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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 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 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 Pubmedia Social Sciences and Humanities Akuntansi: Jurnal Riset Ilmu Akuntansi GEMBIRA (Pengabdian Kepada Masyarakat) Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Sriwijaya Electrical and Computer Engineering (Selco) Journal Jurnal Pengabdian Rekayasa dan Wirausaha Nawadeepa: Jurnal Pengabdian Masyarakat
Melinda Melinda
Jurusan Teknik Elektro Dan Komputer, Fakultas Teknik 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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Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA Cut Siti Azola Syiva; Melinda Melinda; Syahrial Syahrial; Imam Fathur Rahman; Souvik Das; M. Ary Heryanto
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

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

Abstract

Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.
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
Electrocardiogram Signal Analysis Based on Discrete Wavelet Transform with Machine Learning Method in Autistic Children 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 (Article in Progress)
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 manage emotions, interact socially, and respond to the environment. The main challenge in monitoring children's physiological condition is the limited availability of objective observation methods that rely heavily on health professionals. One potential objective approach is to analyze the ECG signal. However, ECG signals in children with ASD generally have high levels of noise due to body movements during recording, making manual analysis and conventional methods difficult. This study aims to develop a classification system for the physiological condition of children with ASD based on ECG signals, specifically to distinguish between quiet and active states. The dataset consists of 1000 from each of the two active classes and 1000 from the quiet class. ECG signals were processed using DWT for filtering, and then classified using three machine learning algorithms: SVM, RF, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that Random Forest provided the best performance, with an accuracy value of 93%. Meanwhile, SVM achieved an accuracy of 91.25%, while AdaBoost showed slightly lower performance at 90.00%. Based on these results, Random Forest was selected as the most optimal model and integrated into a web-based system using Streamlit. This study demonstrates that the combination of DWT and Random Forest is effective for classifying the physiological conditions of autistic children and has the potential to serve as an objective tool for monitoring them.
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
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.
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
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.
Co-Authors . Roslidar Aafiyah, Siti Afra Abdurohim Abdurohim, Abdurohim Abed Nego, Abed Abrina Anggraini, Sinar Perbawani Achmad Maqsudi, Achmad Achmad, Ilham Adawiyah, Muna Robiatul Afnan, Afnan Agnesia Candra Sulyani Agung Enriko, I Ketut Ahmad, R. Andriadi Ahmadiar, Ahmadiar Akbar, Alif Yafi Al Bahri Alam Mahadika, Alam Mahadika Albar, Nizam Alfatirta Mufti Alfatirta Mufti Alfian, Ridho Alfita Khairah Alifia, Rania Sofie Amalia Amalia Amaliatulwalidain, Amaliatulwalidain Ameilia Zuliyanti Siregar Anabel, Cendana Ananda, Mulya Anik Puryatni Anto Ariyanto Anzelina, Dhea Eprillia Aqif, Hurriyatul Ari Rahmat Putra Ibina Ariyani, Amra