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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Gramatika Jurnal Ilmiah KOMPUTASI JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Sebatik Journal of Information Technology and Computer Engineering Digital Zone: Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JURTEKSI INTEK: Informatika dan Teknologi Informasi Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Teknologi Informasi dan Pendidikan Jurnal Elektronika Listrik dan Teknologi Informasi Terapan bit-Tech Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) JSR : Jaringan Sistem Informasi Robotik Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Community Development Journal: Jurnal Pengabdian Masyarakat JUKI : Jurnal Komputer dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Insearch: Information System Research Journal Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Gramatika: Jurnal Penelitian Pendidikan Bahasa dan Sastra Indonesia Journal of Materials Exploration and Findings Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Innovative: Journal Of Social Science Research Jurnal Teknologi Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID Jurnal Riset Pendidikan Multidisiplin dan Pengabdian Kepada Masyarakat
Yuhandri Yuhandri, Yuhandri
UnUniversitas Putra Indonesia YPTK Padang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Optimalisasi Analisis Keamanan Menggunakan Acunetix Vulnerability Pada Rekam Medis Elektronik Tamin, Zulfiqar; Yuhandri, Y; Sumijan, S
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.494

Abstract

The use of the internet and web applications has significantly increased across various sectors, including education, healthcare, finance, and entertainment. However, web applications are highly vulnerable to various types of cyberattacks, such as SQL Injection, Cross-Site Scripting (XSS), and code injection, which can threaten the confidentiality, availability, and integrity of data. In line with technological advancements, the 2022 Ministry of Health regulation mandates that all healthcare facilities in Indonesia implement Electronic Medical Records (EMR). Universitas Andalas Hospital (RS UNAND) has adhered to this policy by developing a web-based EMR system. This study aims to evaluate and analyze the security of the EMR application used at RS UNAND. The Vulnerability Assessment process in this study was conducted using the Acunetix Web Vulnerability Scanner tool, which is designed to identify and assess vulnerabilities in web applications. The results of the first scan revealed that the RS UNAND EMR application had significant vulnerabilities, with a threat level of 3 (high). This scan identified 573 alerts, including 1 high-level, 253 medium-level, 2 low-level, and 317 informational alerts. These issues were followed by a thorough recap and further analysis to determine optimization steps. Several major vulnerabilities identified included HTML Form Without CSRF Protection, User Credentials Sent in Clear Text, Directory Listing, Source Code Disclosure, Git Repository Found, Multiple Vulnerabilities Fixed in PHP Versions, and Slow HTTP Denial of Service Attack. Optimization measures were then taken through a comprehensive review of the source code and enhancements to the security features of the EMR application. After the optimization, the second scan showed a significant reduction in the threat level, with the RS UNAND EMR application dropping to threat level 1 (low), with 12 alerts, consisting of 0 high and medium-level alerts, 9 low-level alerts, and 3 informational alerts. This study underscores the importance of regular security assessments and the optimization of security features to protect sensitive data in electronic medical record systems.
Vulnerability Testing and Analysis on Websites and Web-Based Applications in the XYZ Faculty Environment Using Acunetix Vulnerability Rahmi, Mifthahul; Yunus, Yuhandri; Sumijan, Sumijan
JITCE (Journal of Information Technology and Computer Engineering) Vol 8 No 2 (2024): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.2.83-96.2024

Abstract

The internet's continuous evolution has profoundly impacted society through the advancement of website technology and applications, reshaping contemporary ways of life. These digital platforms offer unrestricted information access, overcoming spatial and temporal limitations. In the realm of software development, Vulnerability Assessment is essential for producing high-quality products, as seemingly minor errors can create dangerous vulnerabilities that malicious actors may exploit to pilfer information from websites or applications. This study examines the security level of the Integrated website and application within the Faculty of Medicine, Universitas Andalas (Fakultas XYZ) environment, utilizing the Acunetix Web Vulnerability Scanner tool. The initial scan revealed a threat level of 3 (high) for the Fakultas XYZ website and level 2 (medium) for the Integrated application. Following a recapitulation process, several web alerts were identified for optimization, including Cross-Site Scripting (XSS), Blind SQL Injection, Application error message, HTML form without CSRF protection, Development configuration file, Directory listing, Error message on page, and User credentials sent in clear text. The optimization process involved source code review and enhancement to improve website features. A subsequent scan post-optimization demonstrated a reduction in threat levels for both the website and the UNAND FK Symphony application, with both achieving threat level 1 (low).
Enhanced U-Net Architecture for Glottis Segmentation with VGG-16 Aldi, Febri; Yuhandri, Yuhandri; Tajuddin, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3088

