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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Ranggagading (JIR) Sinergi Fakultas Ekonomi JURNAL ILMIAH SOCIETY Kitektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Sari Pediatri Jurnal Kedokteran Gigi Universitas Padjadjaran Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Economic, Bussines and Accounting (COSTING) JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Kebijakan Perikanan Indonesia Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT JETL (Journal Of Education, Teaching and Learning) INTERNATIONAL JOURNAL OF NURSING AND MIDWIFERY SCIENCE (IJNMS) JISIP: Jurnal Ilmu Sosial dan Pendidikan Journal of Electronics, Electromedical Engineering, and Medical Informatics JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Pemerintahan dan Politik Jurnal Ilmiah Edunomika (JIE) International Journal of Economics Development Research (IJEDR) Indonesian Journal of Electrical Engineering and Computer Science Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Cahaya Mandalika Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Journal of Government Science (GovSci ) : Jurnal Ilmu Pemerintahan Journal of Emerging Business Management and Entrepreneurship Studies Green Intelligent Systems and Applications JTechLP Jurnal Pengabdian Masyarakat Bangsa Jurnal Rekayasa elektrika Journal of Computing Theories and Applications Triwikrama: Jurnal Ilmu Sosial JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal INFOTEL Akuntansi: Jurnal Riset Ilmu Akuntansi GEMBIRA (Pengabdian Kepada Masyarakat) Indonesian Journal of Community Services Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Sriwijaya Electrical and Computer Engineering (Selco) Journal Jurnal Pengabdian Rekayasa dan Wirausaha Jurnal Teknokes Nawadeepa: Jurnal Pengabdian Masyarakat Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Pengabdian Rekayasa dan Wirausaha
. Melinda
Department Of Electrical And Computer Engineering, Faculty Of Engineering, Universitas Syiah Kuala
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Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda, Melinda; Maulisa, Oktiana; Nabila, Nissa Hasna; Yunidar, Yunidar; Enriko, I Ketut Agung
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.
Image Data Acquisition and Classification of Vannamei Shrimp Cultivation Results Based on Deep Learning Melinda Melinda; Zharifah Muthiah; Fitri Arnia; Elizar Elizar; Muhammad Irhmasyah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3850

Abstract

This research aimed to employ deep learning techniques to address the classification of Litopenaeus vannamei cultivation results in land ponds and tarpaulin ponds. Despite their similar appearance, distinguishable differences exist in various aspects such as color, shape, size, and market price between the two cultivation methods, often leading to consumer confusion and potential exploitation by irresponsible sellers. To mitigate this challenge, the research proposed a classification method utilizing two Convolutional Neural Network (CNN) architectures: Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50), renowned for their success in various image recognition applications. The dataset comprised 2,080 images per class of vannamei shrimp from both types of ponds. Augmentation techniques enhanced the dataset’s diversity and sample size, reinforcing the model’s ability to discern shrimp morphology variations. Experiments were conducted with learning rates of 0.001 and 0.0001 on the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizers to evaluate their effectiveness in model training. The VGG-16 and ResNet-50 models were trained with a learning rate parameter of 0.0001, leveraging the flexibility and reasonable control provided by the SGD optimizer. Lower learning rate values were chosen to prevent overfitting and enhance training stability. The model evaluation demonstrated promising results, with both architectures achieving 100% accuracy in classifying vannamei shrimp from soil ponds and tarpaulin ponds. Furthermore, experimental findings highlight the superiority of using SGD with a learning rate of 0.0001 over 0.001 on both architectures, underscoring the significant impact of optimizer and learning rate selection on model training effectiveness in image classification tasks.
Pelatihan dam implementasi eko enzim mendukung pertanian ramah lingkungan memitigasi ketahanan pangan di Pesantren Al Kautsar, Medan Ameilia Zuliyanti Siregar; Zulkifli Nasution; Meutia Nauly; Tulus Tulus; Netti Herlina Siregar; Ichwana Ramli; Mahidin Mahidin; Nasrul Nasrul; Indera Sakti Nasution; Melinda Melinda; Muhibuddin Muhibuddin
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 5 (2025): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i5.34006

