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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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EXPLANATION OF FEATURE EXTRACTION IN FACE RECOGNITION USING VIOLA JONES ALGORITHM Devita, Retno; Rianti, Eva; Yuhandri, Muhammad Habib; Putra, Ondra Eka
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3844

Abstract

Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.
JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Sari, Rita; Wahyu Saptha Negoro; Yuhandri
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Sistem Deteksi Kerumunan Fasilitas Pelayanan Publik dengan Crowd Counting P, Prihandoko; Yuhandri, Muhammad Habib; Pratama , Abdul Hanif
Jurnal KomtekInfo Vol. 11 No. 4 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i4.410

Abstract

Improvement of system management in public services needs special attention in an era of increasing population growth. Crowd Counting is proposed to ensure that the detection system for crowd objects in public facilities can run optimally. This study aims to develop Crowd Counting in a crowd object detection system in public facilities. This development is carried out to improve the performance of the You Only Look Once (YOLO) algorithm based on the Streamlit Framework. The performance of the YOLO algorithm can provide maximum results by combining the streamlit framework based on the image of the captured object at the train station. The test results of the development of Crowd Counting presented provide output with an mAP value of 90%, Recall 95%, and Precision 93.6%. Blackbox testing has also shown that the performance of Crowd Counting has provided quite significant detection accuracy. This research can contribute to the renewal of the detection system and be used as a form of solution in handling crowd problems in public facilities
Penerapan Metode Vikor Untuk Menentukan Kelayakan Penyewa Tempat Usaha Pada UIN Bukittinggi Resnawita; Yuhandri; Sumijan
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.536

Abstract

Seiring kemajuan teknologi yang berkembangan pesat, mendorong lahirnya inovasi-inovasi baru untuk memenuhi kebutuhan dunia kerja dan sebagai alat bantu manusia. Penggunaan teknologi dalam mengelola sebuah unit usaha kampus akan membantu instansi dalam melakukan pekerjaannya. Unit usaha kampus merupakan bagian penunjang pembelajaran dan pelayanan kepada mahasiswa dan Masyarakat. Unit ini biasanya bertugas untuk mengelola pendapatan dan pengeluaran, mengembangkan strategi keuangan, mengelola layanan katering, toko buku, kantin,tempat parkir, serta menjalankan berbagai jenis usaha lainnya yang terkait dengan kebutuhan mahasiswa dan staf kampus. Pengelolan unit usaha kampus akan membutuhkan banyak pertimbangan untuk megelola unit usaha kampus seperti pengambilan keputusan untuk penyewa tempat usaha atau kantin yang sesuai dengan syarat yang terdapat dalam perguruan tinggi dalam pengambilan keputusan terdapat beberapa faktor ataupun kriteria yang diperlukan agar keputusan yang nantinya diperoleh sesuai dengan syarat yang dibutuhkan oleh unit usaha kampus. Sehingga penggunaan teknologi dibutuhkan untuk mepermudah kampus dalam menentukan keputusan yang akan diperoleh. Penelitian ini bertujuan untuk memberikan rekomendasi kelayakan penyewa tempat usaha di UIN Bukittinggi. Metode penelitian yang digunakan dalam penelitian adalah metode VIKOR (Visekriterijumsko Kompromisno Rangiranje). Data yang digunakan dalam penelitian merupakan data penyewa tempat usaha UIN Bukittinggi. Terdapat 5 kriteria penilaian dan 10 data penyewa yang digunakan dalam penelitian. Dari hasil perangkingan didapatkan hasil bahwa calon penyewa dengan ID P01 mendapatkan nilai terkecil yaitu 0 dan merupakan peringkat pertama dalam perhitungan vikor. Penelitian ini menunjukkan bahwa metode VIKOR efektif dalam memberikan rekomendasi yang berbasis data dan kriteria yang telah ditentukan, sehingga dapat digunakan sebagai alat pendukung keputusan yang handal dalam pemilihan penyewa tempat usaha di lingkungan kampus.
Penerapan Acunetix Vulnerability Scanner dari Serangan Siber pada Keamanan Website Kampus Rusydi, Rezki; Yuhandri; Arlis, Syafri
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.569