Abstract

Laryngeal endoscopic image analysis with segmentation techniques has great potential in detecting various diseases in the glottic area, which is essential for early diagnosis and proper treatment. This study proposes developing the U-Net architecture by integrating the VGG-16 model, aiming to improve the accuracy in detecting glottic areas. VGG-16 is applied to the encoder and bridge sections so that the model can take advantage of previously learned knowledge. This modification is expected to improve segmentation performance compared to standard U-Net, especially in handling variations in laryngeal image complexity. The dataset used consisted of 1,200 images taken randomly from the BAGLS website, a collection of laryngeal endoscopic image data rich in variation. The training results show that the standard U-Net produces an accuracy of 0.9995, IoU 0.6744, and DSC 0.7814. The improved U-Net showed a significant performance improvement, with an accuracy of 0.9998, an IoU of 0.8223, and a DSC of 0.9153. This improvement confirms that modifying the U-Net architecture using VGG-16 provides superior results in detecting glottic areas precisely. VGG-16 also helps model performance in overcoming the problem of smaller datasets. In addition, both models were tested using relevant evaluation metrics, and the test results showed that the improved U-Net consistently outperformed other CNN-based segmentation methods. These advantages show that the proposed approach improves accuracy and contributes significantly to developing glottic disease detection methods through laryngeal endoscopic image analysis, which can ultimately support clinical practice in detecting abnormalities in glottis more effectively.
Enhancing Real Time Crowd Counting Using YOLOv8 Integrated with Microservices Architecture for Dynamic Object Detection in High Density Environments Prihandoko, P; Zufari, Faisal; Yuhandri, Y; Irawan, Yuda
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.575

Abstract

This study presents the implementation of the YOLOv8 algorithm to enhance real-time crowd counting on the ngedatedotid application, which aims to provide accurate crowd density information at various locations. The proposed model leverages the advanced capabilities of YOLOv8 in detecting and localizing head-people objects within crowded environments, even in complex visual conditions. The model achieved a mAP of 85%, outperforming previous models such as YOLO V8'S (78.3%) and YOLO V7 (81.9%), demonstrating significant improvements in detection accuracy and localization capabilities. The custom-trained model further exhibited a detection accuracy of up to 95% in specific scenarios, ensuring reliable and real-time feedback to users regarding crowd conditions at various locations. By implementing a microservices architecture integrated with RESTful API communication, the system facilitates efficient data processing and supports a modular approach in system development, enabling seamless updates and scalability. This architecture allows for independent deployment of services, thereby minimizing system downtime and optimizing performance. The integration of YOLOv8 and the custom-trained model has proven to be effective in enhancing real-time monitoring and detection of crowd density, making it a suitable solution for diverse applications that require dynamic and accurate crowd information. The results indicate that the proposed model and system architecture can provide a robust framework for real-time crowd management, which is crucial for business owners, event organizers, and public safety monitoring. Future research should consider exploring newer versions of YOLO, such as YOLO V9-S, and expanding the dataset to address challenges related to varying lighting conditions, occlusions, and object orientations. Optimizing these factors will further improve the model’s accuracy and reliability, setting a new standard for crowd detection systems in public spaces and enhancing the overall user experience.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

Abstract

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm Yanto, Musli; Yuhandri, Yuhandri; Tajuddin, Muhammad; Septiana, Vina Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1183-1191

Abstract

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Technology Readiness Index untuk Menganalisis Kesiapan Adopsi Teknologi Kecerdasan Buatan Mahasiswa Komputer Wirahmadayanti, Isna; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.584

Abstract

The education sector combined with the branch of artificial intelligence has great potential to change the way information is accessed and managed to improve the learning experience and support decision making in the educational process. It is important to understand the level of readiness for the adoption of artificial intelligence among students as the main stakeholders in the educational environment. The purpose of this study was to determine the readiness for adoption of technology, and what factors influence the readiness for adoption of artificial intelligence in Computer Science Students at Universitas Putra Indonesia "YPTK" Padang. This study uses the Technology Readiness Index (TRI) method which consists of four variables, including the variables of optimism, innovativeness, discomfort, and insecurity. The Technology Readiness Index (TRI) measures a person's tendency to accept and use technology to complete goals in their home life or at work. This study was conducted by distributing questionnaires to 348 students consisting of students of information systems and informatics engineering study programs. Data were obtained from a total population of 2689 students, 348 samples were obtained based on the Slovin formula with an error margin of 5%. Determination of the sample to determine the number of samples of each stratum in the population with proportionate stratified random sampling in the Information Systems study program of as many as 250 students and the Informatics Engineering study program of 98 students. Manual calculations and using applications show that computer students at Universitas Putra Indonesia “YPTK” Padang are very ready to adopt artificial intelligence technology with variable values ​​of optimism 93.27%, innovative 92.64%, discomfort 91.66%, and insecurity 88.73%. These results can be stated that the factors that influence the readiness to adopt artificial intelligence technology include optimism, innovative, discomfort, and insecurity with a median index value of all variables of 92.15%
Prediksi Jumlah Kunjungan Pasien pada Bidan Praktik Mandiri dengan Jaringan Syaraf Tiruan Backpropagation Rifky, Muhammad; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.628

