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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL SISTEM INFORMASI BISNIS Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Jurnal Informatika Upgris E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JIKO (Jurnal Informatika dan Komputer) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL ILMIAH INFORMATIKA SINTECH (Science and Information Technology) Journal Jurnal Infomedia Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Revolusi Indonesia JiTEKH (Jurnal Ilmiah Teknologi Harapan) IJISTECH RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT DEVICE Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Penelitian Inovatif BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Krisnadana STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Krisnadana Journal of Informatics, Electrical and Electronics Engineering
Wanto, Anjar
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia.
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Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia Andriani, Yuli; Silitonga, Hotmalina; Wanto, Anjar
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 4, No 1 (2018): January-June
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1572.598 KB) | DOI: 10.26594/register.v4i1.1157

Abstract

Analisis pada penelitian penting dilakukan untuk tujuan mengetahui ketepatan dan keakuratan dari penelitian itu sendiri. Begitu juga dalam prediksi volume ekspor dan impor migas di Indonesia. Dilakukannya penelitian ini untuk mengetahui seberapa besar perkembangan ekspor dan impor Indonesia di bidang migas di masa yang akan datang. Penelitian ini menggunakan Jaringan Syaraf Tiruan (JST) atau Artificial Neural Network (ANN) dengan algoritma Backpropagation. Data penelitian ini bersumber dari dokumen kepabeanan Ditjen Bea dan Cukai yaitu Pemberitahuan Ekspor Barang (PEB) dan Pemberitahuan Impor Barang (PIB). Berdasarkan data ini, variabel yang digunakan ada 7, antara lain: Tahun, ekspor minyak mentah, impor minyak mentah, ekspor hasil minyak, impor hasil minyak, ekspor gas dan impor gas. Ada 5 model arsitektur yang digunakan pada penelitian ini, 12-5-1, 12-7-1, 12-8-1, 12-10-1 dan 12-14-1. Dari ke 5 model yang digunakan, yang terbaik adalah 12-5-1 dengan menghasilkan tingkat akurasi 83%, MSE 0,0281641257 dengan tingkat error yang digunakan 0,001-0,05. Sehingga model ini bagus untuk memprediksi volume ekspor dan impor migas di Indonesia, karena akurasianya antara 80% hingga 90%.   Analysis of the research is Imporant used to know precision and accuracy of the research itself. It is also in the prediction of Volume Exports and Impors of Oil and Gas in Indonesia. This research is conducted to find out how much the development of Indonesias exports and Impors in the field of oil and gas in the future. This research used Artificial Neural Network with Backpropagation algorithm. The data of this research have as a source from custom documents of the Directorate General of Customs and Excise (Declaration Form/PEB and Impor Export Declaration/PIB). Based on this data, there are 7 variables used, among others: Year, Crude oil exports, Crude oil Impors, Exports of oil products, Impored oil products, Gas exports and Gas Impors. There are 5 architectural models used in this study, 12-5-1, 12-7-1, 12-8-1, 12-10-1 and 12-14-1. Of the 5 models has used, the best models is 12-5-1 with an accuracy 83%, MSE 0.0281641257 with error rate 0.001-0.05. So this model is good to predict the Volume of Exports and Impors of Oil and Gas in Indonesia, because its accuracy between 80% to 90%.
PENERAPAN ALGORITMA CLUSTERING DALAM MENGELOMPOKKAN BANYAKNYA DESA/KELURAHAN MENURUT UPAYA ANTISIPASI/ MITIGASI BENCANA ALAM MENURUT PROVINSI DENGAN K-MEANS Sadewo, Mhd Gading; Windarto, Agus Perdana; Wanto, Anjar
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.348 KB) | DOI: 10.30865/komik.v2i1.943