Abstract

Keamanan website telah menjadi salah satu aspek yang paling penting dalam menjaga integritas, kerahasiaan, dan ketersediaan informasi serta data dari ancaman serangan siber. Sebagai institusi akademis yang mengelola berbagai data penting, Institusi menghadapi tantangan signifikan dalam memastikan bahwa website mereka terlindungi dari berbagai ancaman keamanan yang semakin kompleks dan canggih. Keamanan website tidak hanya penting untuk menjaga data institusi, tetapi juga untuk melindungi privasi dan informasi pribadi pengguna yang berinteraksi dengan platform tersebut. Penelitian ini berfokus pada analisis dan peningkatan sistem keamanan website Fakultas Teknik UM Sumatera Barat dengan menggunakan Acunetix Vulnerability Scanner. Alat ini adalah salah satu solusi otomatis yang dirancang untuk mengidentifikasi kerentanan keamanan pada aplikasi web. Acunetix memungkinkan pendeteksian kerentanan secara cepat dan menyeluruh, sehingga memberikan gambaran yang jelas mengenai potensi risiko yang mungkin dihadapi oleh website tersebut. Metode penelitian yang diterapkan dalam studi ini melibatkan pengujian penetrasi menggunakan Acunetix untuk mendeteksi berbagai celah keamanan yang ada pada website. Pengujian ini mencakup identifikasi terhadap celah yang mungkin dieksploitasi oleh pihak tidak bertanggung jawab, termasuk serangan cross-site scripting (XSS), SQL injection, dan kerentanan terhadap serangan Distributed Denial of Service (DDoS). Hasil analisis menunjukkan bahwa terdapat beberapa kerentanan kritis yang harus segera diatasi untuk mencegah potensi eksploitasi. Berdasarkan temuan ini, peneliti menyusun rekomendasi perbaikan dan mitigasi yang bertujuan untuk mengurangi risiko serangan siber terhadap website. Berdasarkan hasil scanning literasi pertama, website Fakultas Teknik UM Sumatera Barat dikategorikan pada tingkat ancaman 3 yang termasuk tinggi, dengan terdapat 245 peringatan atau kerentanan yang teridentifikasi, di antaranya, 8 dianggap berada pada tingkat high, 2 berada pada tingkat medium, 13 berada ditingkat Low dan selebihnya Informational Berdasarkan evaluasi yang telah dilakukan, tingkat keamanan yang tercapai berada pada level 0. Pada level ini, tidak terdapat kerentanan yang teridentifikasi (nol kerentanan) dan dukungan keamanan juga mencapai tingkat optimal (nol dukungan). Oleh karena itu, dapat disimpulkan bahwa situs web Fakultas Teknik UM Sumatera Barat saat ini, dengan status level 0, tidak memiliki kerentanan keamanan. Hasil penelitian bisa menjadi acuan bagi pengelola website di lingkungan akademis, dalam melindungi website dari ancaman siber.
The Development of Affine Transformation Method Using Scale Invariant Feature Transform (SIFT) Hartika Zain, Ruri Hartika; Yuhandri, Yuhandri; Sovia, Rini
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3653

Abstract

Carving is a technique used to create decorative images on wood, stone, and other materials. In Indonesia, wood is a popular choice because of its durability and attractive grain. Examples of wood carvings include floral designs. The carving process can involve changes in color, texture, and scale, which may affect the carving's size and appearance and cause dimensional changes in certain materials. This study addresses the issue of quality control in wood carving on thin veneer layers. Free wood-carving data are provided as 200 flower images that can be used as input images. Affine transformation is used to determine the system behavior and the material transfer function during the production process. Additionally, we propose extending the affine transformation method to use the Scale-Invariant Feature Transform (SIFT). Affine transformations enable correlation analysis, outlier removal, and feature orientation in the affine domain. The SIFT algorithm accounts for scale, rotation, brightness, and perspective. Applications using ASIFT can efficiently process images and handle those with different pixel sizes to create new carvings. Training samples used to update the filter model are changed to the same pose. This enables the flower wood carving filter to represent objects with 98% accuracy. The model is then used to predict the class of the flower-carving data and to compute the distance between the template image's features and those of the input flower-wood-carving image. This research project has successfully developed an Affine Transformation method using SIFT features to create a new engraving application based on the ASIFT approach. 
Sistem Deteksi Kepuasan Pelanggan dengan Teknik Pengelolaan Citra Menggunakan Convolutional Neural Networks Saputra, Randy; Yuhandri, Yuhandri; Arlis , Syafri
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1219

Abstract

Advancements in computer vision and facial expression recognition provide a new, objective, and non-intrusive method for measuring customer satisfaction in real time. This study develops a customer satisfaction detection system at Rumah Diskusi ALCO Café using Convolutional Neural Networks (CNN) with a mixed-methods approach, combining quantitative and qualitative analysis. The RAF-DB dataset containing 15,339 labeled images (12,271 for training and 3,068 for testing) across seven emotion classes was processed through image acquisition, preprocessing, and ResNet50 fine-tuning. The resulting model achieved an accuracy of 80.34%, with a Precision of 83.55%, Recall of 81.78%, and F1-Score of 82.32% on the test data. Field implementation over four weeks successfully recorded and analyzed thousands of customer facial expressions in key areas such as the cashier and main seating area in real time. Results showed a customer satisfaction distribution of approximately 72% “Satisfied,” 16% “Quite Satisfied,” and 12% “Not Satisfied,” with a declining trend during peak hours in the afternoon. Cross-validation with customer surveys demonstrated a strong correlation between the system’s predictions and reported satisfaction, proving the effectiveness of this method as a real-time monitoring tool. The study contributes a practical technical and methodological framework that can be replicated in other service industries for objective and real-time customer satisfaction monitoring.
Convolutional Neural Network Method in Detecting Digital Image Based Physical Violence Elpina, Elpina Sari Dewi Hasibuan; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.657

