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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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Enhancing Face Detection Performance in Low-Light Conditions Using NIR Thermal Imaging and Image Morphology Maulisa Oktiana; Cut Salsabilla Azra; Rusdha Muharar; Fajrul Islamy; Rizka Ramadhana; Melinda Melinda; Niza Aulia; Muharratul Mina Rizky; Maya Fitria
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 2 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.2.108786

Abstract

Face detection plays a vital role in biometric, security, and surveillance systems. Conventional approaches based on the visible light (VIS) spectrum often suffer performance degradation under poor lighting conditions, limiting their reliability. To address this issue, this study employs thermal imagery in the Near-Infrared (NIR) spectrum, which is less affected by ambient light, combined with image morphology operations to enhance segmentation accuracy. Experiments were conducted using the LDHF-DB dataset (300 images at distances of 1 m, 60 m, and 100 m) and a subset of the Tuft dataset (60 images). Face detection was performed using the HOG + SVM method, followed by Otsu thresholding and morphological operations. Performance was evaluated using Peak Signal-to-Noise Ratio (PSNR). Results show that applying morphological operations significantly improves PSNR values, with an average increase of more than 35%. The best performance was achieved on the 1 m subset, while longer distances presented greater challenges. These findings highlight the potential of integrating NIR thermal imagery and morphological processing to improve the robustness and reliability of face detection systems in low-light environments.
Hybrid Time-Frequency ECG Arrhythmia Classification with Feature-Model Compatibility and Ensemble Decision Fusion J Prayoga; Melinda Melinda; Teuku Yuliar Arif; Herlina Dimiati
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1730

Abstract

Cardiac arrhythmia is a critical cardiovascular disorder associated with high global mortality, while electrocardiogram (ECG)-based diagnosis remains time-consuming and susceptible to inter-observer variability. Although recent artificial intelligence approaches have improved automated ECG analysis, many existing studies rely on single time-frequency representations and uniform classification strategies, limiting their ability to capture complementary ECG characteristics. This study proposed a hybrid time-frequency ECG arrhythmia classification framework incorporating feature–model compatibility and ensemble decision fusion to improve classification robustness and reliability. ECG signals from the MIT-BIH Arrhythmia Database were preprocessed using a fourth-order Butterworth bandpass filter and segmented using overlapping windows. To prevent data leakage, patient-wise cross-validation was employed, ensuring that ECG segments originating from the same patient were assigned exclusively to either training or testing folds. Three complementary time-frequency representations, namely Mel-Spectrogram, Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT), were paired with their most compatible classifiers: Convolutional Neural Network (CNN); Random Forest (RF); and Support Vector Machine (SVM); respectively. Decision fusion was performed using stacking, hard voting, and soft voting strategies. Experimental results showed that the DWT-SVM model with stacking achieved the best overall performance, attaining 94.82% accuracy and a 94.59% F1-score, while STFT-RF with hard voting achieved comparable performance with 94.75% accuracy and a 94.50% F1-score. In contrast, Mel-Spectrogram-CNN produced substantially lower performance with 81.45% accuracy, indicating limited suitability of Mel-scale representations for ECG morphology analysis. Statistical analysis confirmed significant performance differences among models (p < 0.001). The findings demonstrate that integrating hybrid time-frequency representations with feature–model compatibility and model-dependent decision fusion provides a robust framework for automated ECG arrhythmia classification with strong potential for clinical decision support and real-time cardiac monitoring applications
Temporal Deep Learning with Multiscale Principal Component Features for Autism Classification from Electroencephalographic Signals Muliyadi Muliyadi; Melinda; Yuwaldi Away; Syahrul Gazali; Aufa Rafiki; W.K Wong
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1735

