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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Ranggagading (JIR) 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 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 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
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Autism EEG Signal Pre-Processing: Performance Evaluation of MS-ICA and Butterworth Filter Mirza Rahmat, Muhammad; Nurdin, Yudha; Melinda, Melinda; Away, Yuwaldi; Irhamsyah, Muhammad; Wong, W. K
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.107

Abstract

Autism Spectrum Disorder (ASD) is a neurological condition characterized by challenges in communication and social interaction, accompanied by the development of repetitive behavioral patterns. Electroencephalography (EEG) is primarily used to assess brain function in children with Autism Spectrum Disorder (ASD), mainly due to its non-invasive nature and superior temporal resolution compared to other neuroimaging methods. However, EEG signals are often contaminated by biological artifacts, such as eye movements and muscle contractions, which can significantly distort analysis outcomes. Pre-processing is therefore required to increase the accuracy of the EEG signal before additional analysis. The goal of this study was to compare and evaluate the performance of two pre-processing techniques, the Butterworth Band-Pass Filter and Multiscale Independent Component Analysis (MS-ICA), using four different performance metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). The Butterworth method has an MAE of 227.57, which is acceptable. However, it produced an MSE of 160,653.22, an RMSE of 394.49, and a maximum SNR of only 1.33 dB. MS-ICA performs far better with a best MAE of only 0.44, an MSE of 3.33, an RMSE of 1.76, and an SNR of 30.88 dB. Paired t-test (p < 0.05) was employed to determine statistical significance,  while Cohen's d was used to assess the practical significance of the results. The effect sizes of MAE (d = 1.60), MSE (d = 1.02), RMSE (d = 1.54), and SNR (d = -9.50) were all calculated as large. These findings demonstrate that MS-ICA offers both statistical advantages and strong practical usefulness for noise removal while preserving the structural integrity of the original EEG signals. Therefore, MS-ICA proves to be the best approach for pre-processing EEG signals to be used for analysis in children with ASD
Ecofeminist Empowerment: In Preserving the Musi River Waters of Palembang City in 2023 Sanny Nofrima, Sanny Nofrima; Melinda, Melinda; Alam Mahadika, Alam Mahadika
JOURNAL OF GOVERNMENT SCIENCE Vol 6 No 2 (2025)
Publisher : Program Studi Ilmu Pemerintahan Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54144/govsci.v6i2.125

Abstract

This article discusses ecofeminist empowerment in efforts to conserve the waters of the Musi River in Palembang City in 2023. This research takes an ecofeminist approach by combining environmental and gender perspectives, focusing on the understanding that human relationships with nature have complex social and cultural dimensions. This research used a qualitative approach involving interviews and observations. The data collected were thematically analyzed to explore the understanding of ecofeminist contributions to the preservation of the waters of the Musi River. The results showed that the ecofeminist approach has made an important contribution to the efforts to conserve the waters of the Musi River. However, several factors that influence ecofeminist empowerment still need to be addressed. Social and cultural factors, such as gender stereotypes and limited roles for women in environmental affairs, are obstacles in increasing women’s active participation in water conservation. In addition, inadequate policies and regulations also need to be considered to create a supportive environment for ecofeminist empowerment. In this context, collaborative efforts need to be made between the government and civil society to overcome these problems. It is hoped that the results of this research can make a significant contribution to efforts to conserve the waters of the Musi River in Palembang City, while strengthening awareness of the link between the environment and gender.
Autism Face Detection System using Single Shot Detector and ResNet50 Melinda, Melinda; Alfariz, Muhammad Fauzan; Yunidar, Yunidar; Ghimri, Agung Hilm; Oktiana, Maulisa; Miftahujjannah, Rizka; Basir, Nurlida; Acula, Donata D.
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda, Melinda; Yunidar, Yunidar; Miftahujjannah, Rizka; Rusdiana, Siti; Amalia, Amalia; Qadri Zakaria, Lailatul
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.
Smart Door Locking System for Children Using HC-SR04 and IoT Technology Melinda, Melinda; Yunidar, Yunidar; Khairia, Syaidatul; Miftahujjannah, Rizka; Sakarkar, Gopal; Basir, Nurlida
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 2: July 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n2.1293.2025

