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Agus Perdana Windarto
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Implementasi JST pada Prediksi Total Laba Rugi Komprehensif Bank Umum dan Konvensional dengan Backpropagation Windarto, Agus Perdana; Lubis, Muhammad Ridwan; Solikhun, Solikhun
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 4: Agustus 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (327.882 KB) | DOI: 10.25126/jtiik.201854767

Abstract

Total laba rugi komprehensif merupakan hasil yang digunakan untuk mengukur keberhasilan kinerja perusahaan selama periode tertentu yang tidak dipengaruhi oleh operasi normal perusahaan. Informasi total lapa rugi komprehensif sangat penting bagi beberapa pengguna laporan keuangan seperti investor, kreditor dan manajemen dalam memprediksi dimana posisi angka total laba rugi komprehensif untuk menentukan arah investasi masyarakat ke depan, begitu juga bagi pihak bank berguna untuk menentukan kebijakan strategi pemasaran dalam meninggkatkan total laba komprehensif tersebut. Penelitian ini bertujuan untuk membuat prediksi dengan menggunakan Artificial Intelligence dengan algortima backpropagation. Data yang digunakan bersumber dari Otoritas Jasa Keuangan (OJK) pada PT. Bank Mandiri (Persero) Tbk (Januari-Oktober 2016). Untuk melakukan prediksi dengan algortima backpropagation. Proses dilakukan dengan membagi data pelatihan dan pengujian untuk memperoleh model arsitektur terbaik. model arsitektur pelatihan dan pengujian yang digunakan untuk melakukan prediksi Total laba rugi komprehensif yakni: 4-25-1; 4-50-1; 4-50-75-1 dan 4-100-1. Dari serangkaian uji coba didapat pola terbaik dari arsitektur backpropagation adalah 4-50-1 dengan Means Square Error 0,0009978666, epoch 1977 dan akurasi 80% yang selanjutnya akan digunakan untuk melakukan prediksi. Abstract Total comprehensive income is the result used to measure the success of a company's performance over a certain period that is not affected by the company's normal operations. Total information on comprehensive loss is very important for some financial report users such as investors, creditors and management in predicting where the position of the total comprehensive income statement is to determine the direction of public investment going forward, as well as for banks to determine marketing strategy in increasing total profit comprehensive. This study aims to make predictions using Artificial Intelligence with backpropagation algorithms. The data used is sourced from the Financial Services Authority (OJK) at PT. Bank Mandiri (Persero) Tbk (January-October 2016). To predict with backpropagation algorithm. The process is carried out by dividing training and testing data to obtain the best architectural model. the training and testing architectural model used to predict the total comprehensive income: 4-25-1; 4-50-1; 4-50-75-1 and 4-100-1. From a series of trials obtained the best pattern of backpropagation architecture is 4-50-1 with Means Square Error 0,0009978666, epoch 1977 and accuracy 80% which will then be used to make predictions. 
Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia's Primary Energy Supply Windarto, Agus Perdana; Rosanti, Yerika Puspa; Mesran, Mesran
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.29032

Abstract

The rapid economic growth and population development in Indonesia have significantly increased the demand for energy, presenting complex challenges in managing the primary energy supply due to geographical variability and dispersed natural resources. This study addresses these challenges by applying clustering techniques with a hyperparameter approach to explore and map Indonesia's primary energy supply. The research contributes to the field by offering an effective method for analyzing energy data patterns and optimizing energy management. Secondary data on energy production, consumption, and distribution from reliable sources such as the Ministry of Energy and Mineral Resources were collected and analyzed. Various clustering algorithms, including K-Means, Fast K-Means, X-Means, and K-Medoids, were applied to identify energy supply patterns across different regions. The Davies-Bouldin Index was used to evaluate the effectiveness of the clustering algorithms. The results indicate that distance measures such as Euclidean Distance and Chebychev Distance consistently show excellent clustering performance. The study found that the choice of distance measure significantly impacts the clustering quality. The insights gained from this analysis provide valuable information for stakeholders involved in energy planning and policy-making, enhancing the efficiency and sustainability of energy management in Indonesia. This research establishes a foundation for further detailed and holistic energy data analysis, supporting better decision-making in energy planning and development.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih, Poningsih; Windarto, Agus Perdana; Alkhairi, Putrama
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
Penerapan Metode Data Mining C4.5 dalam Penentuan Kelayakan Rehabilitas Rumah Warga Aulia Sugarda; Saifullah; Jalaluddin; Agus Perdana Windarto; Wendi Robiansyah
Journal of Computing and Informatics Research Vol 1 No 3 (2022): July 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v1i3.321

Abstract

The purpose of the study was to find out which houses deserve to be rehabilitated in Pematang Dolok Kahean Village. The source of the data used in this research is using datasets that already exist in Pematang Dolok Kahean Village. The solution given is to classify the feasibility level of residents' houses using the C4.5 data mining method and using the Rapidminer software assistance. This method was chosen because it is one of the most widely used decision tree methods to predict a case. The results of the study stated that the system's accuracy value was 83.33% using split validation where this method produced several rules that could be used in determining the feasibility of the rehabilitation of residents' houses so that government subsidies could be channeled appropriately.
Analisis Model Backpropagation Dalam Meramalkan Tingkat Penjualan Saldo “Link Aja” Dwi Findi Auliasari; Gita Febrianti; Agus Perdana Windarto; Dedy Hartama
Journal of Computing and Informatics Research Vol 2 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i1.382

