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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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Hubungan Penyakit yang Mendasari dengan Status Antropometri pada Pasien Poli Anak Rumah Sakit Umum Daerah Saiful Anwar Malang Alifia, Rania Sofie; Puryatni, Anik; Melinda, Melinda; Tjahjono, Harjoedi Adji
Sari Pediatri Vol 26, No 1 (2024)
Publisher : Badan Penerbit Ikatan Dokter Anak Indonesia (BP-IDAI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14238/sp26.1.2024.1-8

Abstract

Latar belakang. Status antropometri merupakan parameter nutrisi anak yang dinilai berdasarkan indeks antropometri sesuai dengan Permenkes Nomor 2 Tahun 2020. Penelitian terdahulu menunjukkan bahwa status antropometri berkaitan dengan penyakit infeksi dan non-infeksi melalui beberapa mekanisme.Tujuan. Penelitian ini bertujuan untuk mengetahui hubungan antara penyakit yang mendasari, baik infeksi maupun non-infeksi, dengan status antropometri.Metode. Penelitian ini adalah penelitian kuantitatif dengan desain analitik observasional dan rancangan kohort. Sampel diambil dari data registrasi pasien di poli anak Rumah Sakit Umum Daerah Dr. Saiful Anwar Malang selama periode Agustus 2022 hingga Oktober 2022. Kriteria inklusi adalah pasien yang berusia 0-18 tahun, menderita penyakit infeksi atau non-infeksi, dan memiliki catatan medis lengkap. Pasien dengan catatan medis yang tidak lengkap atau menderita penyakit infeksi dan non-infeksi sekaligus dikeluarkan dari penelitian.Hasil. Dari 532 anak yang menjadi sampel, sebagian besar berusia 5-18 tahun (56,77%) dan menderita penyakit non-infeksi (82,89%). Sebagian besar anak dengan penyakit infeksi memiliki berat badan normal (61,9%), tinggi normal (54,8%), dan status gizi baik (76,2%) pada usia 0-60 bulan, serta status gizi baik (67,3%) pada usia 5-18 tahun. Anak dengan penyakit non-infeksi juga sebagian besar memiliki berat badan normal (63,3%), tinggi normal (54,8%), dan status gizi baik (66%) pada usia 0-60 bulan, serta status gizi baik (61,7%) pada usia 5-18 tahun. Metode analisis menggunakan uji likelihood menunjukkan bahwa nilai p pada semua variabel lebih dari 0,05.Kesimpulan. Tidak ditemukan hubungan yang signifikan antara penyakit yang mendasari, baik infeksi maupun non-infeksi, dengan status antropometri pada anak.
MANAJEMEN RISIKO KEUANGAN: INTEGRASI PENDEKATAN MANAJEMEN EKONOMI DAN AKUNTANSI UNTUK MENGELOLA RISIKO PASAR DAN KREDIT Wahyudianty, Melsa Ulfie; Suhara, Ade; Tandi, Asrin; Melinda, Melinda; RoziqiFath, Zain Fuadi Muhammad
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 3 No. 2 (2022)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