Abstract

Abstrak Kegiatan Pengabdian masyarakat Kolaborasi Indonesia (PMKI) USU 2025 sebagai co-host dengan host Universitas Syiah Kuala (USK) ini dilaksanakan di Pondok Pesantren Al Kautsar Al Akbar, Medan, dengan tujuan meningkatkan pengetahuan dan keterampilan santri dalam mengolah sampah organik menjadi ekoenzim sebagai produk ramah lingkungan yang bermanfaat. Metode yang digunakan adalah Asset Based Community Development (ABCD) melalui tahapan discovery, dream, design, define, dan destiny. Pelaksanaan meliputi Sosialisasi, Pelatihan pembuatan ekoenzim, pembuatan desinfektan berbahan ekoenzim, serta implementasinya pada kegiatan berkebun. Sebanyak 40 santri (15 putra dan 25 putri, rentang usia 14-17 Tahun, santri dari kelas 1, 2, 3 SMA) yang mengikuti kegiatan,  diawali dengan pre-test dan diakhiri dengan post-test. Hasil evaluasi menunjukkan peningkatan pemahaman signifikan, dengan rata-rata nilai pre-test 65,25 dan post-test 93,5. Sebanyak 95% peserta menyatakan kegiatan sangat menarik, 93% menyatakan bermanfaat, dan 89% menyatakan pemahamannya meningkat. Kegiatan ini berhasil membekali santri dengan keterampilan pengolahan sampah organik menjadi produk bernilai guna dalam bentuk eko enzim yang mendukung pertanian ramah lingkungan dan mitigasi masalah ketahanan pangan di lingkungan pesantren. Kata kunci: ekoenzim; pengolahan sampah organik; pertanian ramah lingkungan; pengabdian masyarakat. Abstract The Indonesian Collaboration Community Service (ICCS) of University Sumatera Utara Programme 2025 as co-hosy qith host was University Syiah Kuala (USK) was conducted at Pondok Pesantren Al Kautsar Al Akbar, Medan, aiming to enhance students’ knowledge and skills in processing organic waste into eco-enzyme, an environmentally friendly and beneficial product. The program adopted the Asset Based Community Development (ABCD) method through five stages: discovery, dream, design, define, and destiny. Activities included awareness sessions, eco-enzyme production training, eco-enzyme-based disinfectant preparation, and its application in gardening activities. A total of 40 students (15 males and 25 females, age rates was 14-17 years old, consit of 1, 2, 3 SMA students),  starting with a pre-test and ending with a post-test. Evaluation results indicated a significant improvement in understanding, with average pre-test and post-test scores of 65.25 and 93.5, respectively. Furthermore, 95% of participants stated the program was very interesting, 93% found it beneficial, and 89% reported increased comprehension. This program successfully equipped students with skills in organic waste processing into value-added products, while supporting environmentally friendly agriculture and contributing to food security resilience in the pesantren environment. Keywords: eco-enzyme; organic waste processing; community service; environmentally friendly agriculture.
Autism Face Detection System using Single Shot Detector and ResNet50 Melinda Melinda; Muhammad Fauzan Alfariz; Yunidar Yunidar; Agung Hilm Ghimri; Maulisa Oktiana; Rizka Miftahujjannah; Nurlida Basir; Donata D. Acula
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Muhammad Irhamsyah; Melinda Melinda; Yunidar Yunidar; Ikram Muttaqin; Lailatul Qadri Zakaria
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.
Mobile Application Development for Facial Classification of Autistic Children Based on MobileNet-V3 Irsyan Ramadhan; Melinda Melinda; Yunidar Yunidar; Donata D Acula; Rizka Miftahujjannah; Siti Rusdiana; Zulfan Zainal
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1363

Abstract

Early detection of autism spectrum disorder (ASD) is crucial to support timely interventions that can improve children’s cognitive and social development. However, conventional approaches still rely on subjective observations and parental reports. This study proposes the development of a Flutter-based mobile application for face classification of autistic and non-autistic children using the MobileNetV3-Small architecture. The dataset contains 600 original facial images of children aged 4 to 14 years (300 autistic and 300 non-autistic), which were expanded to 1,860 images through augmentation techniques such as Gaussian noise addition, flipping, and contrast adjustment. The model was trained using transfer learning and optimized with the SGD optimizer and sigmoid activation function. During training, the model achieved a training accuracy of 95.27% and a validation accuracy of 97.92%, indicating effective learning with minimal overfitting. Evaluation on the test data showed perfect performance, with accuracy, precision, recall, and F1-score all reaching 100%. The model was then converted to TensorFlow Lite format to allow on-device inference on mobile platforms. The app enables users to upload photos via camera or gallery and instantly receive classification results, which are also saved to Firebase for history tracking. Testing showed a fast response time (1–2 seconds) and a smooth, user-friendly experience. These results highlight the potential of the system as a lightweight, efficient, and accessible facial image-based ASD screening tool, particularly in regions with limited access to specialized healthcare. Future work should include validation using larger and more diverse datasets across different demographics to ensure model robustness, fairness, and generalizability in real-world environments.
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.023

