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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Elektron Jurnal Ilmiah Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech International Journal of Informatics and Computation Dinasti International Journal of Education Management and Social Science Systematics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak 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 Applied Computer Science and Technology (JACOST) Jurnal Manajemen Sains Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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ALGORITMA ASSOCIATION RULE METODE FP-GROWTH MENGANALISA TINGKAT KEJAHATAN PENCURIAN MOTOR (STUDI KASUS DI POLRESTA PADANG) Ghea Paulina Suri; Sarjon Defit; Sumijan
Jurnal Responsive Teknik Informatika Vol. 2 No. 01 (2018): JR : Jurnal Responsive Teknik Informatika
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v2i01.222

Abstract

Kendaraan bermotor merupakan sarana vital dengan mobilitas tinggi yang sangat diperlukan untuk kehidupan di era modern ini. Salah satu cara yang dapat dilakukan untuk penentuan strategi tersebut adalah dengan menggunakan teknik data mining. Adapun teknik yang digunakan Algoritma FP-Growth adalah salah satu alternatif algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam sekumpulan data. Tujuan dari penelitian ini adalah membangun suatu pengetahuan baru dalam menganalisa tingkat kasus pencurian motor dan memberikan informasi kepada kepolisian dalam mengatasi tingkat kejahatan. Sumber data masih belum lengkap karna data mentahnya masih belum diolah, data yang diambil merupakan data pencurian motor yang mencakup laporan dipolresta padang. Data yang di dapat memiliki atribut pekerjaan dan terlapor, data yang telah didapat belum bisa langsung diolah dan dikumpulkan dan diberi kode agar mudah dalam pemrosesan atau pengolahan data mining. Hasil dari pengujian terhadap metode ini maka didapatkan informasi untuk dapat membantu kepolisian dalam mengatasi tingkat kejahatan pada pencurian sepeda motor dan mengimplementasikan algoritma FP-Growth yang menggunakan konsep pembangunan FP-Tree dalam mencari Frequent Itemset. Maka dihasilkan Association Rule.
Comparison of Decision Tree and Random Forest Methods in Predicting Oil Palm Productivity After Replanting Sukardi; Yuhandri; Sarjon Defit
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i1.680

Abstract

Oil palm is a strategic commodity in Indonesia that can be affected by various factors such as plant age, soil conditions, rainfall, and maintenance variations between farmers. Over time, oil palm productivity decreases, so it is necessary to predict the productivity of oil palm rejuvenation. Based on this, the purpose of this study is to apply and compare the Decision Tree and Random Forest algorithms to predict the level of oil palm productivity after rejuvenation. The prediction process was carried out at the Koperasi Unit Desa (KUD) Tirta Kencana, Kuantan Singingi Regency. The Decision Tree algorithm is a supervised prediction model, meaning it requires a training dataset whose role replaces past human experience in making decisions. The Random Forest algorithm is also able to present several decision trees used in the prediction process. The dataset in this study amounted to 241 farmer data sourced from the KUD Tirta Kencana in Kuantan Singingi Regency. The comparative results of these two methods show that both the Decision Tree and Random Forest algorithms are capable of predicting precisely and accurately. The comparative results show that the random forest method outperforms the decision tree method with an accuracy of 99%. The contribution of this research provides knowledge with the application of data mining science by comparing the performance of the decision tree and random forest algorithms in the process of plant productivity management at KUD Tirta Kencana. Keywords: Oil Palm Productivity, Data Mining, Decision Tree, Random Forest, Productivity Prediction
Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision Akhiyar, Dinul; Tukino, Tukino; Defit, Sarjon
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2253.34-43

Abstract

The application of deep learning in various sectors continues to grow due to its ability to provide efficient and effective solutions to complex problems. One significant implementation is in object detection, such as identifying Indonesian rupiah banknotes. This innovation aims to assist individuals with visual impairments in using money more effectively. At present, visually impaired individuals rely on conventional methods, such as identifying banknotes by touch, folding them in specific ways, or seeking assistance from others. However, these methods are often time-consuming, prone to error, and lack practicality in everyday situations. In this project, a system was developed using the Convolutional Neural Network (CNN) architecture combined with the YOLO (You Only Look Once) algorithm. YOLO is renowned for its speed and accuracy in real-time object detection, making it an ideal choice for detecting banknotes in moving images. The training dataset included 1,260 images, and the model underwent 7,000 iterations during training. As a result, the system achieved a high mean Average Precision (mAP) score of 97.65%, demonstrating its robustness and precision. For validation, 140 test images were utilized, which yielded an impressive mAP value of 97.5%. To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. These tests resulted in an mAP score of 88%, showcasing the system's adaptability to real-world conditions. This system provides significant benefits for individuals with visual impairments by offering a practical, efficient, and accurate solution for recognizing banknotes. With this technology, visually impaired users can interact with currency independently, reducing their reliance on others and traditional, less practical methods. This innovation not only enhances their autonomy but also fosters inclusivity in financial transactions. By integrating this system into mobile applications or wearable devices, its accessibility and usability can be further improved, paving the way for a broader societal impact.
DETERMINING THE MARKETING STRATEGY OF STIE MAHAPUTRA RIAU USING THE K-MEANS CLUSTERING ALGORITHM METHOD Rahmadani Hidayat; Sarjon Defit; Menhard Menhard
Jurnal Apresiasi Ekonomi Vol 12, No 3 (2024)
Publisher : Institut Teknologi dan Ilmu Sosial Khatulistiwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31846/jae.v12i3.785