Abstract

Natural disasters are natural events that have a large impact on the human population. Located on the Pacific Ring of Fire (an area with many tectonic activities), Indonesia must continue to face the risk of volcanic eruptions, earthquakes, floods, tsunamis. Application of Clustering Algorithm in Grouping the Number of Villages / Villages According to Anticipatory / Natural Disaster Mitigation Efforts by Province With K-Means. The source of this research data is collected based on documents that contain the number of villages / kelurahan according to natural disaster mitigation / mitigation efforts produced by the National Statistics Agency. The data used in this study is provincial data consisting of 34 provinces. There are 4 variables used, namely the Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Line. The data will be processed by clustering in 3 clushter, namely clusther high level of anticipation / mitigation, clusters of moderate anticipation / mitigation levels and low anticipation / mitigation levels. The results obtained from the assessment process are based on the Village / Kelurahan index according to the Natural Disaster Anticipation / Mitigation Efforts with 3 provinces of high anticipation / mitigation levels, namely West Java, Central Java, East Java, 9 provinces of moderate anticipation / mitigation, and 22 other provinces including low anticipation / mitigation. This can be an input to the government, the provinces that are of greater concern to the Village / Village According to the Natural Health Disaster Mitigation / Mitigation Efforts based on the cluster that has been carried out.Keywords: Data Mining, Natural Disaster, Clustering, K-Means
ANALISA PEMILIHAN BARISTA DENGAN MENGGUNAKAN METODE TOPSIS (STUDI KASUS: MO COFFEE) Hutasoit, Rahel Adelina; Solikhun, Solikhun; Wanto, Anjar
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.348 KB) | DOI: 10.30865/komik.v2i1.935

Abstract

Along with the mushrooming of food & beverrage business in Pematangsiantar city, especially in this case is a coffee shop making a barista a bone of contention for business people in the world of food & beverrage business. This makes business people still feel confused and need information to decide to employ a barista that suits their wishes. The purpose of the study was to analyze the TOPSIS method in determining the selection of baristas with 4 alternatives, namely (A1) Alfian, (A2) Widharta, (A3) Sylviana, and (A3) Anisah. And has 6 assessment criteria, namely (C1) the ability to mix coffee, (C2) know coffee and its intricacies, (C3) taste ability, (C4) work experience, (C5) master the use of a set of coffee machine tools and accessories, and (C6) skill in making latte art. The data obtained will be processed using the TOPSIS method. The results of the study obtained (A3) Widharta with the preference weights (0,6126) as the first rank, followed by the second and third ranks (A2) Sylviana with preference weights (0,4980) and (A1) Disagree with preference weights (0.4597) . It is hoped that this research can help or provide input to Mo Coffee owner in choosing baristas to be employed.Keywords: Barista, TOPSIS, Decision Support System, Pematangsiantar, Assessment Factor, Mo Coffee
REKOMENDASI PENJUALAN AKSESORIS HANDPHONE MENGGUNAKAN METODE ANALITYCAL HIERARCHY PROCESS (AHP) Widyasuti, Meilin; Wanto, Anjar; Hartama, Dedy; Purwanto, Eko
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 1, No 1 (2017): Intelligence of Cognitive Think and Ability in Virtual Reality
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.348 KB) | DOI: 10.30865/komik.v1i1.468

Abstract

Talk about the lifestyle that is now growing, it also affects the appearance of mobile phones that are owned by everyone that is more complete mobile accessories and create attractive appearance. Along with the time mobile phone accessories business is also experiencing a fairly rapid development, not even just a store product that sells mobile phone accessories but also has sold the counter. Here the researchers want to examine the recommendation of mobile accessories, where the selected accessories are the accessories of the most popular consumers based on store ratings. The rise of accessories business is proving that the mobile phone accessories still deserve to be a good opportunity. Some telemonication experts predict that mobile users are increasing the number of populations. This can be an accessory business that has good prospects in the future. Based on the results of research using AHP method with 6 samples of best selling mobile accessories, where the data obtained based on the results of interviews with mobile shops in the city Pematangsiantar, obtained the calculation of AHP method for handpone accessories recommendation is 1. Led Selfie (34%), 2. Gopro (25%), 3. Phone Ring (20%), 4. Scean Guard (16%), 5. Charge Wireless (14%) and 6. Handset (10%)
IMPLEMENTASI RAPIDMINER DENGAN METODE K-MEANS (STUDY KASUS: IMUNISASI CAMPAK PADA BALITA BERDASARKAN PROVINSI) Sari, Riyani Wulan; Wanto, Anjar; Windarto, Agus Perdana
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.348 KB) | DOI: 10.30865/komik.v2i1.930