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
Increasing Teacher Professionalism at SMK Negeri Penerbangan Aceh Through Training Scientific Writing and Scientific Work Development: Meningkatkan Profesionalisme Guru di SMK Negeri Penerbangan Aceh Melalui Pelatihan Tata Tulis Ilmiah dan Pengembangan Karya Ilmiah Yuwaldy Away; Saminan; Rika Sri Utami; Safrizal Razali; Melinda; Muslimsyah
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 7 No. 3 (2023): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

SMK Negeri Penerbangan Aceh is one of the educational institutions in the Aceh region that focuses on vocational education. In this community service initiative, the team identified the challenges faced by teachers at this school, as the lack of motivation and time constraints in writing academic papers. In an effort to address these issues, the team designed and implemented a training program that encompasses academic writing techniques and the use of research software. The evaluation results show a significant improvement in the motivation, self-confidence, and knowledge of teachers related to academic paper writing. This program also facilitates collaboration among teachers, members of the team, and guest speakers. This collaboration provides emotional support and a deeper understanding of research strategies and the application of technology in developing academic papers. The contribution of this community service initiative aids teachers at SMK Negeri Penerbangan Aceh in enhancing their ability to write academic papers, which supports the requirements for promotion and professional career advancement. This training program also has the potential to be implemented in other educational institutions facing similar challenges in improving teacher professionalism.
Enhancing Teachers' Scientific Writing Skills Through Community Service: A Case Study at SMK Negeri 1 Darul Kamal: Penguatan Keterampilan Penulisan Ilmiah Guru Melalui Pengabdian kepada Masyarakat: Studi Kasus di SMK Negeri 1 Darul Kamal Melinda; Budi Arianto; Safrizal Razali; Yuwaldi Away; Nurmalia Zakaria
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 8 No. 4 (2024): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

The Community Service Program at SMK Negeri 1 Darul Kamal is designed to enhance the scientific writing skills of teachers, with the aim of improving the quality of publications and teaching materials. The program includes an initial socialization of its objectives, discussions to identify writing needs, as well as careful coordination and development of training modules. Conducted through interactive workshops, the training utilizes advanced technologies such as data analysis software and scientific reference management tools. Evaluation of the program through pre-test and post-test questionnaires demonstrated significant improvements in methodology understanding, writing habits, motivation for writing, and mastery of educational technology. Notable improvements were recorded with average scores for understanding research methodologies increasing from 2.9 to 4.4, and motivation for writing rising from 2.6 to 3.6. These results validate the success of the training in improving both the quality and efficiency of scientific writing among teachers. Based on these findings, it is recommended that similar training programs be extended to more educators across Indonesia to help raise educational standards and enhance teacher professionalism nationally. This initiative not only supports profession development but also aligns with national educational goals, thereby making a significant contribution to the academic community.
Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA Cut Siti Azola Syiva; Melinda Melinda; Syahrial Syahrial; Imam Fathur Rahman; Souvik Das; M. Ary Heryanto
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15875

