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Agus Perdana Windarto
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing di Indonesia Rika Setiana; Razalfa Aindi Siregar; Fahry Husaini; Agus Perdana Windarto
Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 4 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v2i4.1177

Abstract

A science that has always developed rapidly until now is artificial neural networks. A computational science that works like the human nervous system is an artificial neural network. Artificial neural networks with the backpropagation method can make a prediction on data. In this article, a prediction will be made on the amount of goat production in Indonesia. Goats are one of the livestock that can produce nutritious meat. The lack of goat meat will cause the price of goat meat to rise. Producing enough goat meat helps stabilize the price of meat, but if goat meat production is less than demand, it will lead to price increases. Therefore, looking at the problems above, this study aims to predict goat meat so that in the future it can know how much goat meat must be predicted by processing data first and then being used as input in predicting the amount of goat meat production. Prediction is one way to estimate future demand. Avoiding the lack of meat availability, by predicting the amount of goat meat produced in such a way that there is no scarcity of goat meat and fluctuations in the price of goat meat in the market. Basic methods and data are required to make predictions. In this study, data was obtained from BPS Indonesia in the livestock section using data from 2001-2021 as training data and 2002-2022 as test data. The method applied in this article is the backpropagation algorithm. This article applies 5 network architectures implemented in the mathlab application. The architecture used in this article is 20-25-1 with a Mean Squred Error testing 0.00447765, in 20-30-1 architecture produces Mean Squred Error 0.00300466, in 20-35-1 architecture produces 0.00426823, in 20-37-1 architecture produces 0.00357757. Based on the best architecture produced in this study, the 20-15-1 architecture with 90% accuracy with a Mean Squared Error testing 0.00262384 at epoch 27915 Iterations. Thus it can be concluded that the backpropagation algorithm can provide good accuracy in the prediction process. With this research, the livestock industry can utilize it as one of the materials to predict goat meat in the future
SPK: ANALISA METODE MOORA PADA WARGA PENERIMA BANTUAN RENOVASI RUMAH Chintya Irwana; Zaki Faizin Harahap; Agus Perdana Windarto
Jurnal Teknologi Informasi Mura Vol 10 No 1 (2018): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v10i1.290

Abstract

AbstrakRumah berperan sebagai wadah untuk bernaung dan tempat berlindung bagi manusia, bukan hanya saja memiliki fungsi sebagai tempat tinggal semata, rumah juga berfungsi  untuk tempat pembinaan dan bercengkrama suatu keluarga. Kemiskinan merupakan dampak buruk bagi beberapa orang untuk berhasrat memiliki rumah yang layak. Dengan adanya program bantuan renovasi rumah yang diselenggarakan oleh pemerintah daerah Kecamatan Hatonduhan merupakan upaya yang dilaksanakan pemerintah dalam mengentaskan kemiskinan. Namun terjadi permasalahan dalam penyeleksian bagi warga yang berhak menerima bantuan dana tersebut. Penyeleksian hanya dilakukan hanya dengan penilaian secara subjektif tanpa mempertimbangkan penilaian objektif yang memiliki kriteria yang telah ditentukan meliputi pekerjaan, penghasilan/bulan, jenis dinding, jenis lantai, MCK, dan jenis atap. Hal tersebut diperlukannya sebuah Sistem Pendukung Keputusan (SPK) dalam mengatasi penyeleksian program penerima bantuan terhadap warga yang layak direnovasi. Dengan penggunaan metode MOORA yang  merupakan salah satu  SPK yang berafiliasi dengan penerapan  teknik optimasi multiobjective sehingga dapat diterapkan untuk memecahkan berbagai masalah dalam pengambilan keputusan. Kata Kunci : Renovasi, Rumah, Sistem Pendukung Keputusan, SPK, MOORA
Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing Di Indonesia Rika Setiana; Razalfa Aindi Siregar; Fahry Husaini; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.854

