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All Journal Riau Journal of Computer Science Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Komunikasi : Jurnal Komunikasi ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Sistem Informasi dan Aplikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informatika : Jurnal Informatika, Manajemen dan Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Technopreneurship and Information System (JTIS) Infotekmesin Jurnal Teknologi Informasi dan Multimedia Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Community Engagement and Emergence Journal (CEEJ) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal J-PEMAS Jurnal Ipteks Terapan : research of applied science and education pendidikan, science, teknologi, dan ekonomi Jurnal Rekam Medis (Medical Record Journal) Jurnal Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Telecommunication Control and Intelligent System Journal of Software Engineering and Information System (SEIS) SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Jurnal 7 Samudra Politeknik Pelayaran Surabaya Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Terapan Komunikasi : Jurnal Komunikasi
Yuda Irawan
Universitas Hang Tuah Pekanbaru
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An Integrated Machine Learning and Deep Learning Approach for Multiclass Flood Risk Classification with Feature Selection and Imbalanced Data Handling Irawan, Yuda; Refni Wahyuni; Herianto
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4639

Abstract

Floods are hydrometeorological disasters that often occur in tropical regions such as Indonesia and can have significant impacts on infrastructure, economy, and public health. This study aims to build and compare the performance of 21 artificial intelligence models, consisting of 15 Machine Learning algorithms and 6 Deep Learning architectures, in classifying flood risk levels based on multivariate tabular data. The dataset used includes 22 relevant environmental and social variables, with classification targets in four classes: Low, Moderate, High, and Very High. To improve data quality, feature selection was carried out using the LASSO method and class balancing with the SMOTEENN technique. The evaluation results showed that the C4.5, MLP, Random Forest, and Logistic Regression models obtained the highest accuracy (>94%), followed by deep learning models such as BiLSTM, CNN, and BiGRU with competitive accuracy (≥90%). Confusion matrix analysis confirmed the consistency of predictions across classes with a balanced distribution, especially in the decision tree and deep neural network models. This study emphasizes the importance of selecting a model that suits the characteristics of the data to achieve optimal predictions. The pipeline developed in this study is expected to be the basis for a more accurate and adaptive AI-based early warning system in mitigating flood risks in the future.
Integration of Machine Learning Models Random Forest and XGBoost for Credit Card Fraud Detection in a Python Flask-Based Application Heri, Herianto; Zupri Henra Hartomi; Rian Ordila; Yuda Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4821

Abstract

Credit card fraud is one of the major challenges in modern digital payment systems. The increasing volume of online transactions raises the potential for unauthorized use of cardholder data. This research aims to develop a robust and accurate fraud detection system by integrating two machine learning algorithms, Random Forest and XGBoost, both of which are known for their high performance in data classification. The research process begins with the collection and preprocessing of credit card transaction data, followed by model training using the selected algorithms. The model’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. To enable real-time application, the model is implemented in a web-based system using the Python Flask framework, allowing direct integration into financial transaction environments. The need for adaptive systems that can respond to emerging fraud patterns serves as a key motivation for this study. By combining two complementary algorithms within a single web application platform, the system is expected to detect fraudulent activities quickly and accurately. The expected outcomes of this research include: (1) an optimized fraud detection model based on Random Forest and XGBoost, (2) a prototype web application developed with Python Flask for system implementation, and (3) a scientific publication describing the development and results of the proposed system. The targeted outputs are a publication in a nationally accredited journal (Sinta 4) and intellectual property registration. This research is expected to provide a significant contribution to preventing credit card fraud through the effective application of machine learning technologies
Utilization of IndoBERT Representation and Random Forest for Sentiment Analysis on User Reviews of Halodoc Pharmacy Services in Google Play Hendry Fonda; Herianto; Yuda Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4822