Abstract

Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.
Co-Authors . Roslidar Aafiyah, Siti Afra Abdurohim Abdurohim, Abdurohim Abed Nego, Abed Abrina Anggraini, Sinar Perbawani Achmad Maqsudi, Achmad Achmad, Ilham Afnan, Afnan Agnesia Candra Sulyani Agung Hilm Ghimri Ahmad, R. Andriadi Ahmadiar, Ahmadiar Ainil Syafitri Akbar, Alif Yafi Al Bahri Alam Mahadika, Alam Mahadika Albar, Nizam Alfatirta Mufti Alfatirta Mufti Alfian, Ridho Alfita Khairah Alfita Khairah Alifia, Rania Sofie Amalia Amalia Amaliatulwalidain, Amaliatulwalidain Ameilia Zuliyanti Siregar Ameilia Zuliyanti Siregar Anabel, Cendana Ananda, Mulya Anik Puryatni Anto Ariyanto Anzelina, Dhea Eprillia Aqif, Hurriyatul Ari Rahmat Putra Ibina Ariyani, Amra Arsy Febrina Dewi Arumi, Naila Azaria Arya Suyanda Asriati Asriati, Asriati Astuti, Meti Aufa Rafiki Aufa Rafiki Aufa Rafiki Aufa Rafiki Aufa Rafiki Aulia Arafat Aulia Rahman Aurelia, Gabrella Azhari, Rizki AZMI, MUHAMMAD RAUDHI Azra, Ery Bashir, Nurlida Basir, Nurlida Basuki Toto Rahmanto Bil Haki, Arif Binti Basir, Nurlida Budi Arianto Buleun Waladah Catur Andryani, Nur Afny Cloudya, Cindy Cut Salsabilla Azra Cut Siti Azola Syiva D Acula, Donata Diana Novita Diana, Fitri Dini, Siti Doke, Herlina Theodensia D. Donata D Acula Donata D Acula Donata D. Acula Duana, Maiza Dwi Rosalina Dwita Sakuntala Dzaki Dhiya Ul-Haq E Elizar Elizar Elizar Elizar Elizar Elizar Elizar Elizar Elizar, Elizar Ellsa Fitria Sari Elsy Rahajeng, Elsy Elya, Chayara Alima Rameyza Erika Lety Istikhomah Puspita Sari* Ernita Dewi Meutia Fadilah Amanda Putri Fahmi Fahmi Fajrul Islamy Fakhrus Syakir Farhan Fathur Rahman, Imam Fathurrahman Fathurrahman Fitri Arnia Fitri Indira Nauli Fitriyanti, Emiliy Fuaidah, Mahayaya Gazali, Syahrul Gopal Sakarkar Hamdani Hamdani Hanryono, Hanryono Hanum Aulia Harahap, Subur Harjoedi Adji Tjahjono, Harjoedi Adji Hasan, Hafidh Hasan, Vania Pratama Heltha, Fahri Hendrik Leo Herlina Dimiati Herlina Dimiati, Herlina Herlina Herlina Herwanto, Agus Holyness Nurdin Singadimedja Hubbul Walidainy I Gusti Bagus Astawa I Ketut Agung Enriko Ichwana Ramli Ichwana Ramli Ichwana Ramli Iis Juniati Lathiifah Ikram Muttaqin Imam Fathur Rahman Imam Fathur Rahman Inda Mariana Harahap Indarti, Ghinna Yulia Indera Sakti Nasution Indera Sakti Nasution Indriani, Berlian Irawan Irawan Irli Masitah Marpaung Irsyan Ramadhan Irvan kurniawan, Muhammad Iskandar Hasanuddin Iskandar Hasanuddin Iskandar Hasanuddin Islamy, Fajrul J Prayoga Joanita Jalianery Junidar Junidar Junidar Karlisa Priandana Kencana, Novia Khairani Khairani Khairia, Syaidatul Kristiana kristiana Lailatul Qadri Zakaria Lailatul Qadri Zakaria Lailatul Qadri Zakaria Lailatul Qadri Zakaria Lerrick, Yudith F. Lisbeth Lesawengen, Lisbeth Lucky, Muhammad Luju, Elisabet Lukman Hidayat M Ary Heryanto M Fahrur Rozi M Ghailan Dhiaulhaq Magfirah, Inayah Zaini Mahdi Syukri Mahfuzha, Raudhatul Mahidin Mahidin Mahidin Mahidin Malahayati, M. Margarethy Rohanie Mbado Maulana Imam Muttaqin Maulana, Muhammad Iqbal Maulisa Oktiana Maulisa Oktiana Maulisa, Oktiana Mauliza Putri Mauliza Putri Maya Fitria Mayanti, Andi Mega Fatimah Rosana Meutia Nauly Miftahujjannah, Rizka Mirza Rahmat, Muhammad Mohd. Syaryadhi Morita Sari Muhajir Muhajir Muhamad Risal Tawil Muhammad Dika Rafi Kasha Muhammad Fauzan Alfariz Muhammad Furqan Muhammad Hakkan Syukur Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhmasyah Muhammad Irwanto Muhammad Ridwan