Abstract

A science that has always developed rapidly until now is artificial neural networks. A computational science that works like the human nervous system is an artificial neural network. Artificial neural networks with the backpropagation method can make a prediction on data. In this article, a prediction will be made on the amount of goat production in Indonesia. Goats are one of the livestock that can produce nutritious meat. The lack of goat meat will cause the price of goat meat to rise. Producing enough goat meat helps stabilize the price of meat, but if goat meat production is less than demand, it will lead to price increases. Therefore, looking at the problems above, this study aims to predict goat meat so that in the future it can know how much goat meat must be predicted by processing data first and then being used as input in predicting the amount of goat meat production. Prediction is one way to estimate future demand. Avoiding the lack of meat availability, by predicting the amount of goat meat produced in such a way that there is no scarcity of goat meat and fluctuations in the price of goat meat in the market. Basic methods and data are required to make predictions. In this study, data was obtained from BPS Indonesia in the livestock section using data from 2001-2021 as training data and 2002-2022 as test data. The method applied in this article is the backpropagation algorithm. This article applies 5 network architectures implemented in the mathlab application. The architecture used in this article is 20-25-1 with a Mean Squred Error testing 0.00447765, in 20-30-1 architecture produces Mean Squred Error 0.00300466, in 20-35-1 architecture produces 0.00426823, in 20-37-1 architecture produces 0.00357757. Based on the best architecture produced in this study, the 20-15-1 architecture with 90% accuracy with a Mean Squared Error testing 0.00262384 at epoch 27915 Iterations. Thus it can be concluded that the backpropagation algorithm can provide good accuracy in the prediction process. With this research, the livestock industry can utilize it as one of the materials to predict goat meat in the future
Analisa Metode Backpropagation Pada Prediksi Rata-rata Harga Beras Bulanan Di Tingkat Penggilingan Menurut Kualitas Dwira Azi Pragana; Dicky Wahyudi Manurung; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.855

Abstract

Rice is a staple food in Indonesia and plays a crucial role in the food structure as a source of nutrition. The diverse population of Indonesia, spread across various islands, makes rice availability highly important. The government continues to strive for food security, particularly by increasing domestic production. These considerations become even more significant for Indonesia due to its growing population and extensive geographical distribution. To meet the population's food needs, Indonesia needs sufficient food supply and distribution to fulfill consumption and maintain adequate reserves for extensive logistical operations. Rice shortage can be seen as a threat to economic and political stability. The significance of rice as a food commodity means that it is constantly in demand by people from all walks of life. Price fluctuations over time due to imbalances between supply and demand have a significant impact on the middle class and working class. The instability of rice prices greatly affects both the general public and farmers. Generally, prices are determined by the interaction of supply and demand. If supply is high and demand is low, prices will decrease. Conversely, if supply is low and demand is high, prices will increase. Prediction is an important tool to anticipate future events by recognizing patterns from the past. Backpropagation can be used as a method to predict rice prices. The data used in this study are the average monthly rice prices at the milling level according to the quality of large-scale traders from January 2023 to December 2023, in Indonesian Rupiah per kilogram. This research utilizes data obtained from the website of the Indonesian Central Bureau of Statistics (BPS) from 2013 to 2022. The study employed 5 different architectures for data testing, namely the 15-15-1 architecture with a testing mean square error (MSE) of 0.00644604, the 15-19-1 architecture with a testing MSE of 0.01005532, the 15-30-1 architecture with a testing MSE of 0.02119922, the 15-31-1 architecture with a testing MSE of 0.00009938. The best architecture in this study was the 15-17-1 model with 5206 iterations and a runtime of 18 seconds, achieving the smallest testing MSE of 0.00000105 and the highest accuracy of 100%. From the obtained architectures, it is evident that backpropagation can perform with a high level of precision. This research can serve as a guideline for the government to determine rice availability and establish average rice prices based on quality, thus preventing future rice shortages.
A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023) Agus Perdana Windarto; Anjar Wanto; S Solikhun; Ronal Watrianthos
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5274

