Article Per Year (5 Year)
p-Index From 2021 - 2026
14.191
P-Index
This Author published in this journals
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
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 120 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 120 Documents
Search

 8   9   10   11   12 
FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION Fakhrus Syakir; Muhammad Irhamsyah; Melinda Melinda; Yunidar Yunidar; Zulhelmi Zulhelmi; Rizka Miftahujjannah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6665

Abstract

Lake Maninjau experiences periodic upwelling events that disrupt water quality, harm fish stocks, and pose socioeconomic challenges to surrounding communities. This study aimed to enhance upwelling prediction accuracy by integrating Vector Autoregressive (VAR) time series modelling with Support Vector Machine (SVM) classification. A five-year dataset (2020–2024) of daily climate variables surface temperature, precipitation, and wind speed was collected from NASA. Data stationarity was confirmed using Box-Cox transformations and Augmented Dickey-Fuller tests, while Granger Causality analysis revealed bidirectional relationships among the variables. The optimal forecasting model, VAR(17), was selected based on the Akaike Information Criterion (AIC), ensuring residuals met white-noise criteria. K-means clustering then labelled potential upwelling days, and these labels were employed to train SVM classifiers. An interactive dashboard was developed using Python and Streamlit to facilitate real-time forecasts and classification outputs. The VAR(17) model produced highly accurate forecasts, reflected by minimal error metrics (e.g., RMSE < 0.60). SVM classification of potential upwelling events achieved strong performance, consistently attaining F1-scores above 0.95. By merging time series forecasts with event classification, the hybrid VAR–SVM framework outperformed single-method approaches in identifying and predicting upwelling episodes. This integrated modelling strategy effectively addresses the complexity of upwelling in Lake Maninjau, enabling timely decision-making for fisheries management and local tourism stakeholders. Future work may incorporate additional environmental indicators (e.g., dissolved oxygen, pH) and extend dashboard functionalities to bolster sustainable resource management and community resilience
Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas yunidar; Arya Suyanda; Melinda Melinda; Lailatul Qadri Zakaria; Siti Rusdiana
Jurnal Teknokes Vol. 19 No. 2 (2026): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i2.158

Abstract

Early detection of Autism Spectrum Disorder (ASD) is a crucial challenge in child development interventions because conventional screening methods are often subjective and prone to assessor bias. This study proposes an objective solution in the form of a deep learning approach for automatic ASD classification using facial landmark representations that focus exclusively on the eye and forehead areas. The selection of these areas is based on the eye avoidance hypothesis, which states that these regions contain very rich diagnostic information and behavioral biomarkers related to the ASD phenotype. The pre-processing stage involves isolating the eye and forehead areas using Dlib 68-landmark detection to eliminate background visual noise, followed by detailed topological visualization using MediaPipe Face Mesh with 478 landmark points as the model input. The Convolutional Neural Network (CNN) architecture used is the VGG19 model modified with transfer learning techniques and the addition of Dropout layers to improve efficiency and prevent overfitting. The model was trained on a primary dataset of 1,238 images collected under controlled conditions from children in Banda Aceh. The test results showed very promising performance with an overall accuracy of 94.35%. Specifically, the model achieved a recall (sensitivity) of 95.24%, a precision of 93.75%, and an AUC score of 0.9831. This high sensitivity is crucial in a medical context to minimize the risk of misdetection of positive cases. These results demonstrate that landmark visualization in the eye and forehead areas with the VGG19 model is a highly effective, accurate, and practical method for serving as an economical early screening tool for ASD.
ECG Signal-based Classification of Physiological States in ASD Children DWT and Machine Learning Muhammad Irhamsyah; Hanum Aulia; Yunidar Yunidar; Melinda Melinda; Muhsin Muhsin; Syarifah Rauzatul Jannah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2750

Abstract

ASD is a neurodevelopmental disorder that affects a child's ability to regulate emotions, interact socially, and respond to environmental stimuli. Monitoring physiological conditions in children with ASD is challenging because it relies on subjective observations. In this study, physiological conditions are defined based on observable behavioral states, namely active and quiet. The active state reflects increased physiological arousal and a higher heart rate, while the quiet state represents a resting state with a more stable heart rate. This study proposes an ECG-based classification system to distinguish between these two states. The dataset consists of 2000 samples for each class. Due to noise in the ECG signal caused by body movements, preprocessing was performed using the DWT to improve signal quality. The processed signals were then classified using three machine learning algorithms: SVM, Random Forest, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score. The results showed that without DWT, Random Forest achieved the highest accuracy of 91.00%, followed by SVM at 88.87%, and AdaBoost at 87.25%. While using DWT, Random Forest achieved the highest accuracy of 93.75%, followed by SVM at 91.37%, and AdaBoost at 90.25%. This indicates that DWT can produce better signal quality. Furthermore, Random Forest was selected as the optimal model and implemented in a Streamlit-based web application for real-time monitoring. These findings indicate that the combination of DWT and Random Forest is effective for classifying physiological conditions in children with ASD and has potential as an objective monitoring tool.
Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda Melinda; Syahrul Gazali; Yuwaldi Away; Aufa Rafiki; W.K Wong; Muliyadi Muliyadi; Siti Rusdiana
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar Yunidar; Inda Mariana Harahap; Melinda Melinda; Rosmawinda Rosmawinda; Nurlida Basir; Aufa Rafiki; Imam Fathur Rahman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1523