Abstract

With the growing use of digital healthcare platforms such as Halodoc, maintaining consistent service quality that meets user expectations is essential. User reviews on platforms like Google Play provide valuable insights into user perceptions. This study aims to classify user sentiments toward Halodoc’s pharmacy services based on reviews obtained through web scraping from the Google Play Store. The analysis employs the pre-trained IndoBERT model to extract textual features, followed by sentiment classification using the Random Forest algorithm. This combination was selected for its efficiency with limited hardware resources and small dataset size. To enhance data diversity and minimize overfitting, simple augmentation methods such as random word deletion and synonym substitution were implemented. The expected outcomes include an effective sentiment classification model and visualizations of sentiment distributions (positive, negative, neutral). Furthermore, the study contributes to the development of sentiment analysis techniques for Indonesian-language data through an efficient and contextually relevant approach. The research outputs target publication in a nationally accredited (Sinta 4) journal and Intellectual Property Rights (IPR) registration. Ultimately, this study is expected to support the improvement of technology-based pharmacy services through the strategic application of machine learning.
COMPARISON OF DEEP LEARNING MODELS LSTM AND BILSTM IN DIABETES PREDICTION: COMPARISON OF DEEP LEARNING MODELS LSTM AND BILSTM IN DIABETES PREDICTION Wahyuni, Refni; Irawan, Yuda
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4904

Abstract

Diabetes mellitus remains a major global health concern, requiring early detection to prevent severe complications and reduce mortality. This study developed and evaluated two deep learning architectures, Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for diabetes prediction using the Pima Indians Diabetes Dataset. The research methodology involved systematic preprocessing, including outlier handling with median imputation, data normalization, and training–testing data splitting (80:20). Both models were trained using 614 samples for training and 154 samples for testing, with 50 epochs and a batch size of 32. The evaluation was performed using accuracy, precision, recall, F1-score, and AUC metrics. Results indicated that LSTM achieved an accuracy of 74.03%, while BiLSTM slightly outperformed it with 74.68%. Confusion matrix analysis further revealed that BiLSTM reduced false negatives and provided more consistent learning stability compared to LSTM. Accuracy and loss curves confirmed BiLSTM’s superior convergence and generalization capability. These findings demonstrate that BiLSTM is more effective and reliable for diabetes prediction tasks. The study concludes that BiLSTM offers better potential for integration into decision-support systems, and future research could enhance performance through larger datasets, advanced optimization, and real-world clinical validation.
Empowering Students through Health Literacy: A School-Based Community Learning Innovation in Secondary Education Emy Leonita; Yuda Irawan; Nopriadi Nopriadi; Oktavia Dewi
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 2 (2025): JUNE 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i2.7638

Abstract

Adolescents are increasingly at risk of preventable health issues due to unhealthy lifestyle behaviors and limited access to contextualized health education. Addressing this gap, the study aimed to develop and evaluate a School-Based Community Learning Model that enhances health literacy and student engagement through participatory, project-based learning. The intervention was based on contextual teaching, social constructivism, and experiential learning principles. It involved digital health modules, peer discussions, and community action projects delivered over six weeks to 120 senior high school students in Pekanbaru, Indonesia. A quasi-experimental pretest-posttest control group design was employed to assess changes in health literacy and life skills. Statistical analysis showed a significant improvement in both health literacy and life skills in the intervention group (p 0.001). The average N-Gain score was 0.62, indicating moderate-to-high learning gains. Qualitative observations also indicated enhanced collaboration, critical thinking, and student initiative—markers of affective and psychomotor development. These findings suggest that the model effectively fosters essential health competencies and supports holistic student development. By aligning with national education goals, the program provides a viable framework for integrating health education into the standard curriculum. The School-Based Community Learning Model presents a scalable and practical approach to embedding health literacy in secondary education. It reinforces the role of health education as a fundamental aspect of character building and life skills development in schools.
Regulasi dan Kreativitas dalam Penyiaran Lokal: Studi Kasus Kebijakan Komisi Penyiaran Indonesia Daerah (KPID) Riau Abdullah Mitrin; Rudi Rahman; Lia Anisa; Yuda Irawan
Jurnal Komunikasi Vol. 16 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jkom.v16i1.12091

