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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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Model Interpretation for Student Major Selection Using Principal Component Analysis and Random Forest Antoni Antoni; Sarjon Defit; Yuhandri Yuhandri
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2747

Abstract

The development of information technology has had a significant impact on the education sector by providing data-driven tools to support the process of major selection. This process often causes confusion among students due to its crucial role in determining their academic and career futures. This study aims to develop an accurate and transparent recommendation system for major selection through the integration of Principal Component Analysis (PCA), Random Forest (RF), and SHAP. The research follows a systematic framework that includes data processing and model evaluation stages. PCA is applied to reduce the dimensionality of complex student data in order to improve computational efficiency and minimize information redundancy. Furthermore, the Random Forest algorithm is employed as a classification model to predict major recommendations such as Science, Social Sciences, and Religious Studies. The SHAP method is integrated to provide both mathematical and visual interpretations of the contribution of each academic feature to the model’s prediction results. The research data are obtained from the internal records of MAN 1 Payakumbuh covering the last three academic years (2022/2023–2024/2025). The dataset consists of 571 eleventh-grade students with tenth-grade academic scores and non-academic skill variables. The implementation of this model is able to provide more objective recommendations compared to conventional subjective assessments, achieving an accuracy of 88.70%. Visualization of feature contributions using SHAP enhances transparency and facilitates stakeholders’ understanding of the basis for each model decision. This study contributes to improving the efficiency of the major selection process and supports more accurate academic decision-making for students and educators.
Classification of Avocado Ripeness Levels Using Transfer Learning Based on VGG16 and VGG19 Ibnu Luthfi; Yuhandri Yuhandri; Gunadi Widi Nurcahyo
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2749

Abstract

The determination of avocado ripeness is still commonly performed manually, which is subjective and often inaccurate due to its reliance on human visual perception. Traditional methods, such as pressing the fruit surface, may damage avocado quality and are inefficient for large-scale distribution and marketing. This study aims to automatically classify avocado ripeness levels using a deep learning approach based on transfer learning. The proposed method employs transfer learning using Convolutional Neural Network architectures, namely VGG16 and VGG19, which have been pre-trained on the ImageNet dataset. The research stages include image pre-processing such as resizing, normalization, and data augmentation to enhance input quality. Subsequently, model training and testing are conducted by comparing the performance of both architectures using evaluation metrics. The dataset used in this study is obtained from the Kaggle platform and consists of avocado images with various ripeness levels. Experimental results indicate that both models are capable of classifying avocado ripeness with high accuracy, precision, recall, and F1-score, with the VGG19 model achieving the best performance. These findings demonstrate that the deep learning approach effectively addresses the subjectivity and inaccuracy associated with manual avocado ripeness determination. This study contributes to the development of an accurate, objective, and practical image-based avocado ripeness classification system with potential applications in agriculture and fruit distribution
Penerapan Deep Neural Investigation Network (DNIN) Dengan Feature Selection Untuk Prediksi Bencana Banjir Fachrul Ilmawan; Yuhandri Yuhandri; Sumijan Sumijan
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9321

Abstract

Floods are natural disasters caused by high rainfall intensity and poor absorption capacity in an area. The impact of floods results in material and human casualties, necessitating a flood disaster mitigation process. Based on this, the purpose of this study is to predict flood disasters with the Deep Learning (DL) concept using the Deep Neural Investigation Network (DNIN) method, which is a CNN–BiLSTM hybrid. The research method used includes Deep Neural Investigation Network (DNIN) combined with feature selection to predict floods. Feature selection is carried out using the SelectKBest method with the ANOVA F-test (f_classif) evaluation function to select features that have the most significant influence on the target flood variable. The DNIN method extracts features from input data and processes the sequence of these features to capture two-way temporal dependencies before being used for prediction. This research dataset consists of 3000 rows of data sourced from Kaggle (https://www.kaggle.com/datasets/yusufginanjar7/banjir-jabodetabek) with fields name_2, name_3, avg_rainfall, max_rainfall, avg_temperature, elevation, landcover_class, ndvi, slope, soil_moisture, year, month banjir, lat long. The results of this study have proven the application of the Deep Neural Investigation Network (DNIN) method with feature selection is able to predict floods. The results show that the application of the DNIN method with feature selection is able to predict flood disasters with an accuracy level of 93%. Based on the results of this study, the application of the Deep Neural Investigation Network (DNIN) method with feature selection is able to provide a significant contribution in predicting flood disasters accurately and can be used as a decision support system in flood disaster risk mitigation and reduction efforts
Simulasi dan Analisis Strategi Hybrid Teaming Menggunakan Algoritma Naive Bayes dalam Deteksi Serangan Distributed Denial of Service (DDoS) Aprilian Gevindo; Yuhandri Yuhandri; Billy Hendrik
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9323

