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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian IJOG : Indonesian Journal on Geoscience Journal of Degraded and Mining Lands Management Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis JOURNAL OF HEALTHCARE TECHNOLOGY AND MEDICINE JURNAL SIPIL SAINS Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Sumatra Journal of Disaster, Geography and Geography Education JURNAL MEDIA INFORMATIKA BUDIDARMA Chimica et Natura Acta Ilmu Budaya: Jurnal Bahasa, Sastra, Seni, dan Budaya JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Transformative Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia Kurawal - Jurnal Teknologi, Informasi dan Industri INSIST (International Series on Interdisciplinary Research) Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Sistem Komputer & Kecerdasan Buatan Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Distilat: Jurnal Teknologi Separasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Advance Sustainable Science, Engineering and Technology (ASSET) ALACRITY : Journal Of Education Jurnal IPTEK Bagi Masyarakat Indonesian Journal of Islamic History and Culture Arus Jurnal Sosial dan Humaniora Jurnal Pemberdayaan Masyarakat Indonesia (JPMI) Journal of Social Research KINDAI : Kumpulan Informasi dan Artikel Ilmiah Manajamen dan Akuntansi Prosiding Seminar Nasional Teknik Sipil UMS terAs Law Review: Jurnal Hukum Humaniter dan HAM eProceedings of Art & Design Jurnal Mahasiswa Ilmu Komputer Jurnal Multidisiplin West Science Innovative: Journal Of Social Science Research Journal of Economics and Management Scienties JURNAL PENDIDIKAN OLAHRAGA Ekonom : Jurnal Ekonomi dan Bisnis Kohesi: Jurnal Sains dan Teknologi BAKIRA: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmu Komunikasi dan Sosial Politik Journal of Physics Education and Science Peksos: Jurnal Ilmiah Pekerjaan Sosial MARAS : Jurnal Penelitian Multidisplin Jurnal Riset Ilmiah Review: Journal of Multidisciplinary in Social Sciences Soedirman Law Review Bayt ElHikmah: Journal of Islamic Architecture and Locality The Indonesian Journal of Computer Science Jurnal Penelitian Teknik Sipil Konsolidasi Jurnal Hukum Statuta Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) JoDENS: Journal of Digital Ecosystem for Natural Sustainability IJEFSD Almantiq INTERACTION: Jurnal Pendidikan Bahasa JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) POTENSI: Jurnal Pengabdian Masyarakat Ekonomi dan Bisnis Jurnal Pariwisata dan Budaya (RANATAYA) Nosipakabelo: Jurnal Bimbingan dan Konseling Keislaman
Muhammad Naufal, Muhammad
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Pengaruh Media Sosial TikTok Dalam Dakwah Islam Di Kalangan Mahasiswa Prodi Bimbingan Penyuluhan Islam UIN Syarif Hidayatullah Jakarta Ryazi, Inggit Dzulka; Nabila, Hilma Agisna; Nasichah; Naufal, Muhammad
Nosipakabelo: Jurnal Bimbingan dan Konseling Islam Vol. 4 No. 01 (2023): NOSIPAKABELO : JURNAL BIMBINGAN DAN KONSELING KEISLAMAN
Publisher : Program Studi Bimbingan dan Konseling Islam IAIN Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24239/nosipakabelo.v4i01.2010

Abstract

Tujuan dari penelitian ini adalah mengetahui pengaruh media sosial TikTok dalam dakwah Islam di kalangan mahasiswa Prodi Bimbingan Penyuluhan Islam di UIN Syarif Hidayatullah Jakarta. Objek dalam penelitian ini adalah mahasiswa/i prodi Bimbingan Penyuluhan Islam UIN Syarif Hidayatullah Jakarta dari semester 2, semester 4 dan semester 6. Jumlah responden pada penelitian ini adalah 30 responden. Data dan penelitian ini dikumpulkan dengan teknik metode kuesioner, selanjutnya data di analisis dapat disimpulkan bahwa media sosial TikTok memiliki potensi sebagai sarana yang efektif dalam penyebaran dakwah Islam di kalangan mahasiswa/i Prodi Bimbingan Penyuluhan Islam. meskipun demikian, perlu adanya upaya untuk meningkatkan pemahaman dan kepercayaan terhadap informasi dakwah yang disampaikan melalui TikTok, serta memastikan konten yang disebarkan sesuai dengan ajaran Islam. Penelitian ini memberikan pemahaman tentang penggunaan TikTok dalam dakwah Islam di kalangan mahasiswa Prodi Bimbingan Penyuluhan Islam di UIN Syarif Hidayatullah Jakarta. Hasil penelitian ini dapat menjadi acuan bagi pendakwah dan praktisi dakwah dalam mengoptimalkan penggunaan TikTok sebagai alat komuniksi dakwah yang efektif dan relevan dalam konteks perkembangan teknologi informasi dan komunikasi.
Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection Puspita, Rahayuning Febriyanti; Naufal, Muhammad; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11256