Abstract

Penelitian ini bertujuan mengevaluasi dampak kebijakan KPID Riau terhadap kreativitas dalam produksi siaran lokal, serta mengkaji tantangan dan peluang dalam menyeimbangkan regulasi dan inovasi. Pendekatan yang digunakan adalah kualitatif dengan metode studi kasus, dengan pengumpulan data melalui wawancara mendalam, observasi langsung, serta analisis dokumen kebijakan dan laporan tahunan. Analisis dilakukan secara tematik dengan teknik triangulasi data untuk menjaga validitas. Hasil penelitian menunjukkan bahwa regulasi KPID Riau berhasil menjaga standar etika dan melindungi nilai-nilai budaya lokal. Namun, regulasi yang ketat juga dinilai membatasi ruang inovasi lembaga penyiaran, mengakibatkan berkurangnya variasi program. Beberapa stasiun penyiaran yang mendapat ruang lebih fleksibel terbukti mampu meningkatkan daya tarik konten dan apresiasi publik. Penelitian ini merekomendasikan penerapan kebijakan yang lebih adaptif dan kolaboratif, melibatkan pelaku media dan akademisi dalam proses perumusan kebijakan. Dengan kebijakan yang responsif terhadap perkembangan teknologi dan kebutuhan masyarakat, penyiaran lokal di Riau diharapkan mampu tetap inovatif tanpa meninggalkan nilai-nilai budaya sebagai fondasi utama siaran publik
INTEGRASI SENSOR IOT DAN OPTIMASI ALGORITMA MACHINE LEARNING UNTUK DETEKSI REAL-TIME TINGKAT STRES MAHASISWA Andrianto, Richi; Lubis, Mustopa Husein; Irawan, Rina; Irawan, Yuda; Utami, Urfi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5178

Abstract

Abstract: High levels of stress among university students are a critical issue that can affect mental health, well-being, and academic performance. This study aims to develop a real-time student stress detection system using physiological data integrated with IoT technology and machine learning algorithms. The data used includes body temperature, blood oxygen saturation (SpO₂), heart rate, and blood pressure, acquired via embedded sensors and automatically transmitted to the cloud. The classification model was built using a combination of Random Forest and XGBoost, with enhanced accuracy through SMOTE-based data balancing and hyperparameter optimization using Optuna. The system was tested on a dataset of 3,420 records, classified into four stress levels: anxious, calm, tense, and relaxed. Evaluation results showed that the Random Forest model achieved the highest accuracy of 91%, followed by RF + XGBoost and RF + XGBoost + Optuna with accuracies of 90% each. The final model was deployed in a user interface using Streamlit, allowing real-time stress classification from IoT sensor input and manual input testing. The system proved to be effective and responsive in detecting stress objectively and can support digital-based mental health monitoring and counseling services for students. Keywords: Stress detection, IoT, Machine Learning, Random Forest, XGBoost Abstrak: Tingkat stres yang tinggi di kalangan mahasiswa merupakan permasalahan serius yang dapat memengaruhi kesehatan mental, kesejahteraan, dan performa akademik. Penelitian ini bertujuan untuk mengembangkan sistem deteksi tingkat stres mahasiswa secara real-time menggunakan data fisiologis berbasis teknologi IoT dan algoritma machine learning. Data yang digunakan meliputi suhu tubuh, kadar oksigen dalam darah (SpO₂), detak jantung, dan tekanan darah yang diperoleh melalui sensor terintegrasi dan dikirim ke cloud secara otomatis. Model klasifikasi yang dikembangkan memanfaatkan kombinasi algoritma Random Forest dan XGBoost, dengan peningkatan akurasi melalui teknik balancing data menggunakan SMOTE dan optimasi hyperparameter otomatis menggunakan Optuna. Sistem diuji menggunakan dataset berjumlah 3.420 data dengan distribusi empat kelas stres: cemas, tenang, tegang, dan rileks. Hasil evaluasi menunjukkan bahwa model Random Forest menghasilkan akurasi tertinggi sebesar 91%, disusul oleh RF + XGBoost dan RF + XGBoost + Optuna dengan akurasi masing-masing sebesar 90%. Model akhir kemudian diintegrasikan ke dalam antarmuka pengguna berbasis Streamlit, yang memungkinkan klasifikasi stres secara real-time dari data sensor IoT dan juga melalui input manual. Sistem ini terbukti efektif dan responsif dalam mendeteksi stres secara objektif dan dapat digunakan untuk mendukung layanan konseling atau pemantauan kesehatan mental mahasiswa secara digital. Kata kunci: Deteksi stres, IoT, Machine Learning, Random Forest, XGBoost
PKM Upaya Peningkatan Kesehatan Lansia dan Manajemen Administrasi Berbasis Teknologi Informasi Pada Bina Keluarga Lansia (BKL) Kampung KB Mekar Wangi Pekanbaru Devis, Yesica; Irawan, Yuda; Hamid, Abdurrahman
Community Engagement and Emergence Journal (CEEJ) Vol. 4 No. 2 (2023): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v4i2.3220