Abstract

Independent Midwives (BPM) are important in providing health services for mothers and children. One of the main challenges in managing BPM is the uncertain fluctuation in patient visits, making it difficult to plan resources, such as medical personnel, drug supplies, and other supporting facilities. If the number of patient visits cannot be predicted properly, the risk of shortages or excess resources becomes higher, which can impact operational efficiency and the quality of health services. Uncertainty in the number of patients can also affect financial planning and readiness to face a surge in visits. Based on this, this study aims to develop a prediction model for the number of patient visits using Artificial Neural Networks (ANN) with the Backpropagation method. The dataset uses data on the number of Antenatal Care (ANC) patient visits over the past three years. The results of the model evaluation were carried out based on the Mean Squared Error (MSE) value and the prediction accuracy level presented more than 94% accuracy level. The evaluation results also obtained an MSE value of 0.0023, and MAPE of 5.62% so that the results can be stated that the model prediction error is within acceptable limits. This predictive model can contribute to assisting BPM in resource planning, improving service efficiency, and strategic decision-making in managing health facilities
Penerapan Deep Learning Menggunakan Metode Convolutional Neural Network dan K-Means dalam Klasterisasi Citra Butiran Pasir Olivia, Ladyka Febby; Yuhandri, Y; Arlis, Syafri
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.629

Abstract

Agregat halus (pasir) merupakan bahan bangunan yang paling banyak digunakan dalam dunia konstruksi, sehingga kebutuhan pasir setiap harinya sangat banyak terutama di daerah perkotaan yang pembangunannya sangat pesat. Pasir berbentuk butiran - butiran yang memiliki tekstur berbeda untuk setiap jenisnya. Karakteristik pasir yang baik apat ditentukan melalui beberapa parameter, seperti segi kadar lumpur pasir, pemeriksaan kadar air nyata dan SSD, pemeriksaan gradasi, kadar air, zat organik, berat isi kondisi padat/gembur, daya serap, modulus kehalusan. faktor-faktor ini menjadi acuan dalam memilih pasir yang sesuai untuk berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. Parameter-parameter ini menjadi acuan dalam memilih pasir yang tepat untuk digunakan dalam berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. penelitian ini bertujuan untuk mengelompokkan kesesuaian antara butiran pasir untuk plesteran dinding atau lantai. Gambar dari citra butiran pasir memiliki nilai piksel yang banyak kerena terdiri dari tiga komponen warna yang mana red, green, blue. Sehingga membutuhkan teknik yang baik dalam menganalisa gambar ini. Metode yang digunakan dalam penelitian ini adalah Convulutional neural network (CNN) sebagai untuk mendeteksi dan mengekstraksi fitur butiran pasir, Convolutional Neural Network yang digunakan dalam penelitian ini adalah arsitektur resNet 50 sebagai memiliki kinerja tinggi dalam analisis citra.. Convolutional Neural Network memiliki arsitektur yang terinspirasi oleh struktur visual sistem manusia dan sangat efektif untuk tugas-tugas dalam ekstraksi gambar dan Metode K-means Clustering untuk menentukan pengelompokkan data ke dalam beberapa kelompok (klaster) sehingga data dalam satu klaster memiliki kemiripan tinggi sementara data antar klaster berbeda secara signifikan butiran pasir. Dataset yang diolah dalam penelitian ini bersumber di CV. Sumber Rezeki. Dataset terdiri 94 citra butiran pasir. Hasil penelitian menunjukkan bahwa pasir dapat diklasifikasikan ke dalam beberapa kategori mengelompokan seperti butiran bulat, butiran tajam, butiran tumpul, butiran tidak beraturan, butiran sub angular. Penelitian ini dapat menjadi acuan dalam menentukan kesesuaian butiran citra pasir yang cocok untuk lantai atau plesteran dinding dan membantu kontraktor memilih jenis pasir.
Penerapan Artificial Neural Network untuk Memprediksi Persediaan Obat Esensial Alfallah, Fadhly; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.630