Abstract

Measles is one of the causes of death in children around the world which always increases every year. Although measles immunization programs have been implemented, the incidence of measles in children is still quite high. This study discusses the Implementation of Rapidminer with the K-Means Method (Case Study: Measles Immunization in Toddlers by Province). The increase in cases of measles in toddlers in Indonesia is a case that has never been separated from the governments attention. Data sources and research were obtained from the Central Statistics Agency (BPS). The data used in this study are data from 2004-2017 which consists of 34 provinces. The cluster process is divided into 3 (three) clusters, namely high cluster level (C1), medium cluster level (C2) and low cluster level (C3). So that the assessment for cases of immunization against measles based on high cluster province (C1) is 21 provinces for medium cluster (C2) of 12 provinces and for low cluster (C3) of 1 province. The results of the cluster can be used as input for the government, especially the provinces, so that provinces that enter the high cluster receive more attention and increase the socialization of measles immunization against children under five. Keywords: Data Mining, Measles, Clustering, K-means
PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA SEBAGAI UPAYA ANTISIPASI IMPOR MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION Wanto, Anjar
SINTECH (Science and Information Technology) Journal Vol 2 No 1 (2019): SINTECH Journal Edisi April 2019
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1128.657 KB) | DOI: 10.31598/sintechjournal.v2i1.355

Abstract

Corn is a staple food that is still widely consumed by the population of Indonesia. Based on data from the Indonesian Statistics Agency, corn productivity in Indonesia from 2005 to 2015 calculated an unstable curve. Therefore this research was conducted to predict and see the large growth of maize in Indonesia for the following years so that the government has a reference to continuously strive to increase corn productivity in Indonesia in order to remain stable in order to meet the needs of Indonesian people to minimize corn imports. This study uses data on corn productivity in Indonesia in 2005-2015 sourced from the Indonesian Central Bureau of Statistics. The prediction algorithm used is the Backpropagation Neural Network. This algorithm is able to predict data well, especially data that is maintained for a certain period of time. To facilitate data analysis, the author uses the Matlab 2011b application. In this study, a training and testing process will be carried out using 5 network architecture models, namely 5-25-1, 5-43-1, 5-76-1, 5-78-1 and 7-128-1. Of the 5 architectural models, the best is 5-25-1 with the percentage of 88% and the MSE value of 0.00992433.
Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed Saputra, Widodo; Hardinata, Jaya Tata; Wanto, Anjar
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 1 (2019): EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (218.484 KB) | DOI: 10.31289/jite.v3i1.2704

Abstract

Unemployment is a big problem faced by the Indonesian people from year to year besides poverty. Therefore it is necessary to predict the level of open unemployment in Indonesia so that later the government and private parties have the right references and references to work together to overcome this problem. The prediction method used is Resilient Backpropagation which is one method of Artificial Neural Networks which is often used for data prediction. The research data used is open unemployment data according to the highest education completed in 2005-2018 based on the semester obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18 -2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0.0022135087 and 0.0044974696
PROYEKSI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA MENGGUNAKAN METODE STATISTICAL PARABOLIC DALAM MENYONGSONG REVOLUSI INDUSTRI 4.0 Kirana, Ika Okta; Nasution, Zulaini Masruro; Wanto, Anjar
Jurnal Pendidikan Teknologi dan Kejuruan Vol 16, No 2 (2019): Edisi Juli 2019
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptk-undiksha.v16i2.18178