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose early identification remains challenging because clinical assessment still relies heavily on behavioral observation and expert judgment. Electroencephalography (EEG) offers a noninvasive approach for capturing neural dynamics. Still, EEG-based ASD classification remains difficult because of signal nonstationarity, limited sample sizes, and the risk of subject-identity leakage. This study proposes a comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA). Resting-state eyes-open EEG signals were acquired from only 10 participants, consisting of 5 individuals with ASD and 5 typically developing controls, using a 16-channel acquisition system. The signals were filtered using a fourth-order Butterworth band-pass filter, transformed into PCA or MS-PCA representations, segmented into 4 s windows with 50% overlap, and evaluated using subject-wise 5-fold cross-validation to reduce subject-identity leakage. The results showed that MS-PCA produced higher descriptive performance than PCA in both temporal architectures, with the strongest descriptive result obtained by the MS-PCA + TCN-SA scheme, which achieved a mean accuracy of 97.96 ± 2.37% and balanced precision, recall, F1-score, and specificity. However, the inferential comparison between PCA and MS-PCA did not reach statistical significance at the 0.05 level, and the cohort size was limited to 10 participants. Therefore, these findings should be interpreted as preliminary descriptive evidence within the present cohort rather than evidence of diagnostic readiness or robust clinical applicability. Larger, independent, and demographically diverse EEG datasets with richer clinical characterization are required to confirm the observed trend and evaluate the generalizability of the proposed framework.
Penerapan Sistem Identifikasi Ekspresi Wajah Anak Penyandang Autisme Berbasiskan Citra Termal pada Sekolah Berkebutuhan Khusus di Banda Aceh Melinda Melinda; Yunidar Yunidar; Muhammad Irhamsyah; Muharratul Mina Rizky; Hendrik Leo; Fahmi Fahmi
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

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

Abstract

This community service activity aims to apply technology to detect facial expressions of children with autism through thermal images. The activity was carried out at My Hope Special Need Center, Banda Aceh, an educational center for orphans and children with special needs. By utilizing a combination of psychological and technological approaches, data collection is carried out in the form of thermal images of the faces of children with and without autism. The data obtained was analyzed using the Convolutional Neural Network (CNN) approach to develop an automatic facial expression detection method. The results of this activity show the potential use of facial recognition technology in supporting education and therapy for children with special needs.
Predicting Stress Levels in Special Needs School Teachers Using DASS-21 and Gradient Boosting Machine Alfita Khairah; melinda melinda; Iskandar Hasanuddin; Rizka Miftahujjannah; Rosminazuin Ab Rahim; Siti Rusdiana
Jurnal Teknokes Vol. 19 No. 3 (2026): September
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i3.164

Abstract

The stress experienced by teachers in Special Needs Schools presents considerable threats to both mental health and job performance, making the need for precise and scalable detection techniques essential. This research presents a multimodal framework for stress classification that combines physiological signals from a wearable IoT device with psychological assessment using the DASS-21. Data were gathered from 48 educators, including heart rate (BPM) and body temperature (°C) as physiological indicators and DASS-21 stress subscale scores as psychological indicators. The target variable was categorized as a binary class (stressed vs. non-stressed) utilizing standardized DASS-21 cut-off scores, with physiological thresholds to improve label dependability. The class distribution was examined to reduce class imbalance bias. The dataset was subjected to preprocessing, which involved normalization and feature selection, and was then divided into a 60:40 train-test split. The evaluation of model generalizability was conducted through 5-fold cross-validation. GBM model was utilized to identify non-linear relationships and interactions between features. Performance assessment involved accuracy, precision, recall, F1-score, specificity, and AUC. The suggested model reached accuracy of 94.12%, 87.5%, and 85.71% for the elementary, junior high, and high school groups, respectively, demonstrating consistently high precision and AUC metrics, reflecting strong discriminative ability. These findings indicate that a clearly defined labeling approach, harmonious feature integration, and strict validation procedure facilitate dependable and consistent stress detection. The suggested framework offers a scalable solution for real-time monitoring of mental health in educational environments. Furthermore, the results explicitly confirm that integrating IoT-based wearable systems with the DASS-21 and the GBM algorithm provides a reliable and scalable approach for stress detection among teachers. This study highlights the practical significance of implementing real-time monitoring systems in educational environments, enabling early identification and intervention for stress management. Such a framework not only improves teachers’ mental well-being but also supports institutional decision-making in developing preventive strategies and sustainable mental health programs.
Automated Z-Score Based Nutritional Status Classification for Children Under Two Using Smart Sensor System Yunidar Yunidar; Melinda Melinda; Rina Ridara; Nurlida Basir
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.111