Abstract

Bina Keluarga Lansia (BKL) Kampung KB Mekar Wangi Pekanbaru merupakan suatu kegiatan yang dilakukan secara berkelompok dan bertujuan untuk meningkatkan jangkauan pelayanan kesehatan lansia di masyarakat sehingga terbentuk pelayanan kesehatan yang sesuai dengan kebutuhan lansia di Kampung KB Mekar Wangi Kelurahan Tangkerang Barat Pekanbaru yang menjadi mitra dalam kegiatan PKM ini. Salah satu permasalahan yang sangat mendasar pada lansia adalah masalah kesehatan, sehingga diperlukan upaya untuk meningkatkan kesehatan kelompok lansia. Kader pada BKL Mekar Wangi berjumlah 22 orang dan memiliki pendidikan rata-rata SMA kebawah. Adapun 3 permasalahan prioritas yang dihadapi oleh mitra yaitu: 1) Minimnya pengetahuan kader tentang kesehatan lansia; 2) Pencatatan administrasi BKL masih dilakukan secara manual; dan 3) Minimnya peralatan kesehatan bagi BKL. Tujuan dari kegiatan PKM ini adalah untuk menyelesaikan permasalahan pada mitra Bina Keluarga Lansia Kampung KB Mekar Wangi yaitu upaya meningkatkan kesehatan lansia melalui pelatihan dan penyuluhan kepada kader lansia, penyuluhan kepada lansia, pengadaan peralatan pendukung kesehatan, dan perancangan sistem informasi untuk manajemen administrasi secara terkomputerisasi. Beberapa kegiatan yang telah selesai dilaksanakan pada kegiatan pengabdian yaitu perancangan aplikasi bina keluarga lansia, penyuluhan peningkatan pengetahuan para kader bina keluarga lansia, pelatihan penggunaan peralatan Kesehatan lansia, dan penyuluhan pentingnya posyandu lansia. Hasil dari evaluasi kegiatan menunjukkan bahwa tercapainya peningkatan kesehatan mitra, meningkatnya pelayanan mitra, dan meningkatnya pengetahuan mitra.
PKM Implementasi Teknologi Internet of Things (IoT) Dalam Monitoring Ketinggian Permukaan Air Sungai dan Aplikasi Tanggap Darurat Sebagai Upaya Mitigasi Bencana Banjir di Kota Pekanbaru Irawan, Yuda; Saputra, Haris Tri; Renaldi, Reno
Community Engagement and Emergence Journal (CEEJ) Vol. 4 No. 2 (2023): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v4i2.3221