Abstract

Indeks Pembangunan Manusia (IPM) merupakan indikator yang sangat penting dalam mengukur keberhasilan sebuah negara dalam membangun kualitas hidup penduduk/masyarakat nya, termasuk Indonesia. Ekonomi global saat ini sedang pada titik puncak perubahan besar yang sebanding besarnya dengan munculnya revolusi industri 4.0. Penentuan peringkat atau level pembangunan dan ekonomi dari suatu wilayah atau negara dapat dilihat dari IPM. Karena begitu pentingnya Indeks Pembangunan Manusia (IPM), maka perlu dilakukan proyeksi tingkat perkembangan IPM di tahun-tahun selanjutnya, agar pemerintah Indonesia memiliki referensi dan acuan yang jelas untuk menentukan kebijakan ataupun membuat langkah-langkah strategis yang tepat agar Indeks Pembangunan Manusia (IPM) jangan sampai menurun di masa yang akan datang, bahkan meningkat pada tiap tahunnya. Data yang akan diproyeksi pada penelitian ini adalah data Indeks Pembangunan Manusia (IPM) tahun 2010-2018. Sumber data diambil dari Badan Pusat Statistik (BPS) Indonesia. Pada penelitian ini, metode proyeksi yang digunakan untuk melihat perkembangan IPM di Indonesia adalah Statistical Parabolic Projection (Trend Parabolik). Setelah dilakukan perhitungan, diperoleh selisih antara data asli IPM dengan data hasil proyeksi sangat dekat sekali, dengan tingkat MSE sebesar 0,01659. Sehingga dapat disimpulkan bahwa metode Trend Parabolik sangat baik digunakan untuk melakukan proyeksi Indeks Pembangunan Manusia. Oleh karena itu hasil penelitian ini adalah proyeksi Indeks Pembangunan Manusia (IPM) di Indonesia untuk tahun 2019 hingga tahun 2027
Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids dan Backpropagation pada kasus Pandemi Covid-19 di Indonesia Windarto, Agus Perdana; Na`am, Jufriadif; Yuhandri, Yuhandri; Wanto, Anjar; Mesran, Mesran
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2505

Abstract

The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.
Optimalisasi Parameter dengan Cross Validation dan Neural Back-propagation Pada Model Prediksi Pertumbuhan Industri Mikro dan Kecil Windarto, Agus Perdana; Defit, Sarjon; Wanto, Anjar
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 11, No 1 (2021): Volume 11 Nomor 1 Tahun 2021
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol11iss1pp34-42