Abstract

Cyber attacks, particularly Distributed Denial of Service (DDoS), have become a serious threat to the availability of servers and other network infrastructure. These attacks can paralyze services on large-scale networks by flooding the target system with extremely high traffic. Based on this, the objective of this research is to simulate and analyze a Hybrid Teaming strategy using the Naïve Bayes algorithm. This strategy simulates structured collaboration between the Red Team (attackers), Blue Team (defenders), and Purple Team (evaluators) to test resilience while comprehensively strengthening the security posture. The Naïve Bayes algorithm is one of the best algorithms in Machine Learning and excels at performing data classification processes. The performance of the Naïve Bayes algorithm combined with the Hybrid Teaming strategy is developed into an intelligent detection system. This system is trained using 10,000 data points from a public dataset and 1,688 data points from the network logs of the Tapan Regional General Hospital (RSUD). Based on the data analysis results, the model training outcomes fall into the perfect category, with accuracy, precision, recall, and F1-score achieving a result of 100%. The model was then implemented on a server and a MikroTik router within a simulation environment that replicates the Tapan RSUD network. The test results on these two components show that the system successfully detected various Flooding attack patterns with a detection accuracy of 100%. The system is capable of responding automatically by blocking the attacker's IP (Internet Protocol) address at both layers, as well as sending real-time notifications via WhatsApp and Email. The contribution of this research results in a comprehensive and effective cybersecurity defense framework.
Komparatif Metode Convolutional Neural Network, GoogleNet & Transfer Learning pada Klasifikasi Sampah Ariza Ikhlas; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9326

Abstract

The waste problem is a very complex global issue, especially in Indonesia. The volume of waste that continues to increase every year is a major challenge for the environment, health, and the economy. so it is necessary to conduct research related to smart waste management, namely, a concept of utilizing artificial intelligence in waste management by adopting image management techniques. Based on this, this study aims to compare the modeling of Covolutional Neural Network (CNN), GoogleNet, and Transfer Learning. The methods used in this study, CNN, GoogleNet, and Transfer Learning by utilizing data augmentation, activation functions, and transfer learning, are able to overcome the problem of limited data and reduce or avoid overfitting problems in modeling. The datasets used in this study are sourced from datasets built by the researcher himself and Kaggle datasets with a total of 300 samples consisting of 6 classes: Cardboard, Glass, Plastic, Metal, Paper, and Other/Trash. The results present that the transfer learning method is superior to other methods with accuracy, precision, recall, and f1-score, 100%. The contribution of this research is to enrich the literature in the field of machine learning and computer vision, develop more efficient models for limited datasets, and become a reference for future researchers who want to develop similar systems.
IDENTIFIKASI AKSARA JAWI PADA NASKAH KUNO PADA CITRA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Devi Maryuni; Yuhandri Yuhandri; Sumijan Sumijan
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3832

Abstract

Permasalahan pelestarian naskah kuno tidak hanya terkait dengan kondisi fisik naskah yang semakin rapuh, tetapi juga dengan keterbatasan sumber daya manusia dalam memahami isi serta aksara yang digunakan, khususnya aksara Jawi atau Arab Melayu. Rendahnya kemampuan masyarakat dalam membaca dan memahami aksara Jawi menjadi hambatan utama dalam mengakses isi naskah secara luas, sehingga berdampak pada terbatasnya pemanfaatan naskah kuno sebagai sumber pengetahuan dan warisan budaya daerah. Penerapan teknologi informasi seperti metode Convolutional Neural Network (CNN) diharapkan mampu mengidentifikasi huruf aksara Jawi sehingga dapat membantu mengatasi keterbatasan kemampuan membaca aksara tersebut serta mendukung proses digitalisasi naskah kuno berbasis citra. Data penelitian diperoleh dari citra naskah kuno beraksara Jawi yang tersimpan di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat, yang selanjutnya digunakan sebagai dataset pelatihan dan pengujian model CNN. Hasil penelitian menunjukkan bahwa model CNN yang dibangun mampu mencapai nilai akurasi sebesar 82,24% dalam mengidentifikasi huruf aksara Jawi. Berdasarkan nilai akurasi tersebut, dapat disimpulkan bahwa metode CNN cukup efektif dalam mengatasi permasalahan keterbatasan pemahaman aksara Jawi dan mampu mengenali pola huruf pada naskah kuno dengan baik. Berdasarkan hasil penelitian, penggunaan CNN diharapkan dapat berdampak pada pelestarian naskah kuno terutama dalam peningkatan aksesibilitas, pelestarian digital, serta pemanfaatan naskah kuno sebagai warisan budaya dan sumber pengetahuan bagi masyarakat luas.
IDENTIFIKASI AKSARA JAWI PADA NASKAH KUNO PADA CITRA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Devi Maryuni; Yuhandri Yuhandri; Sumijan Sumijan
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3832

