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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
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

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Students' interest in school Al-Kautsar Al Akbar Medan supported food security in Northern Sumatera, Indonesia Ameilia Zuliyanti Siregar; Zulkifli Nasution; Meutia Nauly; Tulus Tulus; Netti Herlina Siregar; Fitri Indira Nauli; Irli Masitah Marpaung; Ichwana Ramli; Mahidin Mahidin; Nasrul Nasrul; Indera Sakti Nasution; Melinda; Muhibuddin
Indonesian Journal of Community Services Vol. 5 No. 1 (2026): May
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijcs.v5i1.2392

Abstract

Agriculture is a vital sector for the economy and food security, especially for Islamic boarding schools (Pesantren) that have limited resources, such as access to water and electricity. Many Islamic boarding schools in remote areas face challenges in achieving economic independence, while available land is not optimally utilized. Therefore, technology-based innovation is needed to optimize existing resources and increase agricultural productivity. The Indonesian Collaborative Community Service (PMKI) USU 2025 aims to select 32 students and teachers during July–October 2025 who demonstrate interest, potential, and a sense of belonging in gardening activities to introduce and implement a smart farming-based agroecosystem in Al Kautsar Al Akbar Islamic Boarding School. Agripreneur activities train participants in suitable cultivation techniques, organic fertilizer production, seed selection, and marketing of organic fertilizer products to the community. Smart farming systems enable more precise and strategic decision-making through the use of sensors and Internet of Things (IoT)-based collected data.
Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar Yunidar; Melinda Melinda; Mauliza Putri; Muhammad Irhamsyah; Nurlida Basir; Alfita Khairah
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1253

Abstract

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda Melinda; Yunidar Yunidar; Rizka Miftahujjannah; Siti Rusdiana; Amalia Amalia; Lailatul Qadri Zakaria
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.
Beta-Band Electroencephalography Classification for Autism Spectrum Disorder Using Wavelet Features and Least-Squares Support Vector Machine Muhammad Irhamsyah; melinda melinda; Sri Rahayu Ade; Saifullah Nur Muhammad; Yunidar Yunidar; Nurlida Basir
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.v19i2.148

Abstract

Autism spectrum disorder requires accessible and objective neurophysiological biomarkers to complement behavioral assessment, particularly for early screening in resource-limited settings. This study explores a computationally efficient framework for distinguishing children with autism spectrum disorder from neurotypical controls using beta-band electroencephalography activity (12–30 Hz), which has been associated with atypical sensorimotor and cognitive processing in autism. Beta-band oscillations are theoretically relevant for their roles in attention, cognitive control, and inhibitory processes, domains frequently disrupted in autism spectrum disorder. Data were obtained from the public King Abdulaziz University dataset comprising 16 male participants (8 with autism, 8 controls; aged 6–14 years). Following independent component analysis-based artifact removal and bandpass filtering, recordings were segmented into 2-s epochs with 50% overlap. Discrete wavelet transform (Daubechies-4, four levels) was applied to extract statistical features (mean, standard deviation, skewness, kurtosis) from wavelet coefficients across 16 EEG channels, yielding a 320-dimensional feature vector per epoch. Classification was performed using least-squares support vector machines with a polynomial kernel (degree d=3), with hyperparameters optimized via 5-fold cross-validation on the training set, and evaluated via a stratified 70/30 train–test split at the segment level. The polynomial-kernel model achieved 98.49% segment-level accuracy, outperforming the linear kernel (95.07%) and a relative beta-power baseline. However, these results should be interpreted with caution due to the small sample size (n=16), a male-only cohort, and segment-level evaluation, which may inflate performance through intra-subject data leakage. The lightweight computational design supports potential implementation on portable devices. This proof-of-concept demonstrates the feasibility of wavelet-based beta-band analysis for autism classification, but rigorous validation using larger, balanced cohorts with subject-wise cross-validation is essential before clinical translation can be considered.
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda Melinda; Buleun Waladah; Yunidar Yunidar; Raudhatul Mahfuzha; Syahrul Gazali; Siti Rusdiana; Nurlida Basir
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.  
Data Splitting Strategies for Down Syndrome Facial Classification: A Comparative Study Using EfficientNet-B0 and MobileNetV2 Dzaki Dhiya Ul-Haq; Yunidar Yunidar; Melinda Melinda; Nurlida Basir; Rosmawinda Rosmawinda
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.338