Muhammad Saifullah Nur Muhammad Saifullah Nur Muhammad Syafrudin Muharratul Mina Rizky Muharratul Mina Rizky Muhibbuddin Muhibbuddin Muhibuddin Muhibuddin Muhibuddin Muhsin Muhsin Muliyadi Muliyadi Muliyadi Muliyadi Muliyadi Muliyadi Mulyadi Mulyadi Mulyadi, Yose Ega Muslimsyah Mustikawati, Yunitari N Nasaruddin Nabella, Putri Rama Nabila, Nissa Hasna Nasaruddin Nasaruddin Nasaruddin Nasaruddin Nasaruddin Syafie Nasrul Arahman Nasrul Nasrul Nasrul Nasrul Nazilla, Izza Netti Herlina Siregar Netti Herlina Siregar Niza Aulia Nofrima, Sanny Novandri, Andri Nur Afny Catur Andryani Nuraini, Endah Nurbadriani, Cut Nanda Nurfatikah, Aisyah Ariyani Nurhasanah, Lulu Nurlida Basir Nurlida Basir Nurlida Basir Nurlida Basir Nurlida Basir Nurlida Basir Nurlida Basir Nurmalia Zakaria Nusa Muktiadji OKTADINATA, ALEK Oktiana, Maulisa Peronika, Agustina Prabowo, Bangkit Yudo PRATIWI, SASKIA Prayoga, Bima Wicaksana Dwi Pringgandini, Laras Ayu Purwati, Agnes Susana Merry Purwatiningsih, Sri Desti Putra Anwar Ginting, M. Alief Akhbar Qadri Zakaria, Lailatul Rachmawati Rachmawati Rafiki, Aufa Rahmat Maulana Rahmi Susanti Raihan, Siti Rajagukguk, Katarina Rani Rakhmad Syafutra Lubis Ramadan, Muhammad Fahreza Ramadhani, Hanum Aulia Ramdhana, Rizka Ramli, Amaliatulwalidain Raudhatul Mahfuzha Razita Nadhira Rika Sri Utami Rina Ridara Rini Safitri Riska Sufina Rita Khatir Rizal Syahyadi Rizka Miftahujjannah Rizka Miftahujjannah Rizka Miftahujjannah Rizka Miftahujjannah Rizka Ramadhana Romal Ijuddin Rosmawati Rosmawati Rosmawinda Rosmawinda Rosmawinda Rosmawinda Rosminazuin Ab Rahim Rosminazuin Ab Rahim Roy Budiharjo RoziqiFath, Zain Fuadi Muhammad Rusdha Muharar Rusmardiana, Ana Ruzdy, Nabilah Nameera saepudin, udin Safrizal Razali Safrizal Razali Saifullah Nur Muhammad Sakarkar, Gopal Saminan Sanjani, Fenti Sanny Nofrima, Sanny Nofrima Saputra, Nanda Sari*, Erika Lety Istikhomah Puspita Setiawan, Verdy Shaquille Rizki Ramadhan Na Silaban, Keysha Octarina Silaban, Pangeran O. J Simanjorang, Rican Siska, Emi Yulia Siti Rofiah, Siti Siti Rusdiana Siti Rusdiana Siti Rusdiana Siti Rusdiana Siti Rusdiana Siti Rusdiana Siti Rusdiana Siti Rusdiana Sitti Suhada Solissa, Ferdinando Souvik Das Souvik Das Sri Rahayu Ade Sugiatno . Suhara, Ade Sulastri Sulastri Suriadi Suriadi Suriati, Israini Suwandi Suwandi Suyanda, Arya Syahputra, Daniel Syahrial, Syahrial Syahrul Gazali Syahrul Gazali Syahrul Gazali Syahyadi, Rizal Syakir, Fakhrus Syarifah Rauzatul Jannah Syifa Anzella Syifa Anzella Tandi, Asrin Tariliani, Cut Dara Taufik Iskandar Taufiq Abdul Gani Teuku Muhammad Mirza Keumala Teuku Yuliar Arif Tulus Tulus Tulus Tulus Tulus Tulus Ugi Nugraha Ulul Azmi Umrah, Andi Sitti Victoria Ari Palma Akadiati W.K Wong W.K Wong Waani, Fonny J Wahyudianty, Melsa Ulfie Waladah, Bulen Waladah, Buleun Wardana, Surya Wawan Junresti Daya Winarningsih, Rahayu Arum Wong, W. K Wong, W.K Wong, W.K. Yatim, Hertasning Yenti, Riza Reni Yovhandra Ockta Yudesman, Fatriani Margareta Yudha Nurdin Yudha Nurdin Yulia, Prima Dwi Yuliati - Yunidar yunidar Yunidar Wahab Yunidar Yunidar Yunidar Yunidar Yunidar Yunidar Yunidar Yunidar Yunidar yunidar Yunidar Yunidar Yunidar Yunidar Yunidar Yunidar Yusup, Syafina Ainur Yuwaldi Away Yuwaldy Away Zahra, Viqqy Nur Zahran Jemi , Faris Zainal, Zulfan Zetira, Zetira Rizqia Erlin Zharifah Muthiah Zulfan Zainal Zulfan Zulfan Zulfikar Taqiuddin Zulhelmi . Zulhelmi, Zulhelmi Zulkifli Nasution Zulkifli Nasution