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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 POLITICA: Jurnal Hukum Tata Negara dan Politik Islam 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 JIKUM: Jurnal Ilmu Komputer Jurnal Pengabdian Rekayasa dan Wirausaha Jurnal Teknokes Nawadeepa: Jurnal Pengabdian Masyarakat
Melinda Melinda
Jurusan Teknik Elektro Dan Komputer, Fakultas Teknik Universitas Syiah Kuala
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Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar, Yunidar; Harahap, Inda Mariana; Melinda, Melinda; Rosmawinda, Rosmawinda; Basir, Nurlida; Rafiki, Aufa; Rahman, Imam Fathur
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

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

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Robust Facial Classification of Down Syndrome using Lightweight CNNs Wahab, Yunidar; Rafi Kasha, Muhammad Dika; Melinda, Melinda; Basir, Nurlida; Rusdiana, Siti
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

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

Abstract

Down Syndrome (DS) is a genetic disorder caused by trisomy 21 and is characterized by distinctive facial features that can support early screening. However, access to conventional diagnostic tools remains limited, particularly in resource-constrained regions. This study presents a comparative evaluation of two lightweight convolutional neural network (CNN) architectures, EfficientNet-B1 and MobileNetV3-Large, for facial image-based DS classification. A curated dataset of 3,030 facial images underwent quality control and image enhancement processes applied exclusively to the training data, resulting in 2,620 images. The dataset was split into training, validation, and test sets at a 70:20:10 ratio. Both models were fine-tuned using ImageNet-pretrained weights and evaluated based on accuracy, precision, recall, and F1-score. Performance robustness and statistical significance between models were assessed using five-fold cross-validation and one-way ANOVA. The experimental results demonstrate that both architectures achieved high classification performance; however, EfficientNet-B1 exhibited superior stability, more balanced class predictions, and lower fold-to-fold variability. Furthermore, Grad-CAM visualization confirmed that both models focused on clinically relevant facial regions, with EfficientNet-B1 showing more consistent and interpretable attention patterns. These findings suggest that EfficientNet-B1 is a robust and interpretable model for facial-based DS screening, offering significant potential for deployment in resource-limited healthcare settings.
Design and implementation of a state feedback controller for enhanced speed stability of permanent magnet DC motors under load variations Syukri, Mahdi; Lubis, Rakhmad Syafutra; Melinda, Melinda; Syukur, Muhammad Hakkan; Hasanuddin, Iskandar; Irwanto, Muhammad
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.
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.
Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas yunidar; Suyanda, Arya; Melinda, Melinda; Zakaria, Lailatul Qadri; Rusdiana, Siti
Jurnal Teknokes Vol. 19 No. 2 (2026): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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

Abstract

Early detection of Autism Spectrum Disorder (ASD) is a crucial challenge in child development interventions because conventional screening methods are often subjective and prone to assessor bias. This study proposes an objective solution in the form of a deep learning approach for automatic ASD classification using facial landmark representations that focus exclusively on the eye and forehead areas. The selection of these areas is based on the eye avoidance hypothesis, which states that these regions contain very rich diagnostic information and behavioral biomarkers related to the ASD phenotype. The pre-processing stage involves isolating the eye and forehead areas using Dlib 68-landmark detection to eliminate background visual noise, followed by detailed topological visualization using MediaPipe Face Mesh with 478 landmark points as the model input. The Convolutional Neural Network (CNN) architecture used is the VGG19 model modified with transfer learning techniques and the addition of Dropout layers to improve efficiency and prevent overfitting. The model was trained on a primary dataset of 1,238 images collected under controlled conditions from children in Banda Aceh. The test results showed very promising performance with an overall accuracy of 94.35%. Specifically, the model achieved a recall (sensitivity) of 95.24%, a precision of 93.75%, and an AUC score of 0.9831. This high sensitivity is crucial in a medical context to minimize the risk of misdetection of positive cases. These results demonstrate that landmark visualization in the eye and forehead areas with the VGG19 model is a highly effective, accurate, and practical method for serving as an economical early screening tool for ASD.
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
Co-Authors . Roslidar Aafiyah, Siti Afra Abdurohim Abdurohim, Abdurohim Abed Nego, Abed Abrina Anggraini, Sinar Perbawani Achmad Maqsudi, Achmad Achmad, Ilham Adawiyah, Muna Robiatul Afdhal Afdhal 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 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 Arumi, Naila Azaria Asriati Asriati, Asriati Astuti, Meti Aulia Arafat Aulia Rahman Aurelia, Gabrella Awaluddin Awaluddin 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 Elviandri, Elviandri 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 Harahap, Subur Harjoedi Adji Tjahjono, Harjoedi Adji Hasan, Hafidh Hasan, Vania Pratama Hasanuddin, Iskandar Heltha, Fahri Herlina Dimiati, Herlina Herlina Herlina Herwanto, Agus Hubbul Walidainy I Gusti Bagus Astawa I Ketut Agung Enriko Ichwana Ramli Iis Juniati Lathiifah Imam Fathur Rahman Inda Mariana Harahap Indarti, Ghinna Yulia Indera Sakti Nasution Indera Sakti Nasution Indriani, Berlian Irawan Irawan Irvan kurniawan, Muhammad Irwanto, Muhammad Iskandar Hasanuddin Iskandar Hasanuddin Islamy, Fajrul Joanita Jalianery Junidar, Junidar Karlisa Priandana Kencana, Novia Khairah, Alfita Khairah, Divaul Khairia, Syaidatul Kharina, Kharina Khatami, Muhammad Kristiana kristiana Lailatul Qadri Zakaria Leo, Hendrik Lerrick, Yudith F. Lisbeth Lesawengen, Lisbeth Lubis, Rakhmad Syafutra Lucky, Muhammad Luju, Elisabet Lukman Hidayat M Ary Heryanto M Fahrur Rozi Magfirah, Inayah Zaini Maharani, Citra Ayu Deswina Mahdi Syukri Mahfuzha, Raudhatul Mahidin Mahidin Mahidin Mahidin Malahayati, M. Margarethy Rohanie Mbado Maulana Imam Muttaqin Maulana, Muhammad Iqbal Maulisa, Oktiana Mayanti, Andi Mega Fatimah Rosana Meutia Nauly Miftahujjannah, Rizka Mirza Rahmat, Muhammad Mohd. Syaryadhi Morita Sari Muhajir Muhamad Risal Tawil Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Ridwan Muharratul Mina Rizky Muhibbuddin Muhibbuddin Muhibuddin Muhibuddin Muliyadi Muliyadi Mulyadi Mulyadi Mulyadi, Yose Ega Muna, Isyatul 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 Odelia, Marsha 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 Putri Mauliza, Putri Qadri Zakaria, Lailatul Rafi Kasha, Muhammad Dika Rafiki, Aufa Rahman, Imam Fathur Rahmi Susanti Raihan, Siti Rajagukguk, Katarina Rani Ramadan, Muhammad Fahreza Ramadhani, Dina Ramadhani, Hanum Aulia Ramdhana, Rizka Ramli, Amaliatulwalidain Ramli, Ichwana Ridara, Rina Rini Safitri Riska Sufina Rita Khatir Rizal Syahyadi Rizka Miftahujjannah Romal Ijuddin Rosmawati Rosmawati Rosmawinda, Rosmawinda 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 Siregar, Netti Herlina Siska, Emi Yulia Siti Rofiah, Siti 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 Syukur, Muhammad Hakkan 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 Wahab, Yunidar 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 Zulkifli Nasution