Abstract

The availability of essential medicines is a fundamental factor in ensuring high-quality healthcare services, especially in primary healthcare facilities such as Puskesmas. Inefficient drug inventory management can lead to various issues, including drug shortages that disrupt medical services and overstocking that may result in waste due to expiration. An accurate prediction system is essential to support more effective and efficient drug inventory planning. This study aims to analyze historical drug usage patterns to generate more accurate predictions. The research methodology includes problem identification, data collection, preprocessing, ANN architecture design, implementation, and system evaluation. Historical drug usage data from previous years is used for training and testing, with a division of 70% for training and 30% for testing. The backpropagation algorithm is applied to optimize the model by adjusting parameters such as the number of neurons in the hidden layer, learning rate, and activation function. The study results show that the ANN model with a 12-12-1 architecture achieves a high prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.13% for paracetamol stock. The developed MATLAB application provides an interactive platform for users to input historical data and obtain dynamic stock predictions. This system implementation is expected to help Puskesmas manage drug inventory more effectively, reduce the risks of shortages and overstocking, and improve efficiency in essential drug distribution. This study contributes to the field of health informatics by demonstrating the effectiveness of ANN in drug inventory prediction. Future research may explore hybrid machine learning models or integrate external factors, such as seasonal disease patterns and community demand levels, to enhance predictive accuracy and adaptability.
Co-Authors - Hendrick - Khairiazaz AA Sudharmawan, AA Aal, Defrizal Abda Abda Abdul Azis Said Achmad Fauzan Syaputra Ade Dwi Dayani Afifah Cahayani Adha Aggy Pramana Gusman Agung Ramadhanu Agus Perdana Windarto Akbar Iskandar Akbari Wafridh Aldi Muharsyah Alfallah, Fadhly Alifcha Ghazian Alifia Restu Selvanda Allans Prima Aulia Andema, Henky Andre Rahmat Kurniawan Andrean, Fajri Ilhami Angga Putra Juledi Anita Sindar Anjun Dermawan Antoni Antoni Aprilian Gevindo Ardiyan, Destio Arif Budiman Arika Juwita Z Ariza Ikhlas Asyhari, Ahmad Aulia, Allans Prima Auriga, Wira Ayu Prima Siska Bambang Supperianto Billy Hendrik Borianto, B Budayawan, Khairi Budi Jaya Budi Permana Putra Chairul Imam Chairul Imam, Chairul Chandra, Mrs Montesna Dahria, Muhammad Dari, Rahmatia Wulan Darnis, Rahmi Delmayanti, Vera Dendi Ferdinal Deno Yulfa Ardian Desi Laidawati Devi Maryuni Dewi Eka Putri Dian Maharani, Dian Dikki Handoko Djasmayena, Selvia Djesmedi, Dinda Dodi Andre Putra Dolly Indra DWI JULISA UTARI Dwi Narulita Dwika Assrani Dzaki Al Fikri Effendy, Geraldo Revanska Efori Buulolo Eggy Febyanti Edwar Eka Naufaldi Novri Eka Praja Wiyata Mandala Eka Ramadhani Putra Eka Sofianti Elpina, Elpina Sari Dewi Hasibuan Eriyanto, Joko Erizke Aulya Pasel Esa Kurniawan Esa Kurniawan Eska, Juna Eva Rianti Fachrul Ilmawan Fadil Idensia Fahmi Firzada Fajri Ilhami Andrean Fauzan, Yuniko Febri Aldi Febri Hadi Feri Irawan Fernando Ramadhan Fhajri Arye Gemilang Finny Fitry Yani Firna Yenila Firzada, Fahmi Fitra, Ilham Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Dwi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Hadi Syahputra Hadrila P A Halifia Hendri Harkamsyah Andrianof Hartika Zain, Ruri Hartika