Abstract

The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study.
Model Prediksi Algoritma ANN Pada Jumlah Ekspor Barang Perhiasan Dan Berharga Menurut Negara Tujuan Arifah Hanum; Tri Welanda; Anjar Wanto; Agus Perdana Windarto
TIN: Terapan Informatika Nusantara Vol 3 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v3i1.1773

Abstract

Currently Indonesia is one of the exporting countries to industrialized and developing countries. The methods carried out in the research of the prediction of the export of jewelry and valuables from this main destination country use the ANN (Artificial Neural Network) method. The research data used comes from the official website of the government, the Indonesian Central Statistics Agency. In this study, the data used is data from 2013 to 2020 consisting of 8 destination countries, namely Switzerland, Singapore, Hong Kong, United Arab Emirates, South Africa, Taiwan, the United States, and India. Based on this data can be determined network architecture model, namely 3 - 4 - 1, 3 - 8 - 1, 3 - 12 - 1, 3 - 16 - 1 and 3 - 20 - 1. After training and testing of the 5 models, it can be obtained that the best architectural model is on the 3-12-1 model with an MSE value of 0.033777975 on the ANN method
Implementation of Operational Competitiveness Rating Analysis (OCRA) and Rank Order Centroid (ROC) to Determination of Minimarket Location Ida Mayanju Pandiangan; Mesran Mesran; Rohmat Indra Borman; Agus Perdana Windarto; Setiawansyah Setiawansyah
Bulletin of Informatics and Data Science Vol 2, No 1 (2023): May 2023
Publisher : PDSI

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

Abstract

Minimarket location placement is one of the main capital for the business to progress and develop. In determining the location of the minimarket, various considerations must be taken so that nothing is fatal to the sustainability of the business. The problem that occurs in the company in choosing the placement of minmarket locations, namely the various locations that are chosen, each location has its advantages and disadvantages, each of which can affect the analysis of results and takes a long time to make a decision. So that requires a system that can provide a solution to this problem. In this case the resulting system is a system that is useful for determining location placement for minimarkets using the ROC method as its weighting and the OCRA method as a decision generator. This system can provide a solution in determining the location of minimarkets, from various existing locations. The results of each alternative are more objective and definitive in determining the location of minimarkets in a computerized way. For this reason, it is necessary to have supporting criteria for using a decision support system. Determination of importance weight values on conflicting criteria is generated through a weighting method, namely ROC or Rank Order centroid. The OCRA method or Operational Competitiveness Rating Analysis is a method that can calculate and produce rankings efficiently so that the resulting decisions are accurate. The results obtained from the utilization of this system determine the location of minimarkets using the OCRA method and ROC weighting as well as various conflicting criteria determined by the company and development management in Lubuk Pakam resulting in the highest preference value of 0.673 as a location that is suitable for use as a minimarket
Evaluasi Perbandingan Kinerja Convolutional Neural Networks untuk Klasifikasi Kualitas Biji Kakao Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Agus Perdana Windarto, M.Kom
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6533

Abstract

The assessment of cocoa bean quality plays a crucial role in the chocolate industry, and automated approaches utilizing image processing techniques and classifiers have become increasingly appealing. In this study, we implemented and compared the performance of image classifiers using Convolutional Neural Network (CNN) architectures for cocoa bean quality classification. By employing this approach, we developed a system capable of accurately and efficiently classifying cocoa bean images, reducing dependence on human evaluation. We compared several CNN architectures, including VGGNet, to evaluate their performance in cocoa bean image classification. Experimental results demonstrated that CNN-based classifiers can provide accurate assessments of cocoa bean quality, with significant success rates. This research contributes to the development of efficient and accurate image classification systems for cocoa beans, which can enhance efficiency in the chocolate industry and ensure product quality. Additionally, our testing results indicate that the model with a batch size of 64 achieved the highest accuracy of 98.44%, outperforming the other three tested batch sizes in cocoa bean classification performance.
Peramalan Nilai Penjualan Gas Elpiji 3 Kg di Sumatera Utara dengan bantuan Analisis Metode Jaringan Saraf Tiruan Maulidya Rahma Siregar; Adinda Putri Azhari; Dedy Hartama; Agus Perdana Windarto
Bulletin of Artificial Intelligence Vol 1 No 2 (2022): October 2022
Publisher : Graha Mitra Edukasi

