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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

Author-ID : 2476220

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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Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa Syofneri, Nandel; Defit, Sarjon; Sumijan
Jurnal Informasi dan Teknologi 2019, Vol. 1, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v1i4.13

Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.

Implementation Of The ARIMA Method In Predicting LQ 45 Stock Prices (UNTR Issuer) Hadiyanto, Tegas; Defit, Sarjon; Sovia, Rini
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5656

The implementation of technology is used in running businesses or activities that generate profits, such as predicting investments on the stock exchange through transaction data in the transaction data base. Machine learning is an algorithm that produces an approximation function that connects input variables so that it has the potential to be implemented in stock predictions. Stock investment has the characteristics of high risk - high return. Losses are caused by investors' lack of knowledge. Stock value analysis is divided into two, namely fundamental analysis and technical analysis. Technical analysis uses data or records about the market to try to access the demand and supply of a particular stock or the market as a whole. Based on the problems found by investors or bankers, this research will use the autoregressive integrated moving average (ARIMA) method to predict stock price movements. The Arima method consists of four stages, namely identifying time series methods, estimating parameters for alternative methods, testing methods and estimating time series values. Based on these problems, the ARIMA method will be used to predict stock movements. The Arima model (1,0,2) with RMS: 2200.576849857124 successfully predicted for the next 180 days

Early Stopping on CNN-LSTM Development to Improve Classification Performance Anam, M. Khairul; Defit, Sarjon; Haviluddin, Haviluddin; Efrizoni, Lusiana; Firdaus, Muhammad Bambang
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.312

Currently, CNN-LSTM has been widely developed through changes in its architecture and other modifications to improve the performance of this hybrid model. However, some studies pay less attention to overfitting, even though overfitting must be prevented as it can provide good accuracy initially but leads to classification errors when new data is added. Therefore, extra prevention measures are necessary to avoid overfitting. This research uses dropout with early stopping to prevent overfitting. The dataset used for testing is sourced from Twitter; this research also develops architectures using activation functions within each architecture. The developed architecture consists of CNN, MaxPooling1D, Dropout, LSTM, Dense, Dropout, Dense, and SoftMax as the output. Architecture A uses default activations such as ReLU for CNN and Tanh for LSTM. In Architecture B, all activations are replaced by Tanh, and in Architecture C, they are entirely replaced by ReLU. This research also performed hyperparameter tuning such as the number of layers, batch size, and learning rate. This study found that dropout and early stopping can increase accuracy to 85% and prevent overfitting. The best architecture entirely uses ReLU activation as it demonstrates advantages in computational efficiency, convergence speed, the ability to capture relevant patterns, and resistance to noise.

ANALYSIS OF CUSTOMER BEHAVIOR USING THE APRIORI METHOD Septiano, Renil; Defit, Sarjon; Sari, Laynita
Dinasti International Journal of Digital Business Management Vol. 2 No. 4 (2021): Dinasti International Journal of Digital Business Management (June - July 2021)
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/dijdbm.v2i4.883

Data is a very valuable asset because it is able to provide accurate, easy and fast information. This study processes coffee shop business data in the city of Padang to determine how customers behave in choosing menus. From the results of data processing. Researchers processed the data using the a priori method. From the results of data processing at a support value of 15%, it is found that the majority of customers still buy menus in units.

Pemetaan Promosi dalam Penjaringan Calon Mahasiswa Menggunakan Algoritma Backpropagation Kurniawan, Mhd Hary; Defit, Sarjon
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 1 (March 2020)
Publisher : SAFE-Network

Promotion requires a large fee if it is not targeted when doing it. Backpropagation is an excellent method of dealing with the problem of recognizing complex patterns. Backprogation neural network each unit in the input layer is connected to each unit in the hidden layer. Student data from 2014 to 2018 is a comparison point. The results of testing of this method are calculations using a sample value of 5 years before using a comparative value of 2014 to 2018 totaling 602 data. This research uses 5-5-1 architecture, epoch 2000 and learning rate so that the data accuracy reaches 71% with an error value of 0.0099. The results of this study are 16 districts that become promotion recommendations. Ordering of forecasting the highest number of students to the smallest number of students, so it can be concluded that this method is very useful in mapping promotions.