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Lightweight Rice Leaf Disease Classification Using MobileNetV2: A Comprehensive Performance Evaluation Melinda Melinda; Rahmat Maulana; Yunidar yunidar; Muhammad Irhamsyah; Muhammad Saifullah Nur; Nurlida Basir; Elizar Elizar; Muhammad Syafrudin
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1000

Abstract

Rice leaf diseases pose a significant threat to agricultural productivity, and accurate automated detection is essential for timely intervention. This study presents a comparative evaluation of lightweight convolutional neural network architectures for the classification of six rice leaf disease categories: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, and Rice Healthy. MobileNetV2 is proposed as the primary model and benchmarked against EfficientNetB0 and NASNetMobile. All three architectures were trained under an identical experimental setup comprising a two-stage transfer learning strategy, a unified custom classification head consisting of Global Average Pooling, Batch Normalization, two dense layers with dropout and L2 regularization, and a Softmax output layer. The dataset comprised 1,920 images across six classes obtained from Roboflow Universe, with no pre-augmentation applied by the original source. Training-time augmentation including rotation, shifting, shearing, zooming, and horizontal flipping was applied exclusively to the training subset. Experiments were conducted on a stratified split of 1,536 training, 192 validation, and 192 test images with a fixed random seed of 42 to ensure reproducibility. MobileNetV2 achieved the highest test accuracy of 96.35% and macro F1-score of 96.35%, outperforming EfficientNetB0 at 94.27% and NASNetMobile at 89.06%. In terms of computational efficiency, MobileNetV2 also demonstrated the most favorable deployment profile with a TensorFlow Lite model size of 2.75 MB and inference latency of 3.22 ms per image, indicating potential suitability for resource-constrained deployment scenarios. These results suggest that MobileNetV2 offers a competitive balance between classification accuracy and computational efficiency for rice leaf disease identification.
Robust Facial Classification of Down Syndrome using Lightweight CNNs Yunidar Wahab; Muhammad Dika Rafi Kasha; Melinda Melinda; Nurlida Basir; Siti Rusdiana
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1525

Abstract

Down Syndrome (DS) is a genetic disorder caused by trisomy 21 and is characterized by distinctive facial features that can support early screening. However, access to conventional diagnostic tools remains limited, particularly in resource-constrained regions. This study presents a comparative evaluation of two lightweight convolutional neural network (CNN) architectures, EfficientNet-B1 and MobileNetV3-Large, for facial image-based DS classification. A curated dataset of 3,030 facial images underwent quality control and image enhancement processes applied exclusively to the training data, resulting in 2,620 images. The dataset was split into training, validation, and test sets at a 70:20:10 ratio. Both models were fine-tuned using ImageNet-pretrained weights and evaluated based on accuracy, precision, recall, and F1-score. Performance robustness and statistical significance between models were assessed using five-fold cross-validation and one-way ANOVA. The experimental results demonstrate that both architectures achieved high classification performance; however, EfficientNet-B1 exhibited superior stability, more balanced class predictions, and lower fold-to-fold variability. Furthermore, Grad-CAM visualization confirmed that both models focused on clinically relevant facial regions, with EfficientNet-B1 showing more consistent and interpretable attention patterns. These findings suggest that EfficientNet-B1 is a robust and interpretable model for facial-based DS screening, offering significant potential for deployment in resource-limited healthcare settings.
Classification of H2O with HCl and H2O with NaOH Solution Images Using Otsu Segmentation and CNN Mauliza Putri; Melinda Melinda; Siti Rusdiana; Aufa Rafiki; Lailatul Qadri Zakaria
JURNAL INFOTEL Vol 18 No 2 (2026): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i2.1541

