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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences TEKNIK INFORMATIKA Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Fountain of Informatics Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo ILKOM Jurnal Ilmiah Voice Of Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JURTEKSI ComTech: Computer, Mathematics and Engineering Applications CSRID (Computer Science Research and Its Development Journal) JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Sistem Informasi dan Informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Sains, Aplikasi, Komputasi dan Teknologi Informasi JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems JSES : Journal of Sport and Exercise Science Aiti: Jurnal Teknologi Informasi Jurnal Sistem Informasi dan Sistem Komputer Journal of Applied Data Sciences Jurnal J-PEMAS Decode: Jurnal Pendidikan Teknologi Informasi Ikhtisar: Jurnal Pengetahuan Islam Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Sisfo: Jurnal Ilmiah Sistem Informasi Formosa Journal of Science and Technology (FJST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Masyarakat Madani Indonesia SATIN - Sains dan Teknologi Informasi Bulletin of Social Informatics Theory and Application Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) The Indonesian Journal of Computer Science Journal of Informatics and Information Security Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Health Research Innovation
Anam, M Khairul
Universitas Samudra
Religion Aerospace Engineering Humanities 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 Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Edukasi PHBS Anak Pascabanjir Melalui Pemanfaatan Hygiene Kit di Desa Tualang Baro Kabupaten Aceh Tamiang Tjut Rizqi Maysyarah Hadi; Michel Kasaf; Alfisyahrin Alfisyahrin; M. Khairul Anam; Febrina Dian Kurniasari
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 3 (2026): Agustus
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/9m9rzs32

Abstract

Banjir yang terjadi di Desa Tualang Baro, Kabupaten Aceh Tamiang pada 25 November 2025 memberikan dampak signifikan terhadap kondisi kesehatan masyarakat, khususnya anak-anak sebagai kelompok rentan. Kondisi pascabanjir menyebabkan penurunan kualitas sanitasi lingkungan yang berpotensi meningkatkan risiko penyakit berbasis lingkungan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan anak dalam menerapkan Perilaku Hidup Bersih dan Sehat (PHBS) melalui pendekatan edukatif, partisipatif, dan aplikatif. Metode pelaksanaan meliputi tahapan sosialisasi, edukasi PHBS, distribusi dan demonstrasi penggunaan hygiene kit, pendampingan serta evaluasi, dan keberlanjutan program. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan anak dalam menjaga kebersihan diri, seperti mencuci tangan, menyikat gigi, dan menjaga kebersihan tubuh. Pendekatan interaktif melalui permainan dan praktik langsung terbukti efektif dalam meningkatkan partisipasi dan daya serap anak. Selain itu, distribusi hygiene kit sebagai sarana pendukung turut memperkuat perubahan perilaku yang lebih konsisten. Dengan demikian, integrasi antara edukasi PHBS dan penyediaan fasilitas kebersihan menjadi strategi yang efektif dalam meningkatkan derajat kesehatan anak pada kondisi pascabanjir.
Tourist Satisfaction Analysis of East Aceh Coastal Ecotourism Using Stacking Machine Learning and GUI Integration Imam Gunanjar; Ryanda Saputra; Shary Armonitha Lusiana; Martahadi Mardhani; M. Khairul Anam
International Journal of Advances in Data and Information Systems Vol. 7 No. 2 (2026): August 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i2.1614

Abstract

Tourist satisfaction is a key indicator for evaluating service quality and improving destination management in coastal ecotourism. East Aceh has several coastal tourism destinations with strong ecotourism potential, but tourist opinions are not always systematically analyzed for service improvement. This study analyzes tourist satisfaction in East Aceh coastal ecotourism using a stacking machine learning approach and integrates the best-performing model into a Graphical User Interface (GUI). The dataset consists of 1,679 tourist opinions collected from several coastal tourism objects in East Aceh and labeled into three classes: Satisfied, Neutral, and Dissatisfied. To prevent data leakage, the dataset was first divided into training and testing sets using a 60:40 split; TF-IDF fitting and SMOTE resampling were then applied only to the training data, while the testing set was kept unchanged. The evaluated base models include Naive Bayes, Random Forest, Support Vector Machine, and XGBoost, with Logistic Regression used as the stacking meta-model. The results show that the stacking model with SMOTE achieved the best performance with 87.05% accuracy, 87% precision, 87% recall, and 87% F1-score, outperforming the individual models in this experimental setting. However, the improvement over stacking without SMOTE was modest and was not statistically tested. The best-performing model was implemented in a GUI-based prototype that demonstrates how tourist opinions can be uploaded and classified into satisfaction categories. The prototype provides a preliminary interface for presenting classification results and has not yet been validated as an operational decision-support system.
The Application of Na ve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media Ryanda Satria Putra; Wirta Agustin; M. Khairul Anam; Lusiana Lusiana; Saleh Yaakub
Jurnal Transformatika Vol. 20 No. 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.5144