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

Abstract

This research is related to forecasting the sales value of 3 Kg LPG in North Sumatra. The level of sales is influenced by customer satisfaction, service and customer needs. The purpose of this study is to determine the level of sales of 3 Kg LPG in North Sumatra and can overcome problems and overcome the amount of LPG demand in North Sumatra. So this research is needed using an artificial neural network method with a backpropagation algorithm to find the best sales results. The data used is divided into 2 parts, namely training and test data. The best network is taken from the Mean Square error (MSE) value and the smallest test. The experiments carried out in this study used a data rotation pattern, with 6 training and testing models. The experimental results of the 3-10-1 model are tests with the highest accuracy value, which is 100% and the MSE test is 0.00100005
Peningkatan Daya Saing dan Strategi Usaha Untuk Kelompok Ibu Rumah Tangga yang Tergabung Dalam Usaha Jahit “Yuni Phea” Di Kecamatan Siantar Selatan Kota Pematang Siantar Marisi Butarbutar; Acai Sudirman; Agus Perdana Windarto; Erbin Chandra
Prosiding Seminar Nasional Unimus Vol 5 (2022): Inovasi Riset dan Pengabdian Masyarakat Guna Menunjang Pencapaian Sustainable Developm
Publisher : Universitas Muhammadiyah Semarang

