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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 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 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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FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION Syakir, Fakhrus; Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Zulhelmi, Zulhelmi; Miftahujjannah, Rizka
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
Pengenalan Robotika sebagai Media Pembelajaran STEM di SMA Labschool Unsyiah Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Islamy, Fajrul; Priandana, Karlisa; Basir, Nurlida; Safitri, Rini
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi robotika telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dunia pendidikan. Robotika, sebagai bidang multidisiplin yang menggabungkan aspek mekanika, elektronika, dan ilmu komputer, memiliki potensi besar untuk diterapkan dalam pembelajaran berbasis Science, Technology, Engineering, and Mathematics (STEM). Artikel ini bertujuan mendeskripsikan pelaksanaan kegiatan pengabdian masyarakat berupa pengenalan sistem robotika di SMA Labschool Unsyiah Banda Aceh serta mengkaji dampaknya terhadap pengetahuan dan motivasi siswa. Metode pelaksanaan kegiatan meliputi identifikasi kebutuhan mitra, perencanaan materi, pelaksanaan kegiatan, praktik sederhana, hingga evaluasi. Dokumentasi kegiatan menunjukkan antusiasme tinggi dari siswa selama mengikuti seluruh rangkaian kegiatan. Hasil evaluasi melalui kuesioner dan observasi lapangan mengindikasikan bahwa siswa memperoleh peningkatan pemahaman tentang konsep dasar robotika serta terdorong untuk lebih berminat pada bidang STEM. Guru pendamping juga menilai kegiatan ini relevan dengan kebutuhan pembelajaran dan membuka peluang pengembangan robotika sebagai kegiatan ekstrakurikuler di sekolah. Dengan demikian, kegiatan pengenalan robotika tidak hanya berkontribusi pada peningkatan literasi teknologi siswa, tetapi juga menjadi langkah awal dalam membangun ekosistem pendidikan berbasis teknologi yang berkelanjutan di SMA Labschool Unsyiah Banda Aceh.
Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda, Melinda; Maulisa, Oktiana; Nabila, Nissa Hasna; Yunidar, Yunidar; Enriko, I Ketut Agung
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.
Paradoks Kebebasan Beragama di Indonesia: Antara Ketertiban Sosial dan Hukum Negara Odelia, Marsha; Maharani, Citra Ayu Deswina; Ramadhani, Dina; Melinda, Melinda; Elviandri, Elviandri
Politica: Jurnal Hukum Tata Negara dan Politik Islam Vol. 12 No. 2 (2025): POLITICA: Jurnal Hukum Tata Negara dan Politik Islam
Publisher : Prodi Tata Negara (Siyasah) IAIN Langsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32505/politica.v12i2.13476

Abstract

Restrictions on freedom of religion within Indonesia’s legal system continue to generate normative and practical debates, particularly due to regulatory practices that tend to be repressive and discriminatory toward certain religious groups. This situation reflects an ongoing tension between the protection of fundamental rights and the state’s interest in maintaining social order. This study aims to analyze freedom of religion in Indonesia from a utilitarian perspective, specifically through John Stuart Mill’s harm principle, and to propose a legal reformulation oriented toward justice and the promotion of the common good. The research employs a normative legal method using philosophical and conceptual approaches, drawing on statutory regulations, legal doctrines, and utilitarian legal philosophy. The findings indicate that current restrictions on religious freedom in Indonesia are inconsistent with utilitarian principles, as they often undermine the greatest happiness of those affected without clear evidence of actual harm to society at large. The proposed legal reform includes revising discriminatory regulations, simplifying the licensing procedures for houses of worship in a non-discriminatory manner, and accelerating the establishment of a National Regulatory Body as a mechanism for legal harmonization. The application of the harm principle in public policymaking has significant implications for strengthening the protection of religious freedom, balancing individual liberty with social order, and fostering a more just and welfare-oriented legal system.
Rancang Bangun Sistem Steganografi Multi-format LSB dan Enkripsi Fernet Berbasis Python Khairah, Divaul; Melinda, Melinda; Muna, Isyatul; Kharina, Kharina
JIKUM: Jurnal Ilmu Komputer Vol. 2 No. 2 (2026): JIKUM: Jurnal Ilmu Komputer, November 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/jikum.v2i2.211