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Abstract

Manajemen risiko keuangan adalah bagian integral dari strategi perusahaan yang berfokus pada pengelolaan risiko pasar dan kredit. Artikel ini menyelidiki integrasi antara pendekatan manajemen ekonomi dan akuntansi dalam mengelola risiko ini, dengan penekanan khusus pada risiko pasar dan kredit. Kami melihat studi kasus perusahaan yang telah mengadopsi pendekatan ini dan menganalisis dampaknya pada kinerja keuangan perusahaan. Pendekatan manajemen ekonomi melibatkan pemahaman yang mendalam tentang tren ekonomi makro dan mikro yang dapat mempengaruhi perusahaan. Ini mencakup pemantauan perubahan dalam suku bunga, nilai tukar mata uang, indeks saham, dan faktor-faktor eksternal lainnya yang dapat berdampak pada risiko pasar. Di sisi lain, pendekatan manajemen akuntansi fokus pada analisis laporan keuangan perusahaan dan penilaian kualitas kredit pihak ketiga. Integrasi kedua pendekatan ini memungkinkan perusahaan untuk lebih efektif mengidentifikasi, mengukur, dan mengelola risiko pasar dan kredit. Hasil analisis menunjukkan bahwa perusahaan yang mengadopsi integrasi ini mengalami peningkatan signifikan dalam mitigasi risiko dan kinerja keuangan yang lebih baik. Artikel ini menggarisbawahi pentingnya kerja sama antara departemen manajemen ekonomi dan akuntansi dalam mengelola risiko keuangan. Kami juga membahas implikasi praktis dari pendekatan ini dalam konteks perusahaan-perusahaan lain yang berencana untuk mengadopsi strategi manajemen risiko yang serupa.
Analisis Penerapan Akuntansi pajak Pertambahan Nilai (PPN) dan Pengaruhnya Terhadap Penyusunan Laporan Keuangan Hanryono, Hanryono; Maqsudi, Achmad; Solissa, Ferdinando; Saepudin, Udin; Melinda, Melinda
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 3 No. 2 (2022)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

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Abstract

Penelitian ini bertujuan untuk menganalisis penerapan akuntansi pajak Pertambahan Nilai (PPN) dalam konteks pengaruhnya terhadap penyusunan laporan keuangan perusahaan. Dalam lingkungan bisnis yang terus berkembang, peraturan pajak menjadi aspek penting yang perlu diperhatikan oleh perusahaan dalam penyusunan laporan keuangan mereka. Terutama, pajak Pertambahan Nilai (PPN) adalah salah satu pajak yang memiliki dampak signifikan terhadap posisi keuangan perusahaan. Penelitian ini menggunakan metode analisis data sekunder yang melibatkan data laporan keuangan publik dari berbagai industri. Penelitian ini mengidentifikasi dampak penerapan akuntansi pajak Pertambahan Nilai (PPN) terhadap aspek-aspek penyusunan laporan keuangan seperti laba bersih, arus kas, dan ekuitas. Hasil penelitian mengungkapkan bahwa metode penerapan PPn dapat berpengaruh signifikan terhadap posisi keuangan perusahaan. Selain itu, penelitian ini juga mengeksplorasi dampak penerapan PPN pada praktik perpajakan dan komitmen sosial perusahaan. Hasilnya memberikan pemahaman yang lebih dalam tentang sejauh mana praktik perpajakan perusahaan mempengaruhi penyusunan laporan keuangan dan tanggung jawab sosial perusahaan. Dalam era globalisasi dan harmonisasi standar akuntansi, pemahaman yang mendalam tentang penerapan PPN dan pengaruhnya terhadap laporan keuangan menjadi penting bagi para pemangku kepentingan, termasuk investor, regulator, dan manajemen perusahaan. Penelitian ini memberikan wawasan yang berharga tentang pentingnya akuntansi pajak Pertambahan Nilai (PPN) dalam konteks penyusunan laporan keuangan dan dapat membantu perusahaan dalam mengoptimalkan pengelolaan pajak dan memahami dampaknya pada kinerja keuangan mereka.
Thermal Image Classification of Autistic Children Using Res-Net Architecture Ahmadiar, Ahmadiar; Melinda, Melinda; Muthiah, Zharifah; Zainal, Zulfan; Mina Rizky, Muharratul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/365fkd59

Abstract

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.
Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization Rafiki, Aufa; Melinda, Melinda; Oktiana, Maulisa; Dewi Meutia, Ernita; Afnan, Afnan; Mulyadi, Mulyadi; Zakaria, Lailatul Qadri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/40n05b64