Abstract

There are problems that still exist in online learning including limited-reach networks, inadequate facilities and infrastructure, and others. This study discussed the analysis of sentiment which used the Na ve Bayes Classifier (NBC) method with XGBoost feature selection as a performance improvement that took data from news portals. The results of this study showed that graph data on the application of online learning forms in Indonesia had a "Negative" opinion. Performance testing of the NBC method based on XGBoost feature selection was conducted four times. The first experiment resulted in an accuracy value of 60.18% with 50/50 split data. The next experiment had an accuracy value of 56.92% with 70/30 split data. After that, the third experiment resulted in an accuracy value of 65.90% with 80/20 split data. The result of the last experiment was an accuracy value of 63.63% with 90/10 split data. After using XGBoost feature selection, it produced an accuracy of 60.18%, 67.69%, 70.45%, and 77.27%. The study also produced the highest average score at 10-Fold Cross-Validation in the second trial with a score of 65.62%.
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Tri Putri Lestari; Lusiana Efrizoni; Nadya Satya Handayani; Imam Andhika
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
MYCD: Integration of YOLO-CNN and DenseNet for Real-Time Road Damage Detection Based on Field Images Helda Yenni; Rometdo Muzawi; Karpen Karpen; M. Khairul Anam; Michel Kasaf; Tjut Rizqi Maysyarah Hadi; Dewi Sari Wahyuni
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1040

Abstract

Road damage such as cracks, potholes, and uneven surfaces poses serious risks to transportation safety, logistics efficiency, and maintenance budgeting in Indonesia. Manual inspection is time consuming, labor intensive, and prone to error, motivating the use of reliable computer vision solutions. This study proposes MYCD, a hybrid and mobile ready architecture that combines the fast detection ability of YOLO with the dense feature reuse of DenseNet, enhanced by the Convolutional Block Attention Module (CBAM) for spatial and channel focus and Spatial Pyramid Pooling (SPP) for multi scale context understanding. The system detects and classifies the severity of road damage into minor, moderate, and severe categories using images captured by standard cameras. MYCD was trained and validated on 1,120 field images using an 80/20 split to simulate realistic deployment. Validation achieved 64 percent accuracy, with the highest per class precision of 0.72 for minor damage and mAP@0.5 = 0.677. The confusion matrix showed that most errors occurred in the moderate category because of visual similarity with minor and severe damage. Unlike earlier studies that extended YOLO with heavy backbones such as ResNet or EfficientNet, MYCD focuses on feature propagation (DenseNet), attention precision (CBAM), and multi scale fusion (SPP) optimized for real time operation on standard hardware. Efficiency profiling confirmed its deployability. After compression, the model size is 46.8 MB and it requires 3.7 GFLOPs per inference at 640×640 resolution. On a mid-range Android device (Snapdragon 778G, 8 GB RAM), MYCD runs at 19 frames per second with 1.2 GB peak memory. Compared with YOLOv8 WD (68 MB; 5.2 GFLOPs), MYCD reduces computation by 31 percent while maintaining similar accuracy. Overall, MYCD achieves a practical balance of speed, accuracy, and efficiency, providing a deployable and reproducible framework for real time road damage detection in resource limited settings.
Enhancing the Performance of Machine Learning Algorithm for Intent Sentiment Analysis on Village Fund Topic M. Khairul Anam; Pandu Pratama Putra; Rio Andika Malik; Karfindo Karfindo; Teri Ade Putra; Yesri Elva; Raja Ayu Mahessya; Muhammad Bambang Firdaus; Ikhsan Ikhsan; Chichi Rizka Gunawan
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.637