Abstract

This research designs a data security system using Python by integrating Fernet Encryption and LSB Steganograph. The system features multi-format capabilities, allowing users to embed text or files into image and audio media. The process begins by encrypting data using a password-based key, which is then embedded into the least significant bits of the carrier media. Experimental results demonstrate that the system effectively maintains data confidentiality with high visual quality. However, the embedding capacity is strictly limited by the size of the carrier media; oversized files cannot be processed if they exceed the available LSB bit space. This system provides an effective solution for small to medium- scale data protection through double-layer security.
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation Fathur Rahman, Imam; Melinda, Melinda; Yunidar, Yunidar; Basir, Nurlida; Rafiki, Aufa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.313

Abstract

Accurate and reliable classification of autism spectrum disorder (ASD) from electroencephalography (EEG) signals remains challenging due to the inherently nonstationary, nonlinear, and multichannel nature of EEG data. These properties complicate the extraction of discriminative features that are both stable and computationally efficient. To address this challenge, this study proposes a compact deep-learning pipeline that integrates the Multivariate Empirical Wavelet Transform (MEWT) with EEGNet for ASD–EEG classification. MEWT decomposes multichannel EEG signals into spectrally aligned subbands while preserving inter-channel relationships. The resulting MEWT-based features are then processed by EEGNet, a lightweight convolutional neural network specifically designed for EEG-based learning tasks. Performance was evaluated using 5-fold cross-validation. The proposed MEWT with the the EEGNet model achieved a mean test accuracy of 98.35%, with consistently high precision (98.23%), recall (98.45%), F1-score (98.34%), and specificity (98.24%) across all folds. Confusion-matrix results indicated very few and well-balanced false positives and false negatives, supporting stable discrimination between ASD and control EEG segments. A one-sample one-tailed t-test against a 50% chance level confirmed that all evaluated metrics were significantly above chance (p < 0.0001). When benchmarked against previously reported results on the same dataset, the proposed approach slightly improved upon EMD with EEGNet (97.99%) and clearly outperformed EWT with EEGNet (95.08%), suggesting that MEWT-derived multichannel features are well matched to compact convolutional architectures for ASD–EEG analysis. Despite these strong results, the study is limited by a small, single-site cohort and the use of a single deep-learning model. Future work will focus on standardized retraining across multiple feature families and validation on larger and more diverse populations to further assess robustness and generalizability
Pelatihan dam implementasi eko enzim mendukung pertanian ramah lingkungan memitigasi ketahanan pangan di Pesantren Al Kautsar, Medan Ameilia Zuliyanti Siregar; Zulkifli Nasution; Meutia Nauly; Tulus Tulus; Netti Herlina Siregar; Ichwana Ramli; Mahidin Mahidin; Nasrul Nasrul; Indera Sakti Nasution; Melinda Melinda; Muhibuddin Muhibuddin
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 5 (2025): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i5.34006