Abstract

Daerah Kota Pekanbaru memiliki iklim tropis dan curah hujan yang cukup tinggi setiap tahunnya. Keadaan curah hujan yang tinggi mengakibatkan daerah tertentu di Pekanbaru mengalami banjir terutama kawasan di pinggiran sungai siak yang disebabkan melimpahnya sungai siak. Kurangnya informasi yang cepat tentang ketinggian air sungai siak menyebabkan keterlambatan peringatan dan proses evakuasi oleh BPBD Kota Pekanbaru. Akibat keterlambatan pengevakuasian dapat berdampak hanyutnya harta benda ataupun nyawa manusia. Selain itu seringnya terjadi keterlambatan pelaporan saat bencana banjir oleh warga ke BPBD untuk evakuasi disebabkan oleh belum adanya media pelaporan berbasis IT yang terintegrasi sehingga terkadang proses pelaporan dan tindak lanjut sering mengalami keterlambatan. Permasalahan lainnya adalah kepedulian masyarakat terhadap lingkungan, salah satunya yaitu pembuangan sampah di saluran pembuangan air dan sungai. Banyak nya tumpukan sampah disungai dan saluran air menyebabkan tidak lancarnya aliran air sehingga menyebabkan banjir. Kemudian ketidak pedulian masyarakat terhadap tebang pilih pohon dan reboisasi, pembuatan sumur resapan, dan sikap tidak peduli terhadap kebersihan lingkungan. Pembuangan sampah sembarangan juga menyebabkan terciptanya lingkungan yang tidak sehat sehingga dapat menghambat tercapainya kemandirian kesehatan. Berdasarkan 3 permasalahan prioritas tersebut maka tim pengusul beserta mahasiswa melaksanakan kegiatan PKM dengan tujuan mitigasi atau pengurangan resiko yang ditimbulkan akibat terjadinya bencana banjir di Kota Pekanbaru. Adapun upaya yang telah dilakukan adalah pembuatan alat monitoring pengukur ketinggian air sungai berbasis IoT sebagai Teknologi Tepat Guna yang merupakan penerapan hasil penelitian dari tim pengusul dan pembuatan aplikasi tanggap darurat bencana banjir. Selain itu kegiatan ini juga terfokus ke bidang kesehatan yaitu melaksanakan kegiatan penyuluhan sikap kepedulian lingkungan kepada warga terutama warga didaerah sekitar pinggiran sungai di Jl. Sumber Sari, Pekanbaru.
Perancangan Aplikasi Pembayaran SPP DI SDIT Bustanul Ulum Pekanbaru Wahyuni, Refni; Irawan, Yuda; Muhaimin, Abdi
Community Engagement and Emergence Journal (CEEJ) Vol. 4 No. 2 (2023): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v4i2.3299