Abstract

Permasalahan pelestarian naskah kuno tidak hanya terkait dengan kondisi fisik naskah yang semakin rapuh, tetapi juga dengan keterbatasan sumber daya manusia dalam memahami isi serta aksara yang digunakan, khususnya aksara Jawi atau Arab Melayu. Rendahnya kemampuan masyarakat dalam membaca dan memahami aksara Jawi menjadi hambatan utama dalam mengakses isi naskah secara luas, sehingga berdampak pada terbatasnya pemanfaatan naskah kuno sebagai sumber pengetahuan dan warisan budaya daerah. Penerapan teknologi informasi seperti metode Convolutional Neural Network (CNN) diharapkan mampu mengidentifikasi huruf aksara Jawi sehingga dapat membantu mengatasi keterbatasan kemampuan membaca aksara tersebut serta mendukung proses digitalisasi naskah kuno berbasis citra. Data penelitian diperoleh dari citra naskah kuno beraksara Jawi yang tersimpan di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat, yang selanjutnya digunakan sebagai dataset pelatihan dan pengujian model CNN. Hasil penelitian menunjukkan bahwa model CNN yang dibangun mampu mencapai nilai akurasi sebesar 82,24% dalam mengidentifikasi huruf aksara Jawi. Berdasarkan nilai akurasi tersebut, dapat disimpulkan bahwa metode CNN cukup efektif dalam mengatasi permasalahan keterbatasan pemahaman aksara Jawi dan mampu mengenali pola huruf pada naskah kuno dengan baik. Berdasarkan hasil penelitian, penggunaan CNN diharapkan dapat berdampak pada pelestarian naskah kuno terutama dalam peningkatan aksesibilitas, pelestarian digital, serta pemanfaatan naskah kuno sebagai warisan budaya dan sumber pengetahuan bagi masyarakat luas.
GoogLeNetMP: A Development of GoogLeNet Architecture for Multi-Class Microplastic Classification in Subsurface Water Image Halifia Hendri; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1372

Abstract

Microplastic pollution has become a major environmental concern due to its persistence in marine ecosystems and its potential impact on aquatic organisms and human health. Automatic detection of microplastic particles in underwater environments remains challenging because of turbidity, low contrast, light distortion, and the visual similarity between microplastics and natural marine objects. This study proposes GoogLeNetMP, an enhanced GoogLeNet-based deep learning architecture for multi-class classification of subsurface marine images into four categories: primary microplastics, secondary microplastics, non-microplastics, and marine biota. The proposed framework integrates basic image preprocessing (resizing and noise reduction) with a modified GoogLeNetMP architecture designed to intrinsically handle fine-grained feature extraction under degraded conditions, thereby minimizing the reliance on complex external enhancement pipelines. A dataset of underwater images acquired from the coastal waters of Padang, Indonesia, was used for model development and evaluation. Experimental results show that GoogLeNetMP outperformed the standard GoogLeNet model, achieving 95.75% accuracy, 92.80% sensitivity, 97.00% specificity, and an F1-score of 92.06%. The proposed model also demonstrated more stable training convergence and better discrimination of visually challenging classes. The architecture is designed to internalize the robust feature extraction process, thereby minimizing the reliance on extensive external enhancement pipelines while maintaining standard normalization steps for input consistency. These findings indicate that GoogLeNetMP is a promising approach for AI-based marine pollution monitoring and decision support in sustainable coastal management.
Deteksi Pelanggaran Tata Tertib Siswa Sistem Cerdas Menggunakan Face Recognition dengan Metode Convolutional Neural Network Syafril Syafril; Yuhandri Yuhandri; Rini Sovia
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.753

Abstract

Student disciplinary violations are a social problem increasingly common in schools and can negatively impact students' academic and moral development. This phenomenon requires an effective identification system so that prevention and mitigation efforts can be carried out quickly and accurately. This research aims to develop a student face detection system based on Digital Image Processing (DIP) technology that functions to identify and classify adolescent disciplinary violations. The designed system utilizes a camera as an image acquisition device, then processes it to detect the presence of student faces in real-time. The face detection process is carried out using the Haar Cascade Viola-Jones method, which is known to be able to recognize faces with high speed and accuracy. Once a face is detected, the system continues the analysis process using the Convolutional Neural Network (CNN) method to classify facial expressions and behavioral patterns that could potentially indicate violations. The integration between Haar Cascade and CNN allows the system to work efficiently in identifying signs of negative behavior based on visual data. System testing shows satisfactory results, with a high level of facial detection accuracy and fairly reliable behavior classification capabilities. This technology has the potential to be used as a monitoring tool in the school environment, allowing teachers and school management to quickly identify students who need special attention. With the implementation of this system, it is hoped that schools will be able to provide timely guidance, prevent the escalation of deviant behavior, and create a more conducive learning environment. The use of digital image processing-based technology for detecting and classifying student behavior is a relevant innovation in the modern education era, while also supporting efforts to prevent juvenile disciplinary violations through a systematic and measurable approach.
Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Nabilla Yasmin; Yuhandri Yuhandri; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
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