Abstract

Abstrak Kegiatan Pengabdian masyarakat Kolaborasi Indonesia (PMKI) USU 2025 sebagai co-host dengan host Universitas Syiah Kuala (USK) ini dilaksanakan di Pondok Pesantren Al Kautsar Al Akbar, Medan, dengan tujuan meningkatkan pengetahuan dan keterampilan santri dalam mengolah sampah organik menjadi ekoenzim sebagai produk ramah lingkungan yang bermanfaat. Metode yang digunakan adalah Asset Based Community Development (ABCD) melalui tahapan discovery, dream, design, define, dan destiny. Pelaksanaan meliputi Sosialisasi, Pelatihan pembuatan ekoenzim, pembuatan desinfektan berbahan ekoenzim, serta implementasinya pada kegiatan berkebun. Sebanyak 40 santri (15 putra dan 25 putri, rentang usia 14-17 Tahun, santri dari kelas 1, 2, 3 SMA) yang mengikuti kegiatan,  diawali dengan pre-test dan diakhiri dengan post-test. Hasil evaluasi menunjukkan peningkatan pemahaman signifikan, dengan rata-rata nilai pre-test 65,25 dan post-test 93,5. Sebanyak 95% peserta menyatakan kegiatan sangat menarik, 93% menyatakan bermanfaat, dan 89% menyatakan pemahamannya meningkat. Kegiatan ini berhasil membekali santri dengan keterampilan pengolahan sampah organik menjadi produk bernilai guna dalam bentuk eko enzim yang mendukung pertanian ramah lingkungan dan mitigasi masalah ketahanan pangan di lingkungan pesantren. Kata kunci: ekoenzim; pengolahan sampah organik; pertanian ramah lingkungan; pengabdian masyarakat. Abstract The Indonesian Collaboration Community Service (ICCS) of University Sumatera Utara Programme 2025 as co-hosy qith host was University Syiah Kuala (USK) was conducted at Pondok Pesantren Al Kautsar Al Akbar, Medan, aiming to enhance students’ knowledge and skills in processing organic waste into eco-enzyme, an environmentally friendly and beneficial product. The program adopted the Asset Based Community Development (ABCD) method through five stages: discovery, dream, design, define, and destiny. Activities included awareness sessions, eco-enzyme production training, eco-enzyme-based disinfectant preparation, and its application in gardening activities. A total of 40 students (15 males and 25 females, age rates was 14-17 years old, consit of 1, 2, 3 SMA students),  starting with a pre-test and ending with a post-test. Evaluation results indicated a significant improvement in understanding, with average pre-test and post-test scores of 65.25 and 93.5, respectively. Furthermore, 95% of participants stated the program was very interesting, 93% found it beneficial, and 89% reported increased comprehension. This program successfully equipped students with skills in organic waste processing into value-added products, while supporting environmentally friendly agriculture and contributing to food security resilience in the pesantren environment. Keywords: eco-enzyme; organic waste processing; community service; environmentally friendly agriculture.
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.
Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda, Melinda; Gazali, Syahrul; Away, Yuwaldi; Rafiki, Aufa; Wong, W.K; Muliyadi, Muliyadi; Rusdiana, Siti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.
FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION Syakir, Fakhrus; Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Zulhelmi, Zulhelmi; Miftahujjannah, Rizka
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 Agus Herwanto 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 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 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 Heltha, Fahri Herlina Dimiati, Herlina Herlina Herlina Hubbul Walidainy I Gusti Bagus Astawa I Ketut Agung Enriko Ichwana Ramli Iis Juniati Lathiifah Indarti, Ghinna Yulia Indera Sakti Nasution Indera Sakti Nasution Indriani, Berlian Irawan Irawan Irvan kurniawan, 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 Lucky, Muhammad Luju, Elisabet Lukman Hidayat M Fahrur Rozi Magfirah, Inayah Zaini Maharani, Citra Ayu Deswina Mahfuzha, Raudhatul Mahidin Mahidin Mahidin Mahidin Malahayati, M. Margarethy Rohanie Mbado Maulana Imam Muttaqin Maulana, Muhammad Iqbal Maulisa, Oktiana Mayanti, Andi 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 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 Rafiki, Aufa Rahmi Susanti Raihan, Siti Rajagukguk, Katarina Rani Ramadan, Muhammad Fahreza Ramadhani, Dina 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 Suriadi Suriadi 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 Tulus Tulus Ugi Nugraha Ulul Azmi Umrah, Andi Sitti 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 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 Zulkifli Nasution