Optimalisasi Penggunaan Lahan Perkebunan Kelapa Hibrida Menggunakan K-Means Clustering Andema, Henky; Defit, Sarjon; Yunus, Yuhandri
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 2 (June 2020)
Publisher : SAFE-Network

Plantations are the main source of income for farmers in Indragiri Hilir Regency. This plantation is the plantation sector most widely cultivated by farmers is a coconut plantation. The best grouping of coconut cultivation areas is important in developing farmers' income. This study aims to help the Plantation Office in the process of making the best decision areas for planting coconut, especially hybrid coconut. The data used in this study is the data of hybrid coconut plantations in 2018. Data processing in this study uses the K-Means Clustering method with the number of 3 Clusters namely Cluster 0 (C0) Less Potential, Cluster 1 (C1) Enough Potential, Cluster 2 (C2) Very Potential for planting hybrid coconuts. The results of the clustering process with 2 iterations stated that for Cluster 0 there were 7 village data, for Cluster 1 there were 1 village data, and for Cluster 2 there were 2 village data.

Prediksi Tingkat Ketersediaan Stock Sembako Menggunakan Algoritma FP-Growth dalam Meningkatkan Penjualan Aditiya, Rahmad; Defit, Sarjon
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (September 2020)
Publisher : SAFE-Network

Large data sets can be processed to become useful information, one of the data that can be processed is sales transaction data at UD. Smart Aliwansyah, which will become important information to increase sales. This study aims to find the pattern of product purchases to predict the level of availability of staple foods so as to increase sales. The data that is processed in this study uses the sales transaction data of goods obtained from the sales invoice of UD. Smart Aliwansyah, North Sumatra Tax Village. Based on these data, with the provision that a minimum of 2 types of goods in 1 transaction is examined using a data mining technique in association with the FP-Growth algorithm with a confidence value of 75% and a minimum support of 20%. The tools used by Rapidminer 9.4 are to obtain product purchasing patterns which are used as information to predict the level of stock availability. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing basic food stocks to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by grocery stores.

Sistem Pendukung Keputusan Menggunakan Metode Simple Additive Weighting dalam Meningkatkan Pendapatan Jasa Fotografi Bufra, Fanny Septiani; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 4 (December 2020)
Publisher : SAFE-Network