Abstract

This study explores the implementation of Intent Sentiment Analysis on Twitter data related to the Village Fund program, leveraging Multinomial Naïve Bayes (MNB) and enhancing it with Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost (XGB). The analysis categorizes tweets into six labels: Optimistic, Pessimistic, Advice, Satire, Appreciation, and No Intent. Initially, the MNB model achieved an accuracy of 67% on a 90:10 data split. By applying SMOTE, accuracy improved by 12%, reaching 89%. However, adding Chi-Square feature selection did not increase accuracy further. Incorporating XGB into the MNB+SMOTE model led to a 6% improvement, achieving a final accuracy of 95%. Comprehensive model evaluation revealed that the MNB+SMOTE+XGB model achieved 96% accuracy, 96% precision, 96% recall, and a 96% F1-score, with an AUC of 99%, categorizing it as excellent. These findings demonstrate that the combination of SMOTE for addressing class imbalance and XGBoost for boosting performance significantly enhances the MNB model's classification capabilities. The novelty lies in the integration of these techniques to improve intent sentiment classification for public opinion analysis on the Village Fund program. The results indicate that the majority of tweets labeled as "No Intent" reflect a lack of specific sentiment or actionable intent, providing valuable insights into public perception of the program.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset M. Khairul Anam; Munawir Munawir; Lusiana Efrizoni; Nurul Fadillah; Wirta Agustin; Irwanda Syahputra; Tri Putri Lestari; Muhammad Bambang Firdaus; Lathifah Lathifah; Atalya Kurnia Sari
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction Herianto Herianto; Bambang Kurniawan; Zupri Henra Hartomi; Yuda Irawan; M Khairul Anam
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.316

Abstract

Graduating on time is crucial for academic success, impacting time, costs, and education quality. Hang Tuah University Pekanbaru (UHTP) is currently struggling to meet its goal of achieving a 75% on-time graduation rate. This study introduces an innovative approach using machine learning techniques, particularly ensemble learning with Stacking Machine Learning Optuna SMOTE (SMLOS), to address this issue. Our primary objective is to enhance data classification accuracy to predict student graduation timelines effectively. We employ algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (C4.5), Random Forest (RF), and Naive Bayes (NB). These were combined with meta-models, including Logistic Regression (LR), Adaboost, XGBoost, LR+Adaboost, and LR+XGBoost, to create a robust prediction model. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and utilized Optuna for hyperparameter tuning. The findings reveal that SMLOS with the Adaboost meta-model achieved the highest accuracy of 95.50%, surpassing previous models' performances, which averaged around 85%. This contribution demonstrates the effectiveness of using SMOTE for class imbalance and Optuna for hyperparameter optimization. Integrating this model into UHTP's academic information system facilitates real-time monitoring and analysis of student data, offering a novel solution for promoting a Smart Campus through more accurate student performance predictions. This technique is not only beneficial for predicting student graduation but can also be applied to various machine learning tasks to improve data classification accuracy and stability.
Early Stopping on CNN-LSTM Development to Improve Classification Performance M. Khairul Anam; Sarjon Defit; Haviluddin Haviluddin; Lusiana Efrizoni; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.312

Abstract

Currently, CNN-LSTM has been widely developed through changes in its architecture and other modifications to improve the performance of this hybrid model. However, some studies pay less attention to overfitting, even though overfitting must be prevented as it can provide good accuracy initially but leads to classification errors when new data is added. Therefore, extra prevention measures are necessary to avoid overfitting. This research uses dropout with early stopping to prevent overfitting. The dataset used for testing is sourced from Twitter; this research also develops architectures using activation functions within each architecture. The developed architecture consists of CNN, MaxPooling1D, Dropout, LSTM, Dense, Dropout, Dense, and SoftMax as the output. Architecture A uses default activations such as ReLU for CNN and Tanh for LSTM. In Architecture B, all activations are replaced by Tanh, and in Architecture C, they are entirely replaced by ReLU. This research also performed hyperparameter tuning such as the number of layers, batch size, and learning rate. This study found that dropout and early stopping can increase accuracy to 85% and prevent overfitting. The best architecture entirely uses ReLU activation as it demonstrates advantages in computational efficiency, convergence speed, the ability to capture relevant patterns, and resistance to noise.
ACLM Model: A CNN-LSTM and Machine Learning Approach for Analyzing Tourist Satisfaction to Improve Priority Tourism Services Ulya Ilhami Arsyah; Mutiana Pratiwi; Harfeby Fryonanda; M. Khairul Anam; Munawir Munawir
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.974