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

Abstract

Berdasarkan hasil observasi pra survei di lapangan, wawancara, dan pengamatan terhadap Mitra, dapat diidentifikasi beberapa permasalahan prioritas yang dihadapi mitra diantaranya, masih lemahnya manajemen organisasi terkait efektivitas kerjasama tim ibu rumah tangga dan jugapengurus kelompok dalam aktivitas kegiatannya. Kemudian kapasitas produksi yang terbatas dikarenakan kurangnya ketersediaan mesin jahit, sehingga anggota masih memanfaatkan mesin sesama anggota yang dimiliki dan sudah berusia cukup tua yang menyebabkan kapasitas produksi terbatas. Market share yang belum diidentifikasi dengan jelas dan upaya promosi yang belum optimal melalui media digital yang diakibatkan belum pahamnya pengetahuan mengenai strategipemasaran dan penggunaan media pemasaran. Indikator  keberhasilan kegiatan Pengabdian ini berupa peningkatan kapasitas produksi pada mitra yakni mitra berhasil memproduksi hasil kerajinan dalam jumlah yang lebih banyak dan memiliki spesifikasi yang lebih jelas. peningkatanmanajemen usaha pada mitra yakni, telah diterapkannya dan terciptanya tim kerja yang kohesif serta implementasi strategi pemasaran pada usaha dengan ditunjukkan kenaikan omset.Kata Kunci: Daya Saing, Strategi Usaha, Usaha Jahit
Co-Authors Abdul Karim Abdullah Ahmad Acai Sudirman Ade Dwi Amanda Adinda Putri Azhari Afrialita Widiastari Afrina Wati Alkhairi, Putrama Alkhairi, Putrama Alrizca Trydillah Alrizca Trydillah M Amanda, Ade Dwi Ambariyanto Ambariyanto Amri Amri Anan Wibowo Anandi Ayu Anggi Trifani Anjani, Dila Dwi Annisa, Liza Aprilia Syahputri Arfandi Arfandi Ariana, Anak Agung Gede Bagus Arieni, Fildzah Nadya Arifah Hanum Arifin Nur, Khairun Nisa Aulanda, Lulu Aulia Sugarda Aulia Sugarda Ayu Wulandari Ayu, Nur Zannah sekar Azhari, Ridhan Azzahra, Fahrija B. Herawan Hayadi Badawi, Masrof Beauti, Intan Bintang Aufa Sultan Butarbutar, Marisi Chairul Fadlan Chairul Fadlan Chintya Irwana Cici Astria Cici Astria Cici Astria Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Defit, Sarjon Della Puspita Deri Setiawan Desi Asima Silitonga Desi Asima Silitonga Desi Ratna Sari Devi Syahfitri Dewi Fortuna Efendi Dewinta Marthadinata Sinaga Deza Geraldin Salsabilah Saragih Dicky Wahyudi Manurung Dinda Nabila Batubara Dinda Nabila Batubara Dinda Nabila Batubara Dini Rizky Sitorus P Dio Hutabarat Disty Wahyuli Dwi Findi Auliasari Dwi Findi Auliasari Dwira Azi Pragana Dwira Azi Pragana Dwita Elisa Sinaga Edi Suharto Edy Satria Efendi, Muhamad Masjun Ega Widya Sari Eka Desriani Aritonang Eka Irawan Eka Irawan Eka Irawan Erbin Chandra Erlin Windia Ambarsari Evani Sitohang Fachri, Barany Fadhillah Azmi Tanjung Fadilla Anissa Fadillah Alwi Pambudi Fadlan, Chairul Fahrija Azzahra Fahry Husaini Fahry Husaini Fajar Syahputra Fania, Fira Fanny Adelia Fatmawati, Kiki Febiola, Adinda Fica Oktavia Lusiana Fifto Nugroho Fira Fania Fira Fania Fitri Rizki Frskila Parhusip Gita Febrianti Gita Febrianti Gumilar Ramadhan Pangaribuan Handrizal Handrizal Handrizal Handrizal Hanifah Urbach Sari Hanifah Urbach Sari Harahap, Zaki Faizin Hartama, Dedy Hartama, Dedy Hasudungan Siahaan Hendry Qurniawan Hendry Qurniawan Hendry Qurniawan Hersatoto Listiyono Heru Satria Tambunan Ht. Barat, Ade Ismiaty Ramadhona I Gede Iwan Sudipa Ida Mayanju Pandiangan Ihsan Maulana Muhamad Ihsan Syajidan Iin Indriani Iin Parlina Iin Parlina Iin Parlina Iis Warlinda Ikhwan Lubis Ilham Syahputra Saragih Ima Kurniawan Indah Dea Anastasia Indah Pratiwi M.S Indah Syahputri Indra Riyana Rahadjeng Indri Fatma Irfan Sudahri Damanik Irnanda, Khairunnissa Fanny Irwana, Chintya Isnaini, Alvina Ivo Yohana Manurung Iwan Purnama Jahril Jalaluddin Jalaluddin Jaya Tata Hardinata Johan Muslim Jufriadif Na`am, Jufriadif