Abstract

Pembayaran Sumbangan Pembinaan Pendidikan merupakan sumbangan yang wajib dibayarkan oleh siswa/siswi untuk memperlancar kegiatan belajar mengajar di Sekolah. Pembayaran SPP biasanya dibayarkan setiap bulannya oleh siswa dengan nominal sesuai dengan aturan sekolah tersebut (Heriyanto & Oktavianis, 2019) SDIT Bustanul Ulum Pekanbaru merupakan sekolah dasar swasta yang terletak di jalan Hang Tuah Ujung, Sail, Pekanbaru, untuk memperlancar kegiatan pendidikan, siswa/siswi di SDIT Bustanul Ulum juga diwajibkan membayar iuran SPP setiap bulannya. Pembayaran SPP di SD IT Bustanul Ulum Pekanbaru dilakukan dengan cara, siswa/siswa atau orang tua datang langsung ke Sekolah, Setelah itu bagian keuangan mencatat ke buku besar sebagai bukti pembayaran yang dipegang oleh pihak sekolah dan mencatat di kwitasni sebagai bukti pembayaran yang diterima oleh siswa/ wali murid. Dalam pembuatan laporan pembayaran, keuangan harus mencatat data pembayaran di buku besar dan menghitung besar penerimaan setiap bulannya, untuk pembuatan laporan pun dikerjakan secara manual dengan mengatur dan mendesain sendiri bentuk laporannya. Dalam penyelesaian masalah tersebut maka tim mengusulkan tentang perancangan aplikasi pembayaran SPP. Dengan sistem ini jauh akan mempermudah bagian keuangan dalam melayani pembayaran SPP yang dilakukan siswa/siswi atau orang tua wali murid. Dan mempermubah bagian keuangan dalam membuat laporan yang dibutuhkan. Permasalahan yang terjadi dengan kegiatan tersbut adalah kurang efektif dan efisiennya pembayaran SPP. Dari permasalahn tersebut maka akan diberikan solusi yaitu perancangan aplikasi pembayaran SPP. Tujuan dari penerapan  aplikasi ini bisa mempermudah bagian keuangan dalam melayani pembayaran SPP
Co-Authors -, Herianto A.A. Ketut Agung Cahyawan W Abdullah Mitrin Achmad Deddy Kurniawan Achmad Nizar Hidayanto Adhitya, Ryan Yudha Aditya Rickyta Adyanata Lubis Afresi Yunita Agnita Utami Agus Alamsyah Ahmad Fauzan Azim Akbar, Amri Akhmad Zulkifli Aldiga Rienarti Abidin Anam, M Khairul Andre Wahyu Novrianto Anisa, Lia Anita Febriani Aprilia, Ulfa Areta Sonya Rahajeng Arfianto, Afif Zuhri Arnawilis Arnawilis Arnawilis Bakhrizal Bambang Kurniawan Bayu Saputra Budy Mustika Debi Setiawan, Debi Desi Rahmawati Devis, Yesica Dhea Arina Ramadhini Dhini Septhya Diandra, Roni Edriyansyah Eka Sabna Elisawati, Elisawati Fachry Abda El Rahman Fatmawati, Kiki Fitri, Imelda Fonda, Hendry Gilang Citra Lenardo Habib Yuhandri, Muhammad Hadi Asnal, Hadi Hafizh Sallam Hamdani Hamdani Hamid, Abdurrahman Hartomi, Zupri Henra Hasnor Khotimah Hayami, Regiolina Hendro Agus Widodo, Hendro Agus heri, Herianto Herianto Herianto Herianto Herianto - Herianto Herianto Herianto Herianto Hidayati Kurnia Fitri Hohashi, Naohiro Irawan, Rina Irwanda Syahputra Jamaris, Muhamad Jenli Susilo Jenni Oinike Br Sitorus Jepisah, Doni Jeri Trio Sentana Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Khairunisa Khairunisa Khairunisa, Khairunisa Kharisma Rahayu Kurniawan, Bambang Leonita, Emy Lia Anisa Lubis, Mustopa Husein Lucky Lhaura Van FC, Lucky Lhaura Mardainis Mardeni Mardeni Mardeni, Mardeni Matthijs B Punt Maulita Yulia Sari Mbunwe Muncho Josephine Mbunwe Muncho Josephine Melyanti, Rika Mitrin, Abdullah Mohd Rinaldi Amartha Muhaimin, Abdi Muhamadiah, Muhamadiah Muhammad Bambang Firdaus Muhardi Muhardi - Muhardi Muhardi Muhardi Muhardi Mulya Rispani Mutiara Sari, Ria Naima Belarbi Naima Belarbi Nella Sari Nico Chandra Nopriadi Noratama Putri, Ramalia Nurhadi Nurhazimah Rafiah Octaria, Haryani Oktavia Dewi Ordila, Rian Perkasa, Reza Prihandoko, P Purnomo, Nopi Purwanti, Siti Putra Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmalisa, Uci Rahman, Rudi Refni Wahyuni Renaldi, Reno renaldi, reno Reza Perkasa Rian Ordila Rian Ordila Riananda, Dimas Pristovani Richi Andrianto Rickyta, Aditya Rofiqoh, Ummi Rometdo Muzawi, Rometdo Roni Diandra Rudi Rahman Ruwahida, Dewi Rizani Ruwahida Sabna, Eka Sakroni Indra Gunawan Salsabila Rabbani Saputra, Haris Tri Sarjon Defit Sentana, Jeri Trio Siti Aisyah Siti Aisyah Siti Purwanti Sugiati Suherman Sohor Suherman Suherman Suriandi Suriandi Susanti, Susanti Susi Oustria Simamora Susilo, Jenli Syamsul Arifin Uci Rahmalisa Ulfa Aprilia Utami, Urfi Vindi Fitria Winda Herrianti Manullang Winda Sari Wulan Sari Yesica Devis Yuhandri, Y Yulanda Yulanda Yulanda Yulanda, Yulanda YULISMAN Yulisman, Yulisman Yunior Fernando Zufari, Faisal Zufi Pratama Noviardi Zupri Henra Hartomi