Hartomi, Zupri Henra Hasanatul Iftitah Hasni, Salmi Hasri Awal Hendrick, H Hendro Zalmadani Henky Andema Hermanto Heru Rahmat Wibawa Putra Ibnu Luthfi Idir Fitriyanto Idir Idun Ariastuti Ikhlas, Muhammad Ilham Asy'ari Ilham Fitra Indah Dwi Putri Indah Permata Sari Indra Riyana Rahadjeng Irvan Okta Mazhona Iskandar Fitri, Iskandar Ismail Virgo Jaya, Budi Jefdy Kurniawan Jhon Veri Johan Danu Wijaya Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony K Kadrahman Kadrahman, Kadrahman Karseno, Doni Khairani, Maisan Dewi Puspa Khairiazaz Kurniawan, Jefdy Laidawati, Desi Larissa Navia Rani, Larissa Lc Granadi Suhaidir Lidia K Simanjuntak Liga Mayola Lova Endriani Zen Lusi Kestina M Ikhsan Setiawan M Ilham Aldyno M Mutia M, Mutia M.Iqbal, M.Iqbal Maharani Maharani, Maharani Majid Rahman Aziz Mardayulis, Mardayulis Mardison Mardison Mardison Meiditra, Irzon Mesran, Mesran Mey Yuki Lestari Mifthahul Rahmi Mohammad Guntur Montesna Muhammad Abrar Masril Muhammad Amin Muhammad Amin Muhammad Arif Zikir Risky Muhammad Ihksan Muhammad Noor Hasan Siregar Mukhlis Santoso Na'am, Jufriadif Nabilla Yasmin Nandra Sunaryo Nasma Yeni Nasution, Annio Indah Lestari Natalia Silalahi, Natalia Negoro, Wahyu Saptha Nelly Astuti Hasibuan Nissa, Ika Ima Nuning Kurniasih Nurdiyanto, Heri Olivia, Ladyka Febby Ondra Eka Putra P, Prihandoko Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Pratama , Abdul Hanif Pratama, Muhammad Harits Pratiwi, Fitri Prestian Ramadhan Prihandoko Prihandoko Prihandoko Prihandoko, P Pulungan, Akhiruddin Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putra, Rezi Elsya Putri, Stefani R Rahmiyanti Rafi Septiawan Putra Ragil Ardiansyah Rahayu, Rita Rahmad Dian Rahmad Dian Rahmansyah, Rizky Rakhmad Kuswandhie Resnawita Retno Devita Riadi, Rahadatul ‘Aisy Riati, Itin Ridho, Ridho Afwan Rifky, Muhammad Rio Andika Malik Ririn Violina Riski Randa Hidayatullah Rita Sari Rita Sari Rivo Stephano Roby Nurbahri Romi Hardianto Romzi Rahman Ronda Deli Sianturi Rovidatul Rubiati, Nur Rusydi, Rezki S Salmiati Sabri T Rahman Sagala, Gamrina Sahat Sonang Sitanggang Sahri, Alfi Said, Abdul Azis Sajida, Mayang Salman Alfarisi Salimu Salmiati, S Samosir, Khairunnisa Saputra, Randy Sari, Fitri P. Sarjon Defit Seni Oknora Firza Septiana Vratiwi Septiana, Vina Tri Setiawan, Adil Setiawan, Adil Silfia Andini Siregar, Diffri Sisi Hendriani Siska, Ayu Prima Soeheri Soeheri Sonang, Sahat Sonia Indhira Sopi Sapriadi Soraya Rahma Hayati Sovia, Rini Sri Amalia Harahap Sri Dewi Sri Dewi Sri Rahmawati Stefani Hardiyanti Putri Stephano, Rivo Subrianto Chandra Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Sunaryo, Nandra Supriyanto, Boby Surya Darma Nasution Suryani, Vivi Sutiksno, Dian Utami Syafri Arlis Syafrika Deni Rizki Syafril Syafril Syahid Hakam Abdul Halim Syahputra, Afriadi Syaiffullah, Afif Syaljumairi, Raemon Syaputra, Eka B. Tajuddin, Muhammad Takyudin, Takyudin Tamin, Zulfiqar Taufik Nur Zam Zam Teddy Winanda Teguh Junaidi Teri Ade Putra Tessa Y M Sihite Toti Sri Mulyati Tri Agusti Farma Triyolla Ivandina Tukino, Tukino Uthama, Rayhan Veri, Jhon Very, Jhon Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Wenni Afrodita Willy Eka Septian Winanda, Teddy Winarto Winarto Wira Apriani Wira, M Wira Sanjaya Wirahmadayanti, Isna Yanti, Salma Nofri yanto, heri Yanto, Musli Yanto, Musli Yendi Putra Yendi Putra Yeni, Nasma Yolla Rahmadi Helmi Yosua Ade Pohan Yuda Irawan Yuda, Fitra Yuda Yudha Aditya Fiandra Yudha Aditya Fiandra Yundari, Yundari Yuniko Fauzan Yusma Elda Yusmaity Zalmadani, Hendro ZH, Lina Alfaridah. Zufari, Faisal Zupri Henra Hartomi