Abstract

Early identification of Down Syndrome (DS) is essential for timely intervention; however, conventional diagnostic approaches often require specialized clinical expertise and significant resources. Recent advances in deep learning-based facial image analysis offer a promising alternative, yet the impact of data partitioning strategies on model performance and stability remains insufficiently explored. This study investigates the effects of different data-splitting strategies on DS facial image classification using EfficientNet-B0. A total of 3,030 facial images were collected from Roboflow and curated through preprocessing techniques, including Gaussian noise reduction, image sharpening, and contrast enhancement. Two data partitioning configurations, 70:20:10 and 80:10:10, were evaluated using five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score, while statistical significance was examined using the Friedman test. The results show that the 70:20:10 configuration achieved an average accuracy of 87.88% ± 3.03%, while the 80:10:10 configuration achieved a slightly higher accuracy of 89.09% ± 2.53%. The Friedman test indicates statistically significant differences (p < 0.05). However, the improvement is relatively marginal, with a small-to-moderate effect size (Cohen’s d = 0.43) and no significant difference in variance (p > 0.05), indicating limited practical significance. A trade-off between accuracy and evaluation stability was observed. While the 80:10:10 configuration benefits from a larger training set, the 70:20:10 configuration provides more stable and balanced performance, particularly in minimizing false negatives. These findings highlight that higher accuracy does not necessarily imply more reliable or clinically meaningful performance, emphasizing the importance of appropriate data partitioning in medical image classification.
A Model to Investigate Performance of Orthogonal Frequency Code Division Multiplexing Nasaruddin Nasaruddin; Melinda Melinda; Ellsa Fitria Sari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.840

Abstract

Orthogonal Frequency Code Division Multiplexing (OFCDM) is an attractive multiple access scheme for high data rate application in fourth-generation (4G) wireless communication system. Several previous researches were mainly investigated the performance of OFCDM based on variable spreading factor and subcarrier allocation. However, there are also several system parameters may affected the performance of OFCDM. For that purpose, this paper developes a model to investigate the impact of several parameters on the performance system of OFCDM over Rayleigh Fading channel as a realistic channel in wireless communication system.The proposed model is then created in the form of computer simulation using MATLAB programming in order to show the impact of several parameters for OFCDM’s performance including number of carriers, size of symbol, symbol rate, bit rate, size of guard interval and spreading factor. The simulation results show that the higher number of carriers, larger size of symbol, higher symbol rate, higher bit rate and larger spreading factor are giving the better system’s performance in terms of Bit Error Rate (BER). However, the larger guard interval is giving the worst system’s performance.So all the parameters should be considered in the implementation of OFCDM for the 4G wireless communication system.
Design and implementation of a state feedback controller for enhanced speed stability of permanent magnet DC motors under load variations Mahdi Syukri; Rakhmad Syafutra Lubis; Melinda Melinda; Muhammad Hakkan Syukur; Iskandar Hasanuddin; Muhammad Irwanto
Jurnal Polimesin Vol 24, No 2 (2026): April
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i2.8379

