rizqi Al Habib, Arrijal Hadjwan, Razel Hannissa Sanggarini Hariyani , Yuli Sun Hasanah Putri Hengky Yudha Bintara Heru Nugroho Hilman Fauzi, Hilman HUMAIRANI, ANNISA Hurianti Vidyaningtyas HW, EVA AISAH Ilham Edwian Berliandhy Ilmi, M. Bahrul Indrarini Dyah Irawati Inung Wijayanto Irsyad Abdul Basit Istikmal Ivany Sesa Rehadi Ivosierra Andrea Larasaty Jannah, Firna Noor Jannah, Sabila Hayyinun Jasmine, Diva Dhila Jauhari, Muhammad I Javani Sekar Larasati Jehan Pratama Herdaning Jondri Jondri Koredianto Usman Kridanto Surendro Kris Sujatmoko Kurnia Ismanto, Rima Ananda Larasaty, Ivosierra Andrea Lata Tripathi, Suman LATIP, ROHAYA Ledya Novamizanti Lurina, Manda Luthfi Muhammad Pahlevi Lutvi Murdiansyah Murdiansyah M. Nur Imam DJ Mahmud Dwi Sulistiyo Manda Lurina Meidatomo , Muhammad Haykal Milan Adila Amalia Mohamad Ramdhani Muh. Kurniawan, A. Muhamad Roihan Muhammad Adnan Muhammad Afif Ridwansyah Muhammad Iqbal Muhammad Iqbal MUHAMMAD JULIAN, MUHAMMAD Nadya Silva Arline Nasution, Muhammad Ilham Kurniawan Nasution, Seri Wahyuni Naufal Juhaidi Jafal Naufal Rizky Pratama Nur Arviah Sofyan Nur Pratama, Yohanes Juan Nur Ramadhani Nursanto Nursanto NURSANTO NURSANTO, NURSANTO Nurwan Reza Fachrurrozi Okki Rahmalisty, Fiona Pahira, Ela Diranda Permana, Andri Satia Prahara, Dzakwan Bahar Prajna Deshanta Ibnugraha Putra, I Gusti Ngurah R. A. Putri Fatoni, Salwa Berliana Putri, Athaliqa Ananda Putri, Silvi Dahlia R. Dhenake Aghni Bunga R. Yunendah Nur Fu’adah Radial Anwar, Radial Radian Sigit Raditiana Patmasari Rahmaniar, Thalita Dewi Rahmat Widadi Ramdani, Ahmad Zaky Ratna Mayasari Reivind P. Persada RENALDI, LUKY RENALDI, LUKY RENDIKA, ANANDA Rendy Munadi Reni Dyah Wahyuningrum Reny Yuliani Arnis Rina Pudji Astuti Riska Aprilina Rita Magdalena Rita Purnamasari Rizal Fachrudin Maulana Rizky Aulia Rahman Robinzon Pakpahan Rogito, Azriel Gilbert Samuel ROHMAT TULLOH Rosmiati, Mia Ruli Pandapotan, Bagas Ryan Bagus Wicaksono Safitri, Ayu Sekar Said, Ziani Sania Marcellina Bryan Sasmi Hidayatul Yulianing Tyas Sa’idah, Sofia Sekar Safitri, Ayu Septiansyah, Rizky SETIAWAN, AWAN WAHYU Sianturi, Kristian Fery Sidqi, Anka Sigit, Radian Siti Sarah Maidin Siti Zahrotul Fajriyah Sofia Naning Hertiana Suci Aulia Sugeng Santoso Sulistyo, Tobias Mikha Surya Putra Agung Saragih Suyatno Suyatno Syifa Nurgaida Yutia Tasya Chairunnisa Tati Latifah Erawati Rajab Teguh Musaharpa Gunawan Thomhert Suprapto Siadari Tita Haryanti Tobing, Goldfried Manuel Lbn Tri Nopiani Damayanti Triadi Triadi Unang Sunarya Untari Novia Wisesty Utami, Ayu Tuty Vany Octaviany Vera Suryani Wahyu Hauzan Rafi Wibowo, Raiyan Adi Wirakusuma, Muhammad P. Yasmin Azahra Yoza Radyaputra Yudha Purwanto Yudiansyah Yudiansyah YULI SUN HARIYANI YUYUN SITI ROHMAH Zahrah, Nasywa Nur Zhillan Al Rashif, Mohammad Zulfikar F.M. Ramli