Abstract

It is important for us to predict what will happen in future and to reduce uncertainty. Various analyzes are therefore necessary in order to optimize or improve the prediction results by several methods. The objective of this research is to analyze predictive results by optimizing the training and testing by means of cross validating parameters on the growth of micro and small-scale production in Indonesia through the exactness of the return-propagative method. The method of reproduction is used. These results are compared with results of backpropagation during training and testing without optimisation of the same architectural model. The dataset is based on the growth in production in micro and small businesses by province from the Central Statistical Agency(BPS). There were 34 records in which data from 2015-2019 for growth of production were collected. The results with optimisation have surpassed without optimisation the back propagation model by looking at RMSE, in which the best RMSE in the 3-2-1 architectural model was obtained and the side type is mixed sampling. The obtained RMSE value is 0.1526, or a difference between the best background architectural model, 3-2-1 and 0.0034. (0.157). The results of this model were 94 percent.
Co-Authors Abdi Rahim Damanik Agung Pratama Agung Wibowo Agung Yusuf Pratama Agus Perdana Windarto Agus Perdana Windarto Andi Sanggam Sidabutar Arifah Hanum Asro Pradipta Ayu Artika Fardhani Azwar Anas Manurung Azwar Anas Manurung Bahrudi Efendi Damanik Bil Klinton Sihotang Cici Astria Daniel Sitorus Dedi Kusbiantoro Dedi Suhendro Dedi Suhendro Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Deri Setiawan Desi Insani Natalia Simanjuntak Dewi, Rafiqa Dinda Nabila Batubara Edu Wardo Saragih eko hartato Eko Hartato Eko Kurniawan Eko Purwanto Elfin Efendi Eva Desiana Fajar Ramadan Fikri Yatussa’ada Fitri Anggraini Flora Sabarina Napitupulu GS , Achmad Daengs Gumilar Ramadhan Pangaribuan Hartama, Dedy Hartama, Dedy Herlan F Silaban Heru Satria Tambunan Heru Satria Tambunan Hutasoit, Rahel Adelina Hutasoit, Rahel Adelina Ihsan Maulana Muhamad Iin Parlina Iin Parlina Iin Parlina Iin Parlina Iin Parlina Iin Parlina Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Purnama Sari Ilham Syahputra Saragih Imelda Asih Rohani Simbolon Indra Gunawan Indra Gunawan Indra Gunawan Indra Gunawan Indra Satria Indra Satria Indri Sriwahyuni Purba Irawan Irawan Irfan Christian Saragih Irfan Sudahri Damani, Irfan Sudahri Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jaya T Hardinata Jaya Tata Hardinata Jonas Rayandi Saragih Jonas Rayandi Saragih Joni Wilson Sitopu Jufriadif Na`am, Jufriadif Juli Wahyuni Khairunnissa Fanny Irnanda Kirana, Ika Okta M Mesran M Safii M.Ridwan Lubis Marseba Situmorang Martina Silaban Mesran, Mesran Meychael Adi Putra Hutabarat Mhd Ali Hanafiah Mhd Gading Sadewo Mhd. Billy Sandi Saragih Mhd. Buhari Sibuea Mora Malemta Sitomorang Muhammad Aliyul Amri Muhammad Aliyul Amri Muhammad Julham Muhammad Julham Muhammad Mahendra Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Syafiq Muhammad Wijaya Nasution, Rizki Alfadillah Nasution, Zulaini Masruro Ni Luh Wiwik Sri Rahayu Ginantra Nur Ahlina Febriyati Nur Arminarahmah Nur Arminarahmah Nuraysah Zamil Purba Nurhayati Nurhayati Okprana, Harly Okta Andrica Putra Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih R Tri Hadi Febriyanto Rahmat W Sembiring Rahmat W. Sembiring Rapianto Sinaga Ratih Puspadini Reza Pratama Rita Mawarni Rizky Khairunnisa Sormin Ronal Watrianthos Roulina Simarmata Roy Chandra Telaumbanua Ruri Eka Pranata S Solikhun S Solikhun S Sumarno Sadewo, Mhd Gading Safruddin Safruddin Saifullah Saifullah Samuel Palentino Sinaga Samuel Palentino Sinaga Sandy Putra Siregar Saragih, Irfan Christian Saragih, Jonas Rayandi Saragih, Mhd. Billy Sandi Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Sigit Anugerah Wardana Silfia Andini Silitonga, Hotmalina Silitonga, Hotmalina Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Suhada Suhada Suhada Suhada Sumarno Sumarno Sumarno Sumarno Sumarno Sumarno Sundari Retno Andani Sunil Setti Surya Hendraputra Susi Fitryah Damanik Syafri Maradu Manurung Syafrika Deni Rizki Syahri Ramadhan Tia Imandasari Titin Handayani Sinaga Tri Febri Damayanti Tri Welanda Vasma Vitriani Sianipar Venny Vidya utari Vitri Roma Sari Wida Prima Mustika Widodo Saputra Widya Tri Charisma Gultom Widyasuti, Meilin Widyasuti, Meilin Winanjaya, Riki Yuhandri Yuhandri, Yuhandri Yuli Andriani Yuri Widya Paranthy Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulia Almaida Siregar