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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 Information System for Educators and Professionals : Journal of Information System Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal Sebatik 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) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) 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 INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System 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 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Satisfaction as an Experience Engine in Promotion-Intensive Marketplaces: The Roles of Platform-Mediated Relationship Infrastructure and Price Fairness in Repurchase Intention (Shopee, Indonesia) Rafnelly Rafki; Sarjon Defit; Elfiswandi Elfiswandi
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 4 (2026): Dinasti International Journal of Education Management and Social Science (April
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i4.6366

Abstract

This study investigates how price fairness and platform-mediated relationship infrastructure shape repurchase intention in a promotion-intensive online marketplace, and whether customer satisfaction is the dominant mechanism linking these drivers to repeat buying. Using a cross-sectional survey of active Shopee customers in Indonesia (N = 200), we analysed the model with partial least squares structural equation modelling (PLS-SEM) and tested indirect effects via bootstrapping. Customer satisfaction strongly predicts repurchase intention. Relationship infrastructure captured through responsiveness, assurance, and service recovery significantly increases satisfaction but shows no direct effect on repurchase intention, indicating full mediation through satisfaction. Price fairness does not significantly influence satisfaction and has only a weak direct association with repurchase intention, suggesting that fairness functions more as an acceptability and credibility cue than as a driver of satisfaction. The findings position governable relationship infrastructure as a retention engine and pricing transparency as a procedural safeguard.
Optimasi Seleksi Ekstrakurikuler Siswa Menggunakan Metode Profile Matching: Studi Kasus di SMP Negeri 1 Kerinci M. Iqbal Zuqron; Sarjon Defit; Gunadi Widi Nurcahyo
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.v7i3.2211

Abstract

Penerapan metode Profile Matching dalam pengelompokan minat dan bakat ekstrakurikuler siswa di SMP Negeri 1 Kerinci. Pemilihan ekstrakurikuler yang tepat bagi siswa merupakan tantangan tersendiri bagi sekolah, terutama karena belum adanya sistem pendukung keputusan yang terkomputerisasi. Selama ini, pemilihan dilakukan secara manual berdasarkan aspek tinggi badan, berat badan, fleksibilitas, dan kecepatan, yang sering kali tidak objektif dan memakan waktu lama. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem berbasis komputer yang dapat membantu menentukan ekstrakurikuler siswa secara lebih efektif dan efisien. Metode Profile Matching digunakan untuk mencocokkan kompetensi individu dengan standar kompetensi ekstrakurikuler. Proses ini dilakukan dengan mengidentifikasi gap antara nilai profil siswa dan nilai target yang telah ditentukan untuk setiap ekstrakurikuler. Perhitungan dilakukan dengan menentukan bobot pada faktor utama (core factor) dan faktor pendukung (secondary factor), yang masing-masing diberi persentase pengaruh sebesar 60% dan 40%. Dari hasil perhitungan, sistem dapat secara otomatis merekomendasikan ekstrakurikuler yang paling sesuai untuk setiap siswa. Hasil penelitian menunjukkan bahwa sistem berbasis Profile Matching ini dapat meningkatkan akurasi pemilihan ekstrakurikuler hingga 85% dibandingkan dengan metode manual. Selain itu, implementasi sistem berbasis web dengan bahasa pemrograman PHP membantu mempercepat proses seleksi dan meminimalkan subjektivitas dalam pengambilan keputusan. Dengan adanya sistem ini, diharapkan proses seleksi ekstrakurikuler dapat dilakukan dengan lebih objektif, akurat, dan efisien. Persentase keakuratan: 85% (berdasarkan perhitungan metode dan hasil perbandingan dengan sistem manual).
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.
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.
Analysis of Clean Water Consumption Segmentation And Classification Using K-Means Clustering And Random Forest Algorithms Ika Melinia Sapitri Fitriyanti; Sarjo Defit; Rini Sovia
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

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

Abstract

The administrative grouping of PERUMDA Air Minum Kota Padang customers is not yet able to accurately represent actual customer water consumption patterns. This condition makes it difficult for the company to formulate service policies, customer management, and make appropriate data-based decisions. This study aims to analyze and map customer water consumption patterns to produce more representative customer segmentation as a basis for decision making. The research method used is a data mining approach with the application of Principal Component Analysis (PCA) for dimension reduction, K-Means Clustering for customer segmentation, and Random Forest for customer classification, using primary data from the Padang City Water Company's Customer Meter Reading Report with an initial amount of 371 data. The results of the study show that the clustering process successfully formed three customer segments, namely premium customers with high consumption bills, regular customers with moderate and stable consumption, and new customers with low consumption rates. The evaluation of the Random Forest model's performance resulted in an accuracy rate of 68.85% on the training data and 67.69% on the testing data, with an average precision value above 0.84 and an average F1-score value of around 0.68. The consistency of performance between the training data and the testing data shows that the model has fairly good generalization capabilities and does not experience overfitting.
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.
Co-Authors Abdul Azis Said Abuzar Gafari 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 Antoni Antoni 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 Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Dzil Hidayati Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Eka Sofianti Elda, Yusma Elfiswandi, Elfiswandi eriwandi Eva Rianti Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza 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 Firna Yenila 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 Gunadi Widi Nurcahyo, Gunadi Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ika Melinia Sapitri Fitriyanti Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arif 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 Junadhi, Junadhi Kamelia Sari, Rima Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Larissa Navia Rani, Larissa 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 Habib Yuhandri 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 Okfalisa Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania 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 Refina Afindania, Pipin Resnawita, R Retno Devita Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Richi Andrianto Rico Anggara Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila 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, 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 Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yuli Hartati Yulihartati, Sandra Yusma Elda Z Zulvitri Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z