Abstract

Tourist satisfaction is a key proxy for destination service quality, yet automatic sentiment analysis of online reviews still faces class imbalance, overfitting, and limited deployability. This study proposes ACLM, a hybrid sentiment classification pipeline that learns semantic and temporal features with a CNN-LSTM backbone and evaluates three classifier heads (Softmax, Logistic Regression, XGBoost) on a three-class corpus (neutral, satisfied, dissatisfied). The objective is to deliver an accurate and operational model for decision support in tourism services. The idea combines Word2Vec embeddings, a compact CNN for local patterns, an LSTM for sequence dependencies, and a training workflow with text cleaning, SMOTE based balancing, and regularization to curb overfitting; outputs are exposed through a simple Streamlit interface. Results show that CNN-LSTM with a Softmax head attains accuracy 0.89, macro precision 0.89, macro recall 0.84, and macro F1 0.86, outperforming Logistic Regression (accuracy 0.87, macro precision 0.84, macro recall 0.82, macro F1 0.82) and XGBoost (accuracy 0.85, macro precision 0.80, macro recall 0.82, macro F1 0.80). The findings indicate that deep sequence features paired with a simple Softmax head provide the best tradeoff between accuracy and stability for three-way sentiment classification. The contribution is a reusable, end to end blueprint from preprocessing and balanced training to quantitative evaluation and an inference GUI, and the novelty lies in testing interchangeable classifier heads on a single CNN-LSTM feature extractor while explicitly addressing data imbalance and deployment constraints. The GUI is implemented using the highest accuracy model, namely CNN-LSTM with Softmax.
Co-Authors -, Tashid Abd Kadir Mahamad Abrar Hadi Ade Riyanda Putra Adi Surya Darma Agustin Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Agusviyanda Ahmad Ihsan Ahmad Tantoni Ahmad Zamsuri Ahmad Zamsuri, Ahmad Aisum Aliyah Sari Akram, Rizalul Al Amin Fadillah Sani Alfa Saleh Alfisyahrin Alfisyahrin Alkadri Masnur Ambiyar, Ambiyar Andesa, Khusaeri Andi Supriadi Chan, Andi Supriadi Anwar, Reksi Aprillian Kartino Arba, Muhammad Hendra Arda Yunianta Arda Yunianta Arief Hidayat Arita Fitri, Triyani Arsyah, Ulya Ilhami Atalya Kurnia Sari Atalya Kurnia Sari Atmaja, Teuku Hadi Wibowo Ayu Mahessya, Raja Bambang Kurniawan Bambang Kurniawan Br.Situmorang, Elisabet Sinta Romaito Budiman, Edy Budiman, Edy Bunga Nanti Pikir Bunga Nanti Pikir Carles Chatarina Umbul Wahyuni Chichi Rizka Gunawan Cut, Banta Damar Sanggara Habibie Daryanto, Diki Dea Safitri Dedy Irfan Devi Yuliana Dewi Sari Wahyuni Dewi Sari Wahyuni Didik Sudyana Didik Sudyana Diki Daryanto Diky Daryanto Eddy Kurniawan Pradana Efrizoni, Lusiana Emerlada, Esi Tri Erlin Erlin Erlinda, Susi Ersan Fadrial, Yogi Esi Tri Emerlada Fadli Suandi Fahrul Yamani Fajar Arifandi Fajrizal Fatdha, T.Sy. Eiva Faza Alameka Febrina Dian Kurniasari Fernando Elda Pati Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Fryonanda, Harfebi Gendhy Dwi Harlyan Ginda M.A.Siregar Gubtha Mahendra Putra Gunadi Gunawan, Chichi Rizka Habibi Ulayya Hadi Asnal, Hadi Hairah, Ummul Hamdani Hamdani Hamdani - Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif Aulia Happy Yugo Prasetiya Harfeby Fryonanda Haris Kurniawan, Haris Hasan J. Alyamani Haviluddin Haviluddin Haviluddin Haviluddin Hazira, Nadila Helda Yeni Helda Yenni Helda Yenni, Helda Hendra Saputra Hendrawan, Riki hendri, nofri Herianto Herianto