Abstract

This study presents the design and simulation of a State Feedback Controller (SFC) for speed regulation of a Permanent-Magnet DC (PMDC) motor using a state-space modeling approach. The objective is to achieve stable and accurate speed control under dynamic load disturbances that typically degrade the performance of conventional open-loop systems. The Direct Current (DC) motor is modeled in state-space form, with armature current and angular speed selected as the main system states. Controller gains are designed using the pole placement method to ensure fast response and improved stability. The proposed SFC is evaluated through MATLAB®/Simulink® simulations by examining motor speed, armature current, and input voltage responses under step-load variations. Simulation results show that the SFC maintains the motor speed at the reference value of 3,430 rpm even during sudden load increases, whereas the uncontrolled motor experiences significant speed drops and oscillations. Performance analysis confirms notable improvements in transient response. The rise time is reduced from 1.1864 s to 0.4220 s, and the settling time decreases from 2.1132 s to 0.7517 s, indicating faster and more stable system behavior. In addition, smoother current transitions and more efficient voltage regulation are achieved compared to the open-loop configuration. Overall, the results demonstrate that state-space control using pole placement provides a robust and responsive alternative to conventional PID controllers for DC motor speed control under load disturbances. Future work will focus on experimental validation and the exploration of advanced control strategies such as Linear Quadratic Regulation and adaptive control.
Analisis Persepsi dan Nilai Ekonomi Implementasi Eco Enzyme dan Smart Farming di Pondok Pesantren Eumpe Awee Ichwana Ramli; Ameilia Zuliyanti Siregar; Indera Sakti Nasution; Mahidin Mahidin; Muhibbuddin Muhibbuddin; Nasrul Arahman; sulastri sulastri; melinda melinda; Zulkifli Nasution; meutia Nauly; Netti Herlina Siregar; Tulus Tulus
Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 25, No 2 (2025): Suluah Bendang: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sb.06420

Abstract

Pengabdian ini bertujuan untuk menganalisis persepsi, minat, dan potensi ekonomi dari penerapan teknologi Eco Enzyme dan Smart Farming di Pondok Pesantren Eumpe Awee. Metode yang digunakan adalah pendekatan Participatory Rural Appraisal (PRA) dengan melibatkan 26 santri sebagai responden utama dan pelaku praktik pertanian. Hasil menunjukkan bahwa sebagian besar santri memiliki tingkat pengetahuan dan minat yang tinggi terhadap teknologi ini, didukung oleh tersedianya sarana seperti sensor kelembapan tanah, sistem irigasi otomatis, dan perlengkapan tanam lainnya yang diperoleh melalui program PMKI Universitas Syiah Kuala. Budidaya kangkung dan bayam yang dilakukan menghasilkan total penerimaan Rp 330.000 dengan biaya produksi Rp 237.000, menghasilkan R/C ratio sebesar 1,39 yang menunjukkan kelayakan finansial. Selain itu, teknologi Eco Enzyme yang dibuat dari limbah organik dan sistem irigasi otomatis berkontribusi terhadap efisiensi sumber daya dan pengurangan penggunaan bahan kimia, memperkuat aspek keberlanjutan lingkungan. Temuan ini mengindikasikan bahwa dengan pendekatan adaptif, pesantren dapat menjadi pusat edukasi sekaligus praktik pertanian berkelanjutan yang memberdayakan santri secara nyata dan aplikatif
Comparative Analysis of Multispectral Image Classification Based on EfficientNetB0, ResNet152, DenseNet161, DenseNet121, and HSV Segmentation Melinda; Yudha Nurdin; Alfatirta Mufti; Syifa Anzella; Siti Rusdiana; Donata D Acula
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6873

Abstract

This study established a classification system based on Convolutional Neural Networks (CNNs) to detect High-High Fluctuation (HHF) patterns in multispectral data derived from pure water (H2O) and a water-sodium hydroxide (NaOH) solution. This study combines HSV color-space-based segmentation to identify areas with the highest signal amplitude, thereby enhancing the feature extraction of the CNN model. Data augmentation techniques, including random flipping, rotation, and color jitter, along with training parameters such as a learning rate of 0.0001 and a batch size of 32, have been shown to effectively improve model generalization and reduce overfitting. Four different CNN architectures were evaluated: ResNet-152, DenseNet-161, DenseNet-121, and EfficientNet-B0. As a result, ResNet152 achieved the highest accuracy of 97.6%, attributed to its network depth and residual connections that effectively address the vanishing gradient problem. DenseNet161 and DenseNet121 also demonstrated competitive performance, achieving accuracies of 96.7% and 96.2%, respectively, which is supported by their dense connectivity that optimizes feature reuse. Conversely, EfficientNetB0, despite showing lower accuracy (90%), provides significant computational efficiency, making it suitable for real-time applications. These results underscore the importance of selecting a CNN architecture that balances accuracy and efficiency for multispectral data classification.
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