Abstract

The increasing incidence of minors accessing hazardous indoor areas—such as staircases, balconies, and rooms with sharp objects—raises serious safety concerns, often due to insufficient parental supervision. This study proposes an Internet of Things (IoT)-based automatic door lock system to enhance child safety in home environments. The system integrates dual ultrasonic sensors for distance and height detection, a KY-037 sound sensor, and an ESP32-CAM for real-time video monitoring, all accessible via a web interface. A key novelty lies in the integration of multi-sensor spatial awareness with live surveillance, enabling automated control and proactive safety features. Tested on ten children aged 4 to 6 years, the system achieved a 90% success rate in locking the door when a child under 120 cm approached within 1 meter, with an average response time of approximately 2 seconds. A sound-based alarm is also triggered when noise levels exceed 120 decibels, serving as an emergency alert. However, a 10% false negative rate was observed when children were detected at distances of 1.3 to 1.5 meters, suggesting the need for further sensor calibration. Overall, the system demonstrates strong potential as a scalable and cost-effective smart home safety solution, combining automation, real-time monitoring, and child-specific access control. Future work should focus on improving detection accuracy and extending functionality for multi-object scenarios.
EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition Fathur Rahman, Imam; Melinda, Melinda; Irhamsyah, Muhammad; Yunidar, Yunidar; Nurdin, Yudha; Wong, W.K.; Zakaria, Lailatul Qadri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.788

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.
Automated Z-Score Based Nutritional Status Classification for Children Under Two Using Smart Sensor System Yunidar, Yunidar; Melinda, Melinda; Ridara, Rina; Basir, Nurlida
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.
Analisis Persepsi dan Nilai Ekonomi Implementasi Eco Enzyme dan Smart Farming di Pondok Pesantren Eumpe Awee Ramli, Ichwana; Siregar, Ameilia Zuliyanti; Nasution, Indera Sakti; Mahidin, Mahidin; Muhibbuddin, Muhibbuddin; Arahman, Nasrul; sulastri, sulastri; melinda, melinda; Nasution, Zulkifli; Nauly, meutia; Siregar, Netti Herlina; 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
Peningkatan Minat Literasi Siswa pada Era Perkembangan Zaman di Sekolah Dasar Danau Sadar Silaban, Pangeran O. J; Jalianery, Joanita; Setiawan, Rendy; Rofiah, Siti; Yudesman, Fatriani Margareta; Nego, Abed; Cloudya, Cindy; Melinda, Melinda; Kristiana, Kristiana; Nabella, Putri Rama; Rajagukguk, Katarina Rani; Alfian, Ridho; Syahputra, Daniel; Simanjorang, Rican; Peronika, Agustina
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 7 (2025): September
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i7.2979

Abstract

Pelaksanaan Kuliah Kerja Nyata (KKN) merupakan bentuk pengabdian mahasiswa kepada masyarakat dalam upaya menanggulangi berbagai permasalahan, termasuk dalam bidang pendidikan. Di Desa Danau Sadar, Provinsi Kalimantan Tengah, program KKN difokuskan pada isu rendahnya minat literasi di kalangan anak-anak dan masyarakat. Meskipun desa ini telah memiliki fasilitas pendidikan seperti perpustakaan desa dan sekolah dasar, tantangan dalam meningkatkan literasi masih signifikan. Salah satu penyebab utama adalah pengaruh media elektronik, seperti handphone, yang mendorong anak-anak lebih aktif di media sosial dibandingkan kegiatan membaca, menulis, dan menyimak. Untuk mengatasi hal tersebut, mahasiswa Universitas Palangka Raya mengembangkan 13 program kerja berbasis literasi yang dilaksanakan melalui kerja sama dengan Perpustakaan Nasional, Perpustakaan Desa, dan lembaga sekolah. Program ini bertujuan meningkatkan kemampuan literasi dasar serta membentuk kebiasaan membaca yang lebih baik di masyarakat Desa Danau Sadar.
H20 and H20 with NaOH-Based Multispectral Classification Using Image Segmentation and Ensemble Learning EfficientNetV2, Resnet50, MobileNetV3 Melinda, Melinda; Yunidar, Yunidar; Zulhelmi, Zulhelmi; Suyanda, Arya; Qadri Zakaria, Lailatul; Wong, W.K
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1016