Herianto Herianto Herwin Herwin Herwin Herwin, Herwin Ike Yunia Pasa Ikhsan Ikhsan Ikhsan Ikhsan Imam Andhika Imam Gunanjar Indah Mukhlis Tamara Indra Prayogo Indra Prayogo Indri Febrianti Irfan Putra Pratama Irfansyah Irfansyah Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Irzal Arif Wisky Istianah Istianah Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Jetno Harja Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Karfindo, Karfindo Karpen Karpen Kartina Diah K. W. Kharisma Rahayu Khusaeri Andesa Khusaeri Andesa Kresnapati, I Nyoman Bagus Aji Kudadiri, Parlindungan Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah, Lathifah Latifah Liza Fitria Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Lusiana Lusiana Efrizoni Lusiana Efrizoni Lusiana Efrizoni Lusiana Lusiana M Syauqi Hafizh M. Ikhsan Wibowo Machdalena Mahamad, Abd Kadir Mahendra, Muhammad Ihza Mardainis Mardainis Martahadi Mardhani Martilinda Panjaitan Medi Taruk Mega Susanti Mega Susanti Melda Royani Michal Dennis Michel Kasaf Mi`rajul Rifqi Mohamad, Nur Ikhwan Bin Muhaimin, Abdi Muhamad Jamaris Muhamad Sadar Muhamad Sadar, Muhamad Muhammad Bambang F Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Budi Saputra muhammad Fuad Muhammad Ikhsan Wibowo Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Muhammad Yusuf Halim Munawir Munawir Munawir Munawir Munawir Mutiana Pratiwi N.A, Randi Nadila Rahmadhani Nadya Alinda Rahmi Nadya Satya Handayani Nanda, Novianda Nanda Nariza Wanti Wulan Sari Nasrul Sani Neci Nirwanda Nisa, Aida Nofri Hendri Nora Lizarti Novi Yona Sidratul Munti Novia Arista Nu'man Nu'man, Nu'man Nurhuda, Agus Tri Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurul Fadillah Nurul Fadillah Nurul fadillah, Nurul Nurul Indriani Nurwijayanti Osi Draini Pandu Pratama Putra, Pandu Pratama Paradila, Dinda Parlindungan Kudadiri Pradipta , Rahman Pranata, Angga Purwanto Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmi, Nadya Alinda Rahmiati Rahmiati Rahmiati Randy Permana Rebecca La Volla Nyoto Rebecca La Volla Nyoto Refni Wahyuni Reksi Anwar Rini Yanti Rini Yanti Rini Yanti Rinno Hendika Putra Rio Andika Malik Rivaldi Dwi Andhika Rohana Yola Parastika Hutasoit Rohmat Romadhoni Rometdo Muzawi Ruri Hartika Zain Ruri Hartika Zain Ryanda Saputra Ryanda Satria Putra Sabtria Winda Sari Saiful Bukhori Saiyaratul Mawaddah Saleh Yaakub Salsabila Rabbani Salsabila Rabbani Saon, Sharifah Saputra, Eko Ikhwan Sari Irma Yani Sitorus Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Sharifah Saon Shary Armonitha Lusiana Silvyana Dwi Putri Sofiansyah Fadli Sofiansyah Fadli Soni Sovia, Rini suaidah suaidah Sumijan Sumijan Susandri, Susandri Susanti Susanti Susanti Susanti Susanti Susanti Susanti, Mega Susanti, Susanti Susi Erlinda Susi Erlinda SUSI ERLINDA Syam, Salmaini Safitri Syamsiar, Syamsiar T. Sy. Eiva Fatdha Tashid Tashid Tashid Tatang Hidayat Taufik Taufik Tejawati, Andi Tengku Alvin Firdaus Teri Ade Putra Tjut Rizqi Maysyarah Hadi TM Rezaka Alfitra Torkis Nasution Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari, Tri Putri Triyani Arita Fitri Ulfah, Aniq Noviciate Wahyudianto, Mochamad Rizky Waksito, Alan Zulfikar Waskita, Ghozi Indra Wifra, Rizki Wirta Agustin Wirta Agustin Wirta Agustin Yaakub, Saleh Yansyah Saputra Wijaya Yenni, Heda Yesaya Twin Situmorang Yesri Elva Yogi Ersan Fadrial Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yudhistira, Dewangga Yumami, Eva Zainal Arifin Zeki Kurniadi zeki Kurniadi Zupri Henra Hartomi Zupri Henra Hartomi