Abstract

The classification of nutritional status in children under two years old is crucial for monitoring growth and early detection of nutritional problems. However, in many healthcare facilities, this classification is still performed manually, requiring relatively long processing times and being prone to human error in both measurement and data recording. The problem addressed in this study is the inefficiency and potential inaccuracy of manual nutritional status classification in toddlers. This research aims to develop an automatic and digital device capable of measuring body length and weight and classifying nutritional status in children under two years old efficiently, accurately, and in real time. The device utilizes electronic sensors integrated with a microcontroller to streamline the process and reduce measurement error. The main contribution of this study is the design and realization of a portable automation device that integrates an HC-SR04 ultrasonic sensor for measuring body length and a 50 kg full-bridge load cell sensor for measuring body weight, both controlled by an ATmega328P microcontroller. The device processes the data measurement digitally, displays the results on a 20 × 4 LCD, and provides a printed copy via a thermal printer, enhancing the data recording efficiency. The method involves the design of hardware circuits, sensor calibration, software programming using the C language in the Arduino IDE, and performance testing of the device by comparing its results to standard measuring instruments. The device’s performance is evaluated based on measurement error percentage and precision level. The results demonstrate that the device achieved an error percentage of 1.26% for body length measurement and 0.98% for body weight measurement. The overall system error is recorded at 0.5%, with a precision level ranging from ±0.08 to ±0.4.
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation Imam Fathur Rahman; Melinda Melinda; Yunidar Yunidar; Nurlida Basir; Aufa Rafiki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.313

Abstract

Accurate and reliable classification of autism spectrum disorder (ASD) from electroencephalography (EEG) signals remains challenging due to the inherently nonstationary, nonlinear, and multichannel nature of EEG data. These properties complicate the extraction of discriminative features that are both stable and computationally efficient. To address this challenge, this study proposes a compact deep-learning pipeline that integrates the Multivariate Empirical Wavelet Transform (MEWT) with EEGNet for ASD–EEG classification. MEWT decomposes multichannel EEG signals into spectrally aligned subbands while preserving inter-channel relationships. The resulting MEWT-based features are then processed by EEGNet, a lightweight convolutional neural network specifically designed for EEG-based learning tasks. Performance was evaluated using 5-fold cross-validation. The proposed MEWT with the the EEGNet model achieved a mean test accuracy of 98.35%, with consistently high precision (98.23%), recall (98.45%), F1-score (98.34%), and specificity (98.24%) across all folds. Confusion-matrix results indicated very few and well-balanced false positives and false negatives, supporting stable discrimination between ASD and control EEG segments. A one-sample one-tailed t-test against a 50% chance level confirmed that all evaluated metrics were significantly above chance (p < 0.0001). When benchmarked against previously reported results on the same dataset, the proposed approach slightly improved upon EMD with EEGNet (97.99%) and clearly outperformed EWT with EEGNet (95.08%), suggesting that MEWT-derived multichannel features are well matched to compact convolutional architectures for ASD–EEG analysis. Despite these strong results, the study is limited by a small, single-site cohort and the use of a single deep-learning model. Future work will focus on standardized retraining across multiple feature families and validation on larger and more diverse populations to further assess robustness and generalizability
Improved Autism Spectrum Disorder Detection Using Euclidean-Distance Facial Landmark Features to Enhance Classification Accuracy and Stability with Cross-Validation Syifa Anzella; Yudha Nurdin; Melinda; Junidar; Rosminazuin Ab Rahim
Jurnal Teknokes Vol. 19 No. 3 (2026): September
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i3.163