Abstract

Physical violence in the educational environment has a serious impact on mental health, safety, and student achievement, in addition to causing physical injury, violence can cause psychological trauma that interferes with the learning process, due to the limited supervision system, lack of officers, and the absence of automatic detection technology. This research aims to design and develop an automatic detection system of physical violence using digital image processing technology. This study uses the Convolutional Neural Network (CNN) method with the stages of digital image collection and labeling, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The CNN architecture was chosen because it is efficient and accurate, and it supports data augmentation to improve generalization. The dataset was taken from kaggle and primary data at the al-falah huraba Islamic boarding school which consisted of 2000 images which included: 800 images of violence on CCTV of the dormitory room, 500 images of violence simulation of training videos and 500 non-violent images. The results showed that the developed CNN model was able to detect physical violence with an accuracy of above 88%, making it feasible to apply in surveillance camera-based school surveillance systems (CCTV). The system is able to classify images in real-time into two categories: safe and hard. This research contributes to the use of artificial intelligence to support efficient and affordable technology-based education security.
Combination of Support Vector Machine and Artificial Neural Network Methods in Negative Content Filtering System Wira, M Wira Sanjaya; Yuhandri, Y; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.660

Abstract

Local Wi-Fi network access has become a common necessity in everyday digital activities, but it is vulnerable to misuse to access negative content. This content includes pornographic material, hate speech, and violent content that can adversely affect users, especially in educational settings. For this reason, a system that is able to filter malicious content automatically and efficiently is needed. This research aims to design an artificial intelligence-based negative content filtering system that can be run on local network devices. The methods used include image classification using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN), as well as text classification with DistilBERT and Support Vector Machine (SVM). To maintain user privacy, the model is trained using a federated learning approach that allows for decentralized learning. Knowledge distillation is also applied to produce lightweight models that can be run on edge devices such as routers. The datasets used include NSFW Image Dataset, OpenPornSet, as well as a collection of toxic comments from Reddit and Twitter. The evaluation was carried out in a simulation of a local network with 50 active devices. The test results showed an ANN accuracy rate of 93.4% in recognizing visual content, and SVM accuracy of 91.7% in detecting text-based hate speech. This research can be a reference in the application of AI-based content filtering systems for safe and responsible digital access protection
Eksplorasi Algoritma Decision Tree untuk Penentuan Siswa Berprestasi Prestian Ramadhan; Yuhandri; Jhon Veri
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2210

Abstract

Kemajuan teknologi informasi telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk pendidikan. Salah satu tantangan utama dalam meningkatkan kualitas pembelajaran adalah identifikasi siswa berprestasi secara akurat dan objektif. Penelitian ini bertujuan untuk menerapkan algoritma Decision Tree C4.5 dalam menentukan siswa berprestasi di SMPN 1 Kerinci, dengan mempertimbangkan faktor akademik dan non-akademik seperti nilai disiplin, nilai tahfidz, nilai akhlak, dan nilai ujian. Metode penelitian mencakup pengumpulan data siswa, preprocessing data untuk mengatasi ketidakseimbangan data, analisis faktor-faktor yang berpengaruh, serta pembangunan model klasifikasi menggunakan perangkat lunak RapidMiner 9.0. Hasil penelitian menunjukkan bahwa algoritma Decision Tree C4.5 mampu mengklasifikasikan siswa dengan tingkat akurasi sebesar 91,67%, precision 93,33% untuk kelas "Tidak" dan 88,89% untuk kelas "Layak", serta recall masing-masing 93,33% dan 88,89%. Berdasarkan analisis gain ratio, nilai akhlak memiliki pengaruh terbesar dalam klasifikasi siswa, dengan nilai 0,5961, diikuti oleh nilai tahfidz dan nilai disiplin. Model klasifikasi ini dapat membantu sekolah dalam mengidentifikasi siswa secara lebih objektif, sehingga memungkinkan pengambilan keputusan berbasis data dalam memberikan intervensi pendidikan yang lebih tepat sasaran. Selain itu, hasil penelitian ini membuka peluang untuk pengembangan lebih lanjut, seperti penggunaan teknik ensemble learning atau optimasi model menggunakan metode boosting guna meningkatkan performa klasifikasi. Dengan demikian, sistem berbasis data mining ini dapat menjadi solusi inovatif dalam meningkatkan mutu pendidikan, mendukung kebijakan akademik yang lebih adaptif, serta mengarah pada pembelajaran yang lebih personalisasi dan efektif.
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