Abstract

The difficulty of getting new prospective students requires STIE Mahaputra Riau to be able to design an effective and efficient marketing strategy. This study aims to determine a marketing strategy using the K-Means Clustering method. The K-Means Clustering algorithm method is to cluster data based on the attributes of student name, school of origin, area of origin and chosen study program, so that cluster data output is obtained that can be used in making marketing strategy decisions. The sample data used in this study are data from high school, vocational high school or equivalent students who are in the third grade in 2023, specifically for the province of Riau and its surroundings, totaling 750 data. The results of this study indicate that based on the total student data of 750 people, they are grouped into 3 clusters. Cluster 1 consists of 145 people from Rokan Hulu, Indragiri Hilir, Bengkalis, Kuantansingingi and West Sumatra Regencies. Cluster 2 consists of 344 people from Kampar and Indragiri Hulu Regencies. And cluster 3 as many as 261 people from Pelalawan, Siak and Rokan Hilir Regencies. It was also found in each cluster, the study program with the most interest was the S1 Management study program. So the marketing strategy implemented should pay attention to the area of origin and the study program chosen as the basis for implementing policies in accepting new prospective students.Keywords : Data Mining, Marketing Strategy, Clustering, K-Means Method
Accurately Determining Labor Test Results Using the Rough Set Method Retno Devita; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7069

Abstract

An exam is something that must be done to test a person's ability or intelligence. The laboratory exam in the Computer Systems study program at Putra Indonesia University "YPTK" Padang consists of a digital systems exam, a fuzzy logic control exam, and a tool presentation. The Labor Exam must be passed by students who will take the comprehensive exam. In this study, laboratory exam data was taken for 20 students. So far, processing of student laboratory exam results has been done manually so it takes a long time to make decisions. To overcome this problem, a Rough Set method is used to determine laboratory test results. The Rough Set method is part of machine learning. This research produces 29 rules as knowledge, namely {Digital System} Or {A} = 3 rules, {Fuzzy Logic} Or {B} = 3 rules, {Tool Presentation} Or {C} = 3 rules, {Fuzzy Logic, Tool Percentage} Or {BC} = 6 rules, {Digital System, Fuzzy Logic} Or {AB} = 6 rules and {Digital System, Tool Percentage} Or {AC} = 8 rules. The Rough Set method can determine student laboratory exam results (pass or fail) accurately.
Machine Learning Predicts the Level of Disease Spread Dhio Saputra; Irzal Arief Wisky; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7070

Abstract

The aim of the research is predictive analysis of the spread of disease. Variable analysis at the population level in a region and the total disease events detected in the community. These variables can show the accuracy and certainty of the status of the resulting analysis. The concept of Machine Learning analysis is proposed to develop previous analysis models. The methods used include the K-Means cluster, Naïve Bayes, and Decision Tree (DT). There are two stages in the analysis process: pre-processing and classification. The discussion presented by K-Means provides a classification analysis pattern. The patterns obtained will be passed on to the classification process using Naïve Bayes and DT. Naïve Bayes results provide quite significant results with an accuracy rate of 83.33%. DT can also describe the results of information and knowledge analysis in the form of decision trees. DT produces decision trees that can provide knowledge and information analysis. The DT results provide an accuracy rate of 91.76% so these results can be used as consideration in decision making. The resulting information and knowledge can be used as a guide in making policies for handling health in the community.
Prediction of Graduation Accuracy Using the K-Means Clustering Algorithm and Classification Decision Tree Sri Rahmawati; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7073

Abstract

Becoming a scholar at the right time for students is a very meaningful award for them if it is supported by seriousness and perseverance in their studies. Here, sample data was taken from 131 randomly taken in testing. Where there are still students who are not detected by the study program in completing their lectures, so research is carried out on clustering and classification with decision trees in determining the level of accuracy of lectures by clustering data, determining the initial centroid value and the centroid point. The results found were that there were 78 people grouped in cluster 0 and 53 people grouped in cluster 1, where those with potential for punctuality for their studies were in cluster 0 so they were students who could finish within the specified time. Meanwhile, students grouped in cluster 1 illustrate that these students need coaching and guidance both in the study program and with their supervisors. In the classification taken from the results of data clustering, two classes were obtained, namely class a and class b, with 73 and 58 data respectively, so that the results between clustering and classification did not differ too much in the data to predict the accuracy of a student's graduation.
Rought Set: Effective Method for Determining Scholarship Recipients Silfia Andin; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7088