Abstract

This study developed an object detection system for soccer games using the YOLOv8m algorithm with four main classes: player, goalkeeper, referee, and ball. The dataset, consisting of 372 annotated images, exhibited class imbalance, with significantly fewer ball instances compared to players. The basic YOLOv8m architecture was used without internal modifications, but adjustments were made to the output layer and fine-tuning of the pre-trained weights to adapt to the new dataset. Two models were compared: one without and one with advanced augmentation techniques (mosaic, mixup, cutmix). The experimental results showed an increase in mAP@50 from 74.9% to 81.4% in the augmented model, with a statistically significant difference (p < 0.01). However, model performance still decreased under extreme conditions such as high occlusion, rapid movement, and uneven lighting. The combination of data augmentation, output layer adaptation, and fine-tuning proved effective in improving object detection accuracy and provided the basis for the development of a real-time artificial intelligence-based soccer match analysis system.
ANANLISIS MANAJEMEN PELAKSANAAN PADA PROYEK PERBAIKAN RUMAH BAGI MASYARAKAT BERPENGHASILAN RENDAH (RMB) KOTA PAREPARE Naufal, Muhammad; Sulfanita, Andi; ', Hamsyah
JURNAL SIPIL SAINS Vol 15, No 2 (2025)
Publisher : Program Stud Teknik Sipil Fakultas Teknik Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/sipilsains.v15i2.8681

Abstract

Pelaksanaaan pada proyek perbaikan rumah bagi Masyarakat berpenghasilan rendah (RMB) mengalami analisis manajemen pelaksanaan. Tujuan penelitian ini adalah mengidentifikasi faktor-faktor yang menyebabkan keterlambatan proyekpada pelaksanaan proyek Perbaikan Rumah bagi Masyarakat Berpenghasilan Rendah (MBR). Jenis penelitian yang digunakan adalah penelitian kuantitatif, dengan metode survey di lapangan untuk memastikan dan menggambarkan kejadian faktual berupa pelaksanaan proyek perbaikan rumah untuk masyarakat berpenghasilan rendah (RMB) yang berlokasi di Kota Parepare Propinsi Sulawesi Selatan.  Data dianalisis dengan menggunakan metode SPSS. Dari hasil penelitian diperoleh: variabel Kurangnya disiplin pekerja (X1.3) merupakan variabel dengan nilai standar deviasi terbesar untuk x. Kurangnya disiplin pekerja (X1.3) menjadi penyebab utama tertundanya proyek tersebut, sehingga Proyek perbaikan rumah bagi masyarakat berpenghasilan rendah mengalami penundaan. Nilai faktor diturunkan tergantung pada variabel tempat terjadinya penundaan, khususnya: Variabel komunikasi yang kurang baik antar bagian bagian organisasi disetiap kontraktor (Y1.16), Keterlambatan pembayaran dari kontraktor ke subkontraktor (Y1.6), Pengaruh cuaca hujan, dll (Y1.8).
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset Saputra, Kurniawan Aji; Alzami, Farrikh; Kurniawan, Defri; Naufal, Muhammad; Muslih, Muslih; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15530

Abstract

Retinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity.
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk Sofiani, Hilda Ayu; Maulana, Isa Iant; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Rizqa, Ifan; Santoso, Dewi Agustini; Nugraini, Siti Hadiati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15585

Abstract

Hypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs.
Opportunities and Challenges of the Cheesy Corn Business: A Feasibility Study in Central Cikarang’s Clean Market Pratama, Martha Putri; Naufal, Muhammad; Sembiring, Theresia Aprilia Br; Madani, Hanif Al; Rahmawati, Irma; Dianugra, Muhammad Keizhar
Review: Journal of Multidisciplinary in Social Sciences Vol. 2 No. 11 (2025): November 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v2i11.1064