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behavioral patterns. Early detection of ASD is crucial for improving the quality of life of affected individuals and alleviating the burden on their families. This study proposes a computer-aided diagnostic system for ASD by applying a pre-trained Vision Transformer (ViT-B/16) architecture to EEG signal data obtained from King Abdul Aziz University. The dataset comprises EEG recordings from 16 subjects (8 normal and 8 ASD) that have undergone preprocessing—including filtering using the Discrete Wavelet Transform (DWT), segmentation (windowing), and conversion into heatmap representations—and were subsequently partitioned into training, validation, and testing subsets. The ViT model was trained for 100 epochs with a batch size of 16, using the AdamW optimizer and the CrossEntropy loss function, while two learning rate configurations (0.0001 and 0.00001) were evaluated; the best-performing weights were selected based on the lowest validation loss. Test results indicate that the model trained with a learning rate of 0.00001 achieved a testing accuracy of 99.53%, accompanied by excellent precision, specificity, recall, and f1-score, thereby demonstrating strong generalization capabilities and minimal overfitting. Future research is recommended to incorporate locally sourced datasets and to further customize the ViT architecture through comprehensive hyperparameter tuning, with the aim of developing a mobile application to support clinical ASD diagnosis.
IoT-Enhanced mechanical system for fogponic cultivation: Air circulation and environmental control Heltha, Fahri; Yunidar, Yunidar; Syahyadi, Rizal; Melinda, Melinda; Azhari, Rizki; Elizar, Elizar
Jurnal Polimesin Vol 23, No 1 (2025): February
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i1.6183

Abstract

Fogponic cultivation, a hydroponic technique that utilizes water mist for nutrient delivery, offers a significant advantage in water and nutrient efficiency. However, suboptimal air circulation, temperature, and humidity in the root chamber can hinder plant growth and nutrient uptake. This study develops an IoT-enhanced mechanical system to optimize environmental conditions in a fogponic root chamber for cultivating spinach (Spinacia oleracea) seedlings. An actuator in the form of a fan was integrated to regulate air circulation, and managed by a Proportional Integral Derivative (PID) controller for precise temperature and humidity control. The system was monitored using the IoT-based Blink application. The results showed that the PID controller effectively regulated environmental conditions, with optimized parameter values: Kp = 5.76, Ki = 0.576, and Kd =14.4. Performance comparisons with P, PI, and PD controllers demonstrated effective humidity control, achieving the target set point of 92% with rise times of 447–1090 seconds and steady-state errors of 0–0.5%. By integrating mechanical components such as the fan with IoT-based monitoring, the system achieves continuous adjustments to the environment, enhancing plant growth conditions. 
Comparative Analysis of Homomorphic and Morphological Filters Using Inception V3 for Thermal Facial Image Classification of Autistic Children Catur Andryani, Nur Afny; Melinda, Melinda; Tariliani, Cut Dara; Oktiana, Maulisa; Junidar, Junidar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2885

Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by varying degrees of difficulty in social interaction and communication and repetitive behaviors. Early confirmation of the diagnosis of ASD leads to early appropriate treatment. However, confirming ASD diagnosis is challenging due to its wide spectrum and challenging behavior assessment. This research proposes a technology-based ASD diagnosis on children utilizing thermal facial analysis. This is conducted subject to the uniqueness of facial expression that is typically applied to children with ASD. A modified Inception V3 architecture did the intended thermal facial analysis for ASD diagnosis. Homomorphic filters and morphological filters are applied to the data pre-processing to improve the classification ability. The proposed identification method shows better sensitivity to the false-positive aspect. It is indicated by better performance in terms of precision, with a rate of 90% to 91%. This research is expected to support medical experts in confirming early diagnosis in children with ASD.
“Green Aisyiyah” : Praksis Ekofeminis Gerakan Aisyiyah dalam Mengatasi Perubahan Iklim di Indonesia Ramli, Amaliatulwalidain; Kencana, Novia; Melinda, Melinda
Jurnal Pemerintahan dan Politik Vol. 8 No. 3 (2023)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jpg.v8i3.2205