Abstract

High Multispectral imaging has become a promising approach in liquid classification, particularly in distinguishing visually similar but subtly spectrally distinct solutions, such as pure water (H₂O) and water mixed with sodium hydroxide (H₂O with NaOH). This study proposed a classification system based on image segmentation and deep learning, utilizing three leading Convolutional Neural Network (CNN) architectures: ResNet 50, EfficientNetV2, and MobileNetV3. Before classification, each multispectral image was processed through color segmentation in HSV space to highlight the dominant spectral, especially in the hue range of 110 170. The model was trained using a data augmentation scheme and optimized with the Adam algorithm, a batch size of 32, and a sigmoid activation function. The dataset consists of 807 images, including 295 H₂O images and 512 H₂O with NaOH images, which were divided into training (64%), validation (16%), and testing (20%) data. Experimental results show that ResNet50 achieves the highest performance, with an accuracy of 93.83% and an F1 score of 93.67%, particularly in identifying alkaline pollution. EfficientNetV2 achieved the lowest loss (0.2001) and exhibited balanced performance across classes, while MobileNetV3, despite being a lightweight model, remained competitive with a recall of 0.97 in the H₂O with NaOH class. Further evaluation with Grad CAM reveals that all models focus on the most critical spectral areas of the segmentation results. These findings support the effectiveness of combining color-based segmentation and CNN in the spectral classification of liquids. This research is expected to serve as a stepping stone in the development of an efficient and accurate automatic liquid classification system for both laboratory and industrial applications.
Co-Authors . Roslidar Aafiyah, Siti Afra Abdurohim Abdurohim, Abdurohim Abed Nego, Abed Abrina Anggraini, Sinar Perbawani Achmad Maqsudi, Achmad Achmad, Ilham Acula, Donata D Acula, Donata D. Adawiyah, Muna Robiatul Afdhal Afdhal Afnan, Afnan Agnesia Candra Sulyani Agung Enriko, I Ketut Agus Herwanto Ahmad, R. Andriadi Ahmadiar, Ahmadiar Akbar, Alif Yafi Al Bahri Alam Mahadika, Alam Mahadika Albahri, Albahri Albar, Nizam Alfariz, Muhammad Fauzan Alfatirta Mufti Alfatirta Mufti Alfian, Ridho Alifia, Rania Sofie Amalia Amalia Amaliatulwalidain, Amaliatulwalidain Ameilia Zuliyanti Siregar Anabel, Cendana Ananda, Mulya Anik Puryatni Anto Ariyanto, Anto 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 Dewi, Cut 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 Farhan Fathur Rahman, Imam Fathurrahman Fathurrahman Fitri Arnia Fitriyanti, Emiliy Fuaidah, Mahayaya Ghimri, Agung Hilm Gopal Sakarkar Hamdani Hamdani Hanryono, Hanryono Harahap, Subur Harjoedi Adji Tjahjono, Harjoedi Adji Hasan, Hafidh Hasan, Vania Pratama Heltha, Fahri Herlina Dimiati, Herlina Herlina Herlina Hubbul Walidainy I Gusti Bagus Astawa I Ketut Agung Enriko Iis Juniati Lathiifah Indarti, Ghinna Yulia Indera Sakti Nasution Indriani, Berlian Irawan Irawan Irhmasyah, Muhammad Irvan kurniawan, Muhammad Iskandar Hasanuddin Iskandar Hasanuddin Islamy, Fajrul Joanita Jalianery Junidar, Junidar Karlisa Priandana Kencana, Novia Khairah, Alfita Khairia, Syaidatul Khatami, Muhammad Kristiana kristiana Lailatul Qadri Zakaria Leo, Hendrik Lerrick, Yudith F. Lisbeth Lesawengen, Lisbeth Lucky, Muhammad Luju, Elisabet Lukman Hidayat M Fahrur Rozi Magfirah, Inayah Zaini Mahidin Mahidin Malahayati, M. Margarethy Rohanie Mbado Maulana Imam Muttaqin Maulana, Muhammad Iqbal Maulisa, Oktiana Mayanti, Andi Meutia Nauly Miftahujjannah, Rizka Mina Rizky , Muharratul Mina Rizky, Muharratul Mirza Rahmat, Muhammad Misbahuddin Misbahuddin Mohd. Syaryadhi Morita Sari Muhamad Risal Tawil Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Ridwan Muhibbuddin Muhibbuddin Muhibuddin Muhibuddin, Muhibuddin Muliyadi Muliyadi Mulyadi Mulyadi Mulyadi, Yose Ega Mustikawati, Yunitari Muttaqin, Ikram N Nasaruddin Nabella, Putri Rama Nabila, Nissa Hasna Nasaruddin Nasaruddin Nasaruddin Nasaruddin Nasaruddin Syafie Nasrul Arahman Nasrul Nasrul Nazilla, Izza 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 Putri Mauliza, Putri Qadri Zakaria, Lailatul Rafiki, Aufa Rahmi Susanti Raihan, Siti Rajagukguk, Katarina Rani Ramadan, Muhammad Fahreza Ramadhan, Irsyan Ramadhani, Hanum Aulia Ramdhana, Rizka Ramli, Amaliatulwalidain Ramli, Ichwana Rendy Setiawan Ridara, Rina Rini Safitri Riska Sufina Rita Khatir Rizal Syahyadi 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 Siregar, Netti Herlina Siska, Emi Yulia Siti Rofiah, Siti Siti Rusdiana Sitti Suhada Solissa, Ferdinando Suhara, Ade Sulastri Sulastri Suriati, Israini Suwandi Suwandi Suyanda, Arya Syahputra, Daniel Syahrial Syahrial, Syahrial Syahyadi, Rizal Syakir, Fakhrus Tandi, Asrin Tariliani, Cut Dara Taufik Iskandar Taufiq Abdul Gani Teuku Muhammad Mirza Keumala Tulus Tulus Ugi Nugraha Ulul Azmi Umrah, Andi Sitti Waani, Fonny J Wahyudianty, Melsa Ulfie Waladah, Bulen 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 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 Zulkifli Nasution