The photography business grew very rapidly and was very profitable. The intense competition made the photo studio suffer losses and even went out of business because it was unable to compete and made wrong decisions. Like during the Covid-19 Pandemic in 2020, several photo studios experienced a decline in revenue because there were no bookings for photo services or canceling agreed projects. The purpose of this study is to assist the owner of a photo studio or photographer in determining the best decision from an investment plan that has been planned based on predetermined criteria in order to increase photography service income. In this study using the Simple Additive Weighting method. The variables that are the main criteria in this decision-making system are Cost, Productivity, Priority Needs, and Availability. The alternative data used is the Photo studio Investment Plan data in July 2020. Based on the results of the calculations using the Simple Additive Weighting method, the results show that Alternative 1, namely Paid Promotion on Social Media, is recommended as the best decision with the highest preference value of the 12 sample data. tested is 0.93. Comparison of data from manual counting with the system created, namely the Website-based Decision Support System, resulted in the same calculation value. So that the accuracy value is 100% and is declared accurate. With this Decision Support System, it can produce objective decisions to assist owners in determining investment plans that can increase income from photography services. Bisnis fotografi tumbuh sangat pesat dan sangat menghasilkan. Ketatnya persaingan membuat studio foto mengalami kerugian bahkan sampai gulung tikar karena tidak mampu bersaing dan salah dalam mengambil keputusan. Seperti pada masa Pandemi Covid-19 ditahun 2020, beberapa studio foto mengalami penurunan pendapatan karena tidak adanya yang booking jasa foto ataupun membatalkan project yang telah disepakati. Tujuan dari penelitian ini adalah untuk membantu owner studio foto atau fotografer dalam menentukan keputusan terbaik dari rencana investasi yang sudah direncanakan berdasarkan kriteria yang telah ditentukan agar dapat meningkatkan pendapatan jasa fotografi. Penelitian ini menggunakan metode Simple Additive Weighting. Variabel yang menjadi kriteria utama pada Sistem Pengambilan Keputusan ini yaitu Biaya, Produktivitas, Prioritas Kebutuhan, dan Ketersediaan. Data alternatif yang digunakan yaitu data Rencana Investasi studio Foto pada bulan Juli 2020. Berdasarkan hasil dari perhitungan dengan menggunakan metode Simple Additive Weighting ini, didapatkan hasil bahwa Alternatif 1 yaitu Promosi Berbayar di Sosial Media direkomendasikan sebagai keputusan terbaik dengan nilai preferensi tertinggi dari 12 data sampel yang diuji yaitu 0.93. Dilakukan perbandingan data dari hitungan manual dengan sistem yang dibuat yaitu Sistem Pendukung Keputusan berbasis Website menghasilkan nilai perhitungan yang sama. Sehingga nilai keakurasiannya adalah 100% dan dinyatakan akurat. Dengan adanya Sistem Pendukung Keputusan ini dapat menghasilkan keputusan objektif untuk membantu owner dalam menentukan rencana investasi yang dapat meningkatkan pendapatan jasa fotografi.

Klasterisasi Bibit Terbaik Menggunakan Algoritma K-Means dalam Meningkatkan Penjualan Hartati, Yuli; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 1 (March 2021)
Publisher : SAFE-Network

Tiara Bersaudara is a shop that sells seeds and agricultural needs. To maintain a stock of seeds that farmers are interested in, sellers must be able to analyze seed sales data. This process is difficult to do because UD has a lot of sales data. The existing problem can be solved by clustering seed sales data. Clustering is grouping data into several clusters based on the level of data similarity. The research objective was to group the best-selling seedlings in UD.Tiara Bersaudara in increasing sales. Seed sales data from January to April 2019 are data that will be processed in this study. The clustering method uses the K-Means algorithm by partitioning the data into clusters based on the closest centroid to the data. Then the test is done by comparing the calculation results with the Rapid Miner studio 9.7 software. Clustering is tested based on lots of data and many clusters. The data tested were 42 seedlings by obtaining 2 clusters, 4 data which were best-selling seeds as cluster one (C1), and 38 data which were unsold seeds as cluster two (C2). Best-selling seeds are the best seeds that can increase sales consisting of Bibit Jagung NK 212, Bibit Jagung NK 7328, bibit Jagung Pioneer 32, Bibit Jagung NK 617232. The results of this study can be used as benchmarks for decision support by UD.Tiara Berasaudara to set up a marketing strategy to increase sales.

Klasterisasi Dana Bantuan Pada Program Keluarga Harapan (PKH) Menggunakan Metode K-Means Said, Abdul Azis; Defit, Sarjon; Yunus, Yuhandri
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 2 (June 2021)
Publisher : SAFE-Network

The Family of Hope Program (PKH) is a program that aims to reduce poverty and improve the quality of human resources. Optimizing the provision of assistance in accordance with the expectations of those in need. Data on the poor or integrated social welfare data is needed as a reference for grouping. This study aims to make it easier for the selection team to provide assistance in accordance with the predetermined criteria whether or not they deserve to receive the assistance. The data used in the study is data from 2019. The data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 1 (C1) Nearly Poor Households (RTHM), Cluster 2 (C2) Poor Households (RTM), Cluster 3 (C3) Very Poor Households (RTSM). The results of the clustering process with 2 iterations state that for Cluster 1 the amount of data is, for Cluster 2 the amount of data, and for Cluster 3 the amount of data. So this research is very helpful in relocating targeted assistance according to the family hope cluster.

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

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