Abstract

The purpose of this article is to discuss the Cheesy Corn business as an innovative business. Cheesy Corn products offer a combination of classic flavors and new flavor variants that differ from typical cheese corn. The research method for assessing the feasibility of the Cheesy business involves a qualitative descriptive approach through systematic stages. The data analysis technique uses triangulation, which evaluates various aspects such as the market, human resource management, operations, and finance. This research focuses on the development of the Cheesy Corn business concept as an innovative new business in the Central Cikarang area. The location of the Clean Market is a strategic consideration because it has active and diverse consumer dynamics, especially from the middle and lower middle classes who are always looking for affordable but quality snacks.
Glambot SNAFH-R1: Lengan Kamera 4-DOF Biaya Rendah untuk Photobooth Indoor Berbasis Pengolahan Citra oktavia, sanni; Hendry; Naufal, Muhammad; Satyawira, Ryan; Anuari, Nong; Cecillia, Febby; Suwandi, Agus; Erz Saragih, Raymond
Journal of Digital Ecosystem for Natural Sustainability Vol 5 No 2 (2025): Desember 2025
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v5i2.322

Abstract

Advances in automated cinematography have opened new possibilities in video production. This research focuses on the development of the Glambot 'SNAFH-R1,' a robotic camera system designed for controlled indoor environments, particularly for short video sessions and photo-booth applications. The system utilizes image processing techniques to track and capture dynamic shots, enhancing the experience for models and event participants. The robotic arm is equipped with a high-speed camera and programmed to execute smooth motion paths, creating cinematic effects. Experimental results demonstrate that the system can achieve precise and repeatable camera movements, improving video quality and user experience. This study highlights the feasibility of integrating image processing and robotics in creative video production, providing an automated and cost-effective solution for the entertainment industry.
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
Co-Authors ', Hamsyah A, Biyandra Timothee Abdurrahmansyah Abdurrahmansyah, Abdurrahmansyah Achmad Achmad Achmad Chumaidi, Achmad Ade Gohan, Muhammad Adrian Permana Zen Agmi, Nadia Agus Riyanto, Indra Ahmad Cahyadi **) Ahmad Suriansyah Ahsyar, Tengku Khairil Akrom, Muhamad Al Fahreza, Muhammad Daffa Al zami, Farrikh Al-Azies, Harun Alfarizky, M Ferdy Agus Alfitri, Mira Ali Asroni Ali Rakhman Hakim Alzami, Farrikh Amalina, Azka Amanda Cahyadewi, Felicia Amanda, Amelia Andayan, Melia Andrean, Muhammad Niko Andrisyah, Fauzi Anggi Pramunendar, Ricardus Anggita, Ivan Maulana Anisa Anisa Anuari, Nong Aprillia Dwi Ardianti Ardiansyah, Felix Ardytha Luthfiarta Arifitama, Budi Arsy, Selma Kendida Ashari, Ayu Atthoriq, Muhamad Naufal Aulyanti, Davina Dewi Ayu Pertiwi Azizi, Adni Salsabil Azka, Muhammad Azwar, Erizaldy Bayu Kurniawan Brawijaya, Kriska Brilianto, Rivaldo Mersis Budi Setiawan Budoyo, Joto Candra, Mellyana Cecillia, Febby Chandra Pratama, Ragil Cinantya, Celia Darmawahyuni, Annisa Denta Saputra, Fahrizal Desthabu, Meurina Dewi Agustini Santoso Dewi Puspaningtyas Dewi, Ernia Novika Dewi, Lativa Andam Dianugra, Muhammad Keizhar Dinda Lestarini Dwi Laksono, Angga Dwi Putra, Rakhmat Dwisari Dillasamola Eka Yuniarto Eko Haryono Endang Komariah, Endang Erwin Yudi Hidayat Erz Saragih, Raymond Fadhilla, Naylatul Fadil, Muhammad Syukri Fadillah Putra Fadlullah, Rizal Fahmi