Abstract

This study aims to identify the Green Aisyiyah Program as a realization of the eco-feminist idea of dealing with climate change in Indonesia. The Green Aisyiyah Program is a program initiated by the Division of the Environmental Agency for Disaster Management (LLHPB), as one of the divisions within the structure of the Aisyiyah Movement. In elaborating this research, using qualitative methods with a phenomenological approach supported by Green Politics theory and ecofeminist theory. The results of the study explained that the Green Aisyiyah program is synonymous with empowering women to commit and contribute to protecting the environment in a sustainable manner in anticipation of climate change, various derivatives of the Green Aisyiyah program such as Green Ramadan, Green Eid al-Fitr, and Green Eid al-Adha were constructed through various activities that are very close to the daily lives and lives of women as agents of change because it cannot be denied that women are an important subject in access to natural resources, food and the environment.
Precise Electrocardiogram Signal Analysis Using ResNet, DenseNet, and XceptionNet Models in Autistic Children Yunidar, Yunidar; Melinda, Melinda; Albahri, Albahri; Ramadhani, Hanum Aulia; Dimiati, Herlina; Basir, Nurlida
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.v7i4.1044

Abstract

In autistic children, one of the important physiological aspects to be examined is the heart condition, which can be assessed through electrocardiogram (ECG) signal analysis. However, ECG signals in autistic children often contain interference in the form of noise, making the analysis process, both manual and conventional, challenging. Therefore, this study aims to analyze the ECG signals of autistic children using a classification method to distinguish between two main conditions: playing and calm conditions. A deep learning approach employing the Convolutional Neural Network (CNN) architectures was used to obtain accurate results in distinguishing the heart conditions of autistic children. The data used consists of 700 ECG signal data in each class, processed through the filtering, windowing, and augmentation stages to obtain balanced data. Three CNN architectures, ResNet, DenseNet, and XceptionNet, were tested in this study. Although these architectures are originally designed for 2D and 3D image data, modifications were made to adapt the input data structure to perform 1D data calculations. The evaluation results show that the XceptionNet model achieved the best performance, with accuracy, precision, recall, and F1-score of 97,14% each, indicating a good ability in capturing the complex patterns of ECG signals. Meanwhile, the ResNet obtained good results with 96,19% accuracy, while DenseNet performed slightly lower results with 94,76% accuracy and evaluation metrics. Overall, this study demonstrates that a deep CNN architecture based on dense connections can enhance the accuracy of ECG signal classification in autistic children.
Image Segmentation Performance using Deeplabv3+ with Resnet-50 on Autism Facial Classification Melinda, Melinda; Aqif, Hurriyatul; Junidar, Junidar; Oktiana, Maulisa; Binti Basir, Nurlida; Afdhal, Afdhal; Zainal, Zulfan
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1144

Abstract

In recent years, significant advancements in facial recognition technology have been marked by the prominent use of convolutional neural networks (CNN), particularly in identification applications. This study introduces a novel approach to face recognition by employing ResNet-50 in conjunction with the DeepLabV3 segmentation method. The primary focus of this research lies in the thorough analysis of ResNet-50's performance both without and with the integration of DeepLabV3+ segmentation, specifically in the context of datasets comprising faces of children on the autism spectrum (ASD). The utilization of DeepLabV3+ serves a dual purpose: firstly, to mitigate noise within the datasets, and secondly, to eliminate unnecessary features, ultimately enhancing overall accuracy. Initial results obtained from datasets without segmentation demonstrate a commendable accuracy of 83.7%. However, the integration of DeepLabV3+ yields a substantial improvement, with accuracy soaring to 85.9%. The success of DeepLabV3+ in effectively segmenting and reducing noise within the dataset underscores its pivotal role in refining facial recognition accuracy. In essence, this study underscores the pivotal role of DeepLabV3+ in the realm of facial recognition, showcasing its efficacy in reducing noise and eliminating extraneous features from datasets. The tangible outcome of increased accuracy of 85.9% post-segmentation lends credence to the assertion that DeepLabV3+ significantly contributes to refining the precision of facial recognition systems, particularly when dealing with datasets featuring faces of children on the autism spectrum.
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