Arumi, Naila Azaria Asriati Asriati, Asriati Astuti, Meti Aulia Arafat Aulia Rahman Aurelia, Gabrella Azhar, Deden Azhari, Rizki AZMI, MUHAMMAD RAUDHI Azra, Ery Bashir, Nurlida Basir, Nurlida Basuki Toto Rahmanto Bil Haki, Arif Binti Basir, Nurlida Catur Andryani, Nur Afny Cloudya, Cindy Cut Siti Azola Syiva D Acula, Donata Diana Novita Diana, Fitri Dini, Siti Doke, Herlina Theodensia D. Duana, Maiza Dwi Rosalina Dwita Sakuntala E Elizar Elizar Elizar Elizar Elizar, Elizar Ellsa Fitria Sari Elsy Rahajeng, Elsy Elya, Chayara Alima Rameyza Ernita Dewi Meutia Fahmi Fahmi Fakhrus Syakir Farhan Fathur Rahman, Imam Fathurrahman Fathurrahman Fitri Arnia 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 Herlina Herlina Herwanto, Agus Hubbul Walidainy I Gusti Bagus Astawa I Ketut Agung Enriko Ichwana Ramli Ichwana Ramli Iis Juniati Lathiifah Imam Fathur Rahman Indarti, Ghinna Yulia Indera Sakti Nasution Indriani, Berlian Irawan Irawan Irvan kurniawan, Muhammad Iskandar Hasanuddin Iskandar Hasanuddin Islamy, Fajrul Joanita Jalianery Junidar, Junidar Karlisa Priandana Kencana, Novia Khairia, Syaidatul Khatami, Muhammad Kristiana kristiana Lailatul Qadri Zakaria Lailatul Qadri Zakaria Lerrick, Yudith F. Lisbeth Lesawengen, Lisbeth Lucky, Muhammad Luju, Elisabet Lukman Hidayat M Ary Heryanto M Fahrur Rozi Magfirah, Inayah Zaini Mahdi Syukri Mahfuzha, Raudhatul Mahidin Mahidin Malahayati, M. Margarethy Rohanie Mbado Maulana Imam Muttaqin Maulana, Muhammad Iqbal Maulisa, Oktiana Mauliza Putri Mayanti, Andi Mega Fatimah Rosana Meutia Nauly Miftahujjannah, Rizka Mirza Rahmat, Muhammad Mohd. Syaryadhi Morita Sari Muhajir Muhajir Muhamad Risal Tawil Muhammad Furqan Muhammad Hakkan Syukur Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irwanto Muhammad Ridwan Muharratul Mina Rizky Muhibbuddin Muhibbuddin Muhibuddin Muhibuddin Muhsin Muhsin Muliyadi Muliyadi Mulyadi Mulyadi Mulyadi, Yose Ega Mustikawati, Yunitari N Nasaruddin Nabella, Putri Rama Nabila, Nissa Hasna Nasaruddin Nasaruddin Nasaruddin Nasaruddin Nasaruddin Syafie Nasrul Arahman Nasrul Nasrul Nazilla, Izza Netti Herlina Siregar Nofrima, Sanny Novandri, Andri Nuraini, Endah Nurbadriani, Cut Nanda Nurfatikah, Aisyah Ariyani Nurhasanah, Lulu Nurhetty , Putri Alia Nurlida Basir Nurlida Basir Nusa Muktiadji OKTADINATA, ALEK Oktiana, Maulisa Peronika, Agustina Prabowo, Bangkit Yudo Pramesti, Nadya Wahyu 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 Rafiki, Aufa Rahmi Susanti Raihan, Siti Rajagukguk, Katarina Rani Rakhmad Syafutra Lubis Ramadan, Muhammad Fahreza Ramadhani, Hanum Aulia Ramdhana, Rizka Ramli, Amaliatulwalidain Rini Safitri Riska Sufina Rita Khatir Rizal Syahyadi Rizka Miftahujjannah Rizka Miftahujjannah Romal Ijuddin Rosmawati Rosmawati Roy Budiharjo RoziqiFath, Zain Fuadi Muhammad Rusmardiana, Ana Ruzdy, Nabilah Nameera saepudin, udin Sakarkar, Gopal 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 Sitti Suhada Solissa, Ferdinando Souvik Das Suhara, Ade Sulastri Sulastri Suriadi Suriadi Suriati, Israini Suwandi Suwandi Suyanda, Arya Syahputra, Daniel Syahrial Syahrial, Syahrial Syahyadi, Rizal Syakir, Fakhrus Syarifah Rauzatul Jannah Tandi, Asrin Tariliani, Cut Dara Taufik Iskandar Taufiq Abdul Gani Teuku Muhammad Mirza Keumala Tulus Tulus Tulus Tulus Ugi Nugraha Ulul Azmi Umrah, Andi Sitti Victoria Ari Palma Akadiati 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 Yulia, Prima Dwi Yuliati - Yunidar Yunidar Yunidar Yunidar Yunidar Yusup, Syafina Ainur Yuwaldi Away Yuwaldi Away Zahra, Viqqy Nur Zahran Jemi , Faris Zainal, Zulfan Zetira, Zetira Rizqia Erlin Zharifah Muthiah Zulfikar Taqiuddin Zulhelmi . Zulhelmi, Zulhelmi Zulkifli Nasution