Amiq Fajri, Aulia Romadhona Jihad Al Fariski, Aulia Isnaeni Fatchurohman, Hendy Fathonah, Naila Izzatun Fauziah, Ismahani Febriyanto, Hendra Ferdiansyah, Muhammad Reynaldi Fil'Awalin, Hanif Firdaus Firdaus Firmansyah, Gustian Angga Ghaly, Raihan Athallah Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Habibi, Alif Farhan Hadi, Heru Pramono Hafsah, Nur Handar Subhandi Bakhtiar Handayani, Ni Made Kirei Kharisma Hanisa, Najma Syakina Hardiana Widyastuti Harisa, Ardiawan Bagus Hariyadi, Nursyifa Nada Hartono, Andhika Rhaifahrizal Harun Al Azies Hasibuan, Ahmad Daniel Hayatul Cholsy Helda Orbani Rosa Hendriansah Hendry . Hermansyah Hidayat, Novianto Nur Hilary Reinhart Husaini Husda IDA RAHMAWATI Ifan Rizqa Ika Wiani Ilma, Novita Indra Refipal Sembiring Indrawan, Michael Inggih Permana Irawan, Diki Surya Irlianda, Rahma Irma Rahmawati Ismail, Mahesa Isnaini, Lisa Fajri ISWAHYUDI ISWAHYUDI Jazman, Muhammad Jun Justinar K, M. Rizal Kharisma, Ni Made Kirei Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Anggy Tyas Kurniawan, Defri Kusumawati, Yupie Labib, M. Ainul Ledy Diana Listya Endang Artiani, Listya Endang Liya Umaroh, Liya Lusi Ariyani M Widyastuti Madani, Hanif Al Malahayatie Mallorca, Muhammad Nur Alfat Margaretha Widyastuti Marsuni, Yusriadi Marwan Marwan, Arif Masliana Masliana, Masliana Maulana, Isa Iant Megantara, Rama Aria Miftahul Jannah Moch Anjas Aprihartha Moch. Solikin, Moch. Mohammad Arif Mufadhdhal, Muhammad Asad Muhadzid, Fayyadh Atsil Muhammad Asyroful Mujib Muhammad Aziz Muhammad Fachrurrozi Bafadal Muhammad fauzan Muhammad Yazid MUKAROMAH MUKAROMAH Muljono Muljono Mursanto, Mursanto Muslem, Asnawi Muslih Muslih Mylova, Rezha Willy Putra Nabila, Hilma Agisna Nasichah Nazella, Desvita Dian Ningrum, Novita Kurnia Noor Ageng Setiyanto, Noor Ageng Norma Eralita Noverial Nugraini, Siti Hadiati Nurhayati Nurhayati Nurjaman, Satya Pirman Nursantri Yanti Oktafani, Reni oktavia, sanni Oktaviani, Dilla Onggo, Nico Pamungkas, Bintang Putra Dwi Prabowo, Wahyu Aji Eko Prafinda Ababil, Putri Prasetyo Adi, Muhammad Wahyu Pratama, Martha Putri Pratiwi, Eghinna Elsa Prayoga, Yegi Pulung Nurtantio Andono Puspita, Rahayuning Febriyanti Putra, Permana Langgeng Wicaksono Ellwid Putri Rachmadani, Nisa Putri, Biomechy Oktomalio Putri, Popy Dwi Rafid, Muhammad Rafly, Muhammad Naufal Rahayu, Linda Zulkifa Rahma, Anne Rahmah, Mulia Rajudin, Rajudin Ramadhan Rakhmat Sani Ramadhan, Fajri Ramadhani, Nasywa Anjani Rani Maharani Respati, Adnasohn Aqilla Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Richo Kurniawan, Ibnu RISAL, SYAHLAN Riswandi, Muh Syahlan Riyanto, Indra Agus Ruri Suko Basuki Ryazi, Inggit Dzulka Safitri, Aprilyani Nur Safitriani, Indah Sagita, Adilah Nisrina Ayu Samangi, Arfina Sari Ayu Wulandari Sarifah Putri Raflesia, Sarifah Putri Sariman, Syahrul Satyawira, Ryan Sa’adah, Muna Sembiring, Theresia Aprilia Br Sepe, Muslimin Silalahi, Purnama Ramadani Silvester Dian Handy Permana, Silvester Dian Handy Siregar, Karina Josephine Siti Anisah Siti Nurmaini Situmeang, Zefanya Angelica Sofiani, Hilda Ayu Sri Sentanu, I Gede Eko Putra Sri Winarno Sudarmin Sudibyo, Usman Suharnawi Suharnawi Suhendra Sulfanita, Andi Sumartono Sutomo, Rudi Suwandi, Agus Thoriq, Muhammad Tjahyo Nugroho Adji Tomy Perdana Trihandono, Donny Trisnapradika, Gustina Alfa Ulhaq, M. Jia Umar Fakhrizal, Irsyad Wahyu Ristiawan, Angga Welgaputri, Feby Wianda, Muhammad Nabiel Augis Wibawa, Yohanes Eka Winda, Nadia Oktavia Wulandari, Wita Wulansari, Rindi Yudhistira, M Halley Zahra, Lintang Aulia Zahro, Azzula Cerliana Zakky Zamrudi Zami, Farrikh Al Zayn, Afta Ramadhan Zulfikar Jayakusuma