Abstract

The classification of the image of chemical solutions is crucial in laboratory automation and chemical industry applications; however, it remains challenging when solutions such as H2O with HCl and H2O with NaOH exhibit nearly identical visual characteristics under imaging conditions, particularly when their spectral fluctuation patterns are visually subtle. This study proposes an image classification framework that integrates Otsu-based segmentation in the HSV color space with convolutional neural network (CNN) models to classify High Height Fluctuation (HHF) images generated from a Multi-Scale chemical detection system (MSCS). The dataset consists of 102 HHF images, evenly distributed between the two solution classes. Transfer learning is applied using three CNN architectures, namely EfficientNetV2S, DenseNet201, and EfficientNetB0, and performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that DenseNet201 achieves the best overall performance, while EfficientNetV2S provides competitive results with computational efficiency and Efficient-NetB0 yields a lighter model with lower recall. These findings indicate that combining segmentation with modern CNN architectures can effectively improve classification robustness in chemically similar solutions. This study presents a practical framework that combines Otsu-based HSV segmentation with transfer-learning CNNs to classify chemically similar solutions, providing actionable insights for deep learning-based chemical sensing applications.
Optimasi Penempatan Distributed generation dan Kapasitor Bank Berbasis Particle Swarm Optimization untuk Meningkatkan Kinerja Sistem Distribusi Kota Sabang Muhajir Muhajir; Suriadi Suriadi; Melinda Melinda
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5343

Abstract

The Sabang electricity system is an isolated grid system that relies on diesel power plants with long distribution lines, which can cause high losses and voltage drops. This study aims to find the optimal placement of Distributed Generation (DG) and capacitors using Particle Swarm Optimization (PSO) Method to reduce losses and voltage deviation, with a voltage limit of 5% on a 20 kV system. Simulation results show that without DG and capacitors, losses reach 26 kW at peak load and 24.4 kW at normal load, with low voltages at some buses. After optimization, the combined use of DG and capacitors reduces losses by 50.82% at normal load and 51.15% at peak load. The voltage profile also improves by about 200 - 350 V at each bus, bringing it closer to the nominal value of 20 kV. Compared with Genetic Algorithm (GA), PSO provides better results, with 0.45 - 3.79% lower active power losses and 0 - 7.40% lower reactive power losses. This proves that PSO is effective in improving the efficiency, voltage quality, and reliability of isolated distribution systems.Keywords – Distributed generation (DG), Losses, Particle Swarm Optimization (PSO), Renewable Energy
Implementasi Media Pembelajaran Emosi Berbasis AR untuk Meningkatkan Literasi Emosi Anak di Panti Sosial Rumoh Seujahtera Aneuk Nanggroe M Ghailan Dhiaulhaq; Melinda Melinda; Aulia Rahman; Yunidar Yunidar; Siti Rusdiana; Ainil Syafitri; Fadilah Amanda Putri
Jurnal Pengabdian Rekayasa dan Wirausaha Vol. 3 No. 1 (2026): Mei
Publisher : Fakultas Teknik Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v3i1.1922

Abstract

Kemampuan mengenali dan meregulasi emosi merupakan bagian penting dalam perkembangan sosial dan emosional anak. Namun, proses pembelajaran emosi di lingkungan pengasuhan sosial masih didominasi metode konvensional yang kurang interaktif sehingga pemahaman anak terhadap materi emosi belum optimal. Kegiatan pengabdian ini bertujuan mengembangkan dan mengimplementasikan media pembelajaran emosi berbasis Augmented Reality (AR) bernama MEURASA untuk meningkatkan literasi emosi anak di UPTD Rumoh Seujahtera Aneuk Nanggroe (RSAN). Media dikembangkan dalam bentuk aplikasi interaktif berbasis AR yang menampilkan visualisasi emosi tiga dimensi melalui perangkat mobile. Metode pelaksanaan meliputi observasi kebutuhan mitra, perancangan dan pengembangan aplikasi, implementasi, serta evaluasi menggunakan pendekatan pre-test dan post-test dengan instrumen Difficulties in Emotion Regulation Scale (DERS-16). Kegiatan melibatkan 31 anak usia 7–12 tahun sebagai peserta. Hasil implementasi menunjukkan bahwa aplikasi Meurasa dapat digunakan dengan baik dalam proses pembelajaran dan mampu meningkatkan keterlibatan peserta selama kegiatan berlangsung. Seluruh peserta memperoleh nilai di atas 85 poin pada evaluasi akhir pemahaman materi emosi. Selain itu, hasil pengukuran DERS-16 menunjukkan penurunan rata-rata skor dari 52,3 pada pre-test menjadi 38,7 pada post-test, yang mengindikasikan peningkatan kemampuan regulasi emosi peserta. Dengan demikian, media pembelajaran berbasis AR yang dikembangkan dinilai efektif sebagai inovasi teknologi pembelajaran untuk mendukung peningkatan literasi emosi anak di lingkungan panti sosial.  
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