Khairun Nisa Arifin Nur Khairunnissa Fanny Irnanda Khairunnissa Fanny Irnanda Kiki Apni Puspita Sari Kiki Fatmawati Kurniawan Kurniawan Kusuma, Rizky Tri Leza Khairani Linda Sari Dewi Listy Oktaviani Lubis, Ikhwan M Fauzan M Fauzan M FAUZAN M Fauzan M Fauzan M Fauzan M Fauzan M Mesran M Mesran M. Fauzan M.Ridwan Lubis Manurung, Dicky Wahyudi Maria Etty Simbolon Marini Marini Masitha Masitha Masitha, Masitha Maulidya Rahma Siregar Mawaddah Anjelita Mawaddah Anjelita Mesran Mesran Mesran, Mesran Mhd Gading Sadewo Mhd Gading Sadewo Mhd Gading Sadewo Mhd Ridhon Ritonga Millah Sari Miralda, Viya Mita Yustika Mokhamad Ramdhani Raharjo Mokhamad Ramdhani Raharjo Mora Malemta Sitomorang Muhamad Muhamad Muhammad Alfahrizi Lubis Muhammad Aliyul Amri Muhammad Dwi Chandra Muhammad Fachrur Rozi Muhammad Fauzan Muhammad Kurniawansyah Muhammad Mahendra Muhammad Noor Hasan Siregar Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Yasin Simargolang muhammad yuda rizki Muhammad Yuda Rizki Muliadi Musiafa, Zayid Mustika Azzahra N Nurhayati N Nurhayati Nasution, Della Fatricia Nasution, Irmanita Nasution, Rizki Alfadillah Nazlina Izmi Addyna Nelson Butarbutar Nila Soraya Damanik Ninaria Purba Ningsih, Selfia Novika, Tri Nur Wulandari Nurul Atina Nurul Izzah Hadiana Nurul Rofiqo Nurwijayanti Ogi Wahyudi Okprana, Harly Oktaviani, Selli Onita Sari Sinaga P, Dini Rizky Sitorus P.P.P.A.N.W Fikrul Ilmi R.H.Zer Parinduri, Ikhsan Parlina, Iin Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih, Poningsih Prakasiwi, Cindy Pramesti, Adinda Frizy Prihandoko Prihandoko Putrama Alkhairi Putrama Alkhairi Putrama Alkhairi Rafiqotul Husna Raharjo, Mokhamad Ramdhani Rahmat Widia Sembiring - Rahmat Zulpani Raichan Septiono Ramadana, Rica Ramadani, Sri Ramadhani, Cerah Fitri Ranjani Rapianto Sinaga Ratih Ramadhanti Ratika Rizka Lubis Razalfa Aindi Siregar Rica Ramadana Ridho, Ihda Innar Rika Nur Adiha Rika Setiana Rika Setiana Rika Setiana Riski Yanti Rizal Efendi Rizki, Muhammad Yuda Rofiqo, Nurul Rohmat Indra Borman Rohmat Indra Borman Ronal Watrianthos Roni Kurniawan Rosanti, Yerika Puspa Rotua Sihombing Hutasoit Roy Chandra Telaumbanua Rozy, Muhammad Fachrur S Solikhun S Solikhun Sadewo, Mhd Gading Sahendra Fahreza Saidah, Fatiyah Saifullah Saifullah Saifullah Saifullah Salis, Rahmi Samosir, Rafiah Aini Sandy Erlangga Sari, Hanifah Urbach Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Sekar Rizkya Rani Selfia Ningsih Setiawan, Yudika Dwi Setiawansyah Setiawansyah Sigit Anugerah Wardana Sinaga, Dolli Sari Sinaga, Waris Pardingatan Sinta Maulina Dewi Sinta Maulina Dewi Sintya Sintya Siregar, Razalfa Aindi Siregar, Sandy Putra Siti Hajar Siti Hawani Siti Maysaroh Siti Sundari Sitompul, Wati Rizky Pebrianti Sitti Rachmawati Yahya Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Rahayu Ningsih Sri Ramadani Suci Cahya Mita Suhada Suhada Suhendro, Dedi Sundari Retno Andani Sundari Retno Andani Susi Susilowati, Susi Syahfitri, Retno Ayu Syahputra, Fajar Syahputra, Muhammad Tania Dian Tri Utami Tanjung, Fadhillah Azmi Tanjung, Fatimah Dwi Puspa Tia Imanda Sari Tia Imandasari Tia Imandasari Tira Sifrah Saragih Manihuruk Tri Ayu Lestari Tri Novika Tri Novika Tri Welanda Trydillah, Alrizca Ulfah Indriani Viya Miralda Waldi Setiawan Wanto, Anjar Warlinda, Iis Wendi Robiansyah Wendi Robiansyah Wida Prima Mustika Widiastari, Afrialita Widodo Saputra Widya Try Taradipa Winanjaya, Riki Winda Lidyasari Winda Permata Sari Wiranto Hernandesz Sirait Yanto, Musli Yuegilion Pranavarna Purba Yuegilion Pranayama Purba Yuhandri Yuhandri, Yuhandri Yuhandri, Muhammad Habib Yuli Sartika Nasution Yulia Andini Yuni Sara Luvia Zahra Nur Atthiyah Zahra Syahara Zaki Faizin Harahap Zer, P. P.P.A.N.W.Fikrul Ilmi R.H. Zulfia Darma Zuly Budiarso