Abstract

Every year, higher education institutions receive a KIP Tuition scholarship quota that has been determined by Ristek Dikti through LLDIKTI which is given during the new student admissions process. The process of determining recipients is carried out manually resulting in inaccurate scholarship recipients being selected and the selection results may not be the same based on those who participated in making the decision. This research is motivated by the need for an algorithm for determining prospective scholarship recipients that is appropriate and effective because the recipient selection process often takes a long time because many high school and equivalent students register so that they exceed the quota limit while the quota given is limited. This research aims to use a system for scholarship recipients and provide rules and knowledge, namely rough set Theory and adapted to the Rosetta application, using prospective student data during the selection process for new students who apply for the KIP Kuliah scholarship in the 2020/2021 academic year. The resulting decision is the KIP Opportunity which consists of 4 (four) attributes, including parents' income, housing status, dependents, and parental status. The results of this research using sample data from 12 people produced 6 (six) rules and knowledge of 26 rules. This research is very supportive in identifying the eligibility of KIP Kuliah recipients.
Texture and Flag Color Extraction in Backpropagation Neural Network Architecture Syafrika Deni Rizki; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 5 (2024): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i5.7146

Abstract

A flag is a rectangular or triangular piece of cloth or paper used as a symbol of the state, association, body, etc. or as a sign. It is often also used to symbolize a country to show its sovereignty. Along with the large number of countries, the country's flag also has many varieties and colors. The use of computers as a human aid is expected to the extent that the computer's ability can replace the limitations that humans have. Humans can recognize an object by using their eyes and brain, but if the eyes and brain cannot work properly it will hamper human work. In this research, training will be conducted on the Back Propagation Neural Network Architecture. Characteristic data for image recognition is obtained by extracting texture features and RGB color features. So that the network can recognize the flags by matching the feature data obtained from the training carried out. Characteristic data obtained from 24 data consisting of 16 training images and 8 testing images. From the results of the image network training can be identified properly, the accuracy rate of object identification is 87.50%. GUI users are able to identify flag images based on RGB text and color features.
Development of the Rough Set Method to Determine Lecturer Scholarship Opportunities Surmayanti; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 5 (2024): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i5.7147

Abstract

Currently, all groups can experience the development of artificial intelligence, this happens because artificial intelligence has experienced very significant changes. Artificial Intelligence (AI) consists of several branches, one of which is machine learning. Machine Learning (ML) technology is a branch of AI that is very interesting because it is a machine that can learn like humans. The method used here is the rough set method. In this research, a case will be raised to determine scholarship opportunities for lecturers based on predetermined criteria. To solve the problem above, machine learning was used using the Rough Set method, using Rosetta software. By the regulations determined by the scholarship provider, in this case, the institution concerned where the lecturer is registered as teaching staff to obtain a scholarship, criteria are needed to determine who will be selected to receive the scholarship. The distribution of scholarships is carried out to improve lecturer performance, as an achievement as well as an appreciation for the lecturer concerned for his long service to the institution.
Co-Authors Abdul Azis Said Abulwafa Muhammad Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zaki Ahmad Zamsuri, Ahmad AHMADI Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Ali Ikhwan Alkhairi, Putrama Alvi Dwi Wahyuni Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andri Nofiar Angga Putra Juledi Anisya Anisya Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Billy Hendrik Bob Subhan Riza Bosker Sinaga Boy Sandy Dwi Nugraha.H Breinda, Engla Brestina Gultom Bufra, Fanny Septiani Chairun Nas Cyntia Trimulia Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Defi Pebriyanti Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devia Kartika Devita, Retno Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma Elfiswandi Elfiswandi eriwandi Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza Fajrul Islami Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febi Nur Salisah Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus Firdaus Firdaus, Muhammad Bambang Fitri Safnita Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Ghea Paulina Suri Gunadi W Nurcahyo Gunadi Widi N. Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Guslendra Guslendra, Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Istianingsih, Nanik Iswandi Saputra Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Lengga S. Sandy Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Iqbal Zuqron M. Syahputra Mardayatmi, Suci Mardian, Zurni Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen Muhammad Dahria Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Ramdani Bayu Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rafki, Rafnelly Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Ramadhanu, Agung - Randy Permana Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Rianti, Eva Rico Anggara Rini Sovia Rini Sovia Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila S Sumijan S Sumijan Sabil, Muhammad Said, Abdul Azis Saiful Nurarif Sandrawira Anggraini Sani, Rafikasani Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Silfia Andin Sintia Sintia Siregar, Diffri Solihin Siregar, Fajri Marindra Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti, Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva Tukino, Tukino tukino, tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yenila, Firna Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z