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication skills, social interaction, and behavioral patterns. Early detection is essential for timely intervention; however, conventional diagnostic methods remain time-consuming and subjective, as they rely heavily on clinical observations and expert judgment. This limitation highlights the need for an automated and objective approach to support early ASD screening. This study aims to analyze the performance, stability, and generalization of ASD classification using geometric features extracted from distances between facial landmarks. By representing facial morphology in terms of quantitative spatial relationships, this approach provides a more interpretable alternative to raw image-based methods. This study contributes by proposing a geometric feature representation based on facial landmark distances, providing a comparative analysis between linear and nonlinear classifiers, and ensuring robust evaluation through cross-validation. The dataset consists of 2,032 facial images, evenly distributed between children with ASD and those with typical development. A total of 68 facial landmark points were detected and used to compute pairwise Euclidean distances as classification features. Two classification algorithms, Logistic Regression and Extra Trees Classifier, were evaluated using 5-fold cross-validation to ensure reliable and unbiased performance estimation. The results show that Logistic Regression achieved an average accuracy of 89.91%, precision of 91.04%, recall of 88.56%, and F1-score of 89.76%. Meanwhile, the Extra Trees Classifier outperformed the linear model, achieving an average accuracy of 91.88%, precision of 92.69%, recall of 90.89%, and F1-score of 91.77%. Overall, both models demonstrated stable and consistent performance across validation folds, with the Extra Trees Classifier showing superior ability to capture nonlinear patterns in the data. These findings indicate that geometric feature extraction based on facial landmark distances is effective for ASD detection and has strong potential to be developed as an objective, interpretable, and efficient early screening tool using children’s facial images.
Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures Rachmawati Rachmawati; Arsy Febrina Dewi; Melinda Melinda; Razita Nadhira; Aufa Rafiki; Nurlida Basir
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

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

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy (  for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.
Natural and Horizontal Visibility Graphs of EEG Power Spectra for Autism Spectrum Disorder Classification Melinda Melinda; Muhammad Saifullah Nur; Yuwaldi Away; Siti Rusdiana; Souvik Das; Zulfan Zulfan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 4 (2026): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i4.373

Abstract

Autism spectrum disorder (ASD) is primarily identified through behavioral and developmental assessment, which may require specialist expertise and considerable time. Electroencephalography (EEG) offers non-invasive neurophysiological information, but reliable ASD classification remains challenging because EEG signals are nonstationary and highly variable across individuals. This study evaluates whether visibility-graph topology derived from EEG power spectral density (PSD) curves provides complementary information beyond conventional spectral descriptors. Resting-state EEG from the Banda Aceh dataset (7 ASD and 7 typically developing participants, 250 Hz) and the KAU dataset (8 ASD and 8 control participants, 256 Hz) were processed independently. Preprocessing included quality inspection, 0.5–45-Hz zero-phase filtering, dataset-specific 50/60-Hz notch filtering, common average referencing, and non-overlapping 30-s segmentation. Welch PSD was estimated using 2-s Hann windows with 50% overlap across five frequency bands. Direct PSD, natural visibility graph features, and horizontal visibility graph features were compared using RBF-SVM and a six-hidden-layer MLP. Evaluation used subject-wise balanced cross-validation, with performance calculated at the epoch level from participant-disjoint outer-test folds. VG-NVG-PSD with SVM achieved the best observed accuracy of 97.14 ± 3.29% on Banda Aceh, representing a 0.97-percentage-point numerical gain over direct PSD-SVM. VG-HVG-PSD with SVM achieved 96.83 ± 2.34% on KAU, representing a 0.35-percentage-point gain. SVM produced the highest observed configuration on both datasets, whereas graph-spectral transformation did not consistently improve MLP performance. Bonferroni-corrected pairwise comparisons did not establish statistical superiority among individual methods. These findings indicate that PSD-derived visibility graphs provide an interpretable complementary representation for EEG-based ASD classification, although the preferred visibility criterion was dataset-dependent. Larger cohorts and external validation are required before clinical generalization
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