Abstract

Analysis of a prediction (forecasting) is very important in a study, so that research becomes more precise and directed (Wanto and Windarto, 2017). As is the case in predicting the level of Link Aja's balance sales. This research is expected to be useful for an agency as one of the study materials in business development. A system to predict the level of sales of Link Aja balance at PT. Wahana Putra Yudha. Artificial Neural Network is a method that is able to perform a mathematical process to predict the level of sales of Link Aja Balance at PT. Wahana Putra Yudha. By using the backpropagation method, the previous data processing process is carried out which will be used as input to predict the sales level of Link Aja Balance. The data were taken from January 2021 to April 2022. January 2021 to August 2021 were used as training data, while September 2021 to April 2022 were used as test data. The training architecture model used to predict the sales level of Link Aja's Balance is: 4-2-1; 4-25-1; 4-50.1; 4-75-1; and 4-100-1. The best architecture is 4-50-1, the percentage result is 75% in each test
Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing Di Indonesia Rika Setiana; Siregar, Razalfa Aindi; 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; Manurung, Dicky Wahyudi; 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.
Penerapan Metode Jaringan Saraf Tiruan Dalam Memprediksi Produksi Daging Domba Menurut Provinsi Listy Oktaviani; Sandy Erlangga; Bintang Aufa Sultan; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.992

Abstract

Prediction is the process of estimating future needs. This research aims to predict the amount of sheep meat production by province. Lamb is a source of protein which is also a high value commodity. However, along with the increase in lamb production in Indonesia, the level of lamb meat consumption in Indonesia has tended to fluctuate in recent years. Imports are the step most often taken by the government to meet domestic sheep meat needs. By using Artificial Neural Networks and the backpropagation algorithm, the amount of sheep meat production will be predicted based on provinces in order to determine steps to fulfill domestic sheep meat needs based on the amount of sheep meat consumption in the community. This research uses data from 2001 to 2022 with 1 target, namely data for 2023.
Analisa Metode Backpropagation Dalam Memprediksi Jumlah Perusahaan Konstruksi Berdasarkan Provinsi di Indonesia Muhammad Kurniawansyah; Rafiqotul Husna; Raichan Septiono; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.993

Abstract

This research aims to analyze the number of construction companies in Indonesia and gain an understanding of the trends and characteristics of the construction industry in that country. In this research, data related to the number of construction companies is analyzed using available sources such as government statistical reports, industry publications, and other secondary data sources. The data we use in this research is data on the number of construction companies by province in Indonesia from 2016-2021 which was taken from the website of the Central Statistics Agency (BPS) using the backprogation artificial neural network (JST) method. The analysis results show that the number of construction companies in Indonesia has increased significantly in recent years. It is hoped that this research will encourage strong economic growth and increasing investment in the infrastructure and property sectors has driven demand for construction services. In addition, government policies that support the construction sector, such as infrastructure development programs and regulations that facilitate foreign investment, also contribute to the growth in the number of construction companies. Apart from growth trends, this research also identifies several characteristics of the construction industry in Indonesia. The industry is dominated by small and medium-sized companies operating locally, although there are also large companies involved in large-scale projects. Competition in this industry is fierce, with companies vying to win construction contracts and develop a competitive advantage. The architectural models that we use in this research are 6 architectural models, of which the best architectural model will be obtained. The architectural models include 5-11-1-1 with an accuracy percentage of 61.8%, 5-12-1- 1 with an accuracy percentage of 70.6%, 5-14-1-1 with an accuracy percentage of 82.4%, 5-18-1-1 with an accuracy percentage of 64.7%, 5-20-1-1 with an accuracy percentage of 70.6%, 5-22- 1-1 with an accuracy percentage of 73.5%. So the best architectural model is obtained, namely the 5-12-1-1 model which produces an accuracy rate of 82.4%. with a Mean Square Error (MSE) of 0.00099997 with an error prone of between 0.001-0.05. These results are quite good in predicting the number of construction companies based on provinces in Indonesia
Klasifikasi Peminatan Topik Keilmuan Dalam Penyelesaian Studi Menggunakan Algoritma Naive Bayes Waldi Setiawan; Dedy Hartama; Muhammad Ridwan Lubis; Ihsan Syajidan; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1200

Abstract

Academic expertise is a subject of study taught at the university level to assist students in completing their thesis writing, thereby enabling them to successfully complete their graduate studies. The chosen academic specialization aligns with the vision and mission of each program and can have a positive impact on the university. Students' chosen fields of expertise in completing their studies may either align or not align with the program's vision and mission. The variables used in this research are GPA, MKRV1, MKRV2, and Academic Expertise. The aim of this research is to determine how many students select an academic topic that aligns with the program's vision and mission, particularly in this case, the Computer Science program, as they complete their studies. The Naïve Bayes algorithm is employed in this research, yielding an accuracy rate of 98.11%. This research can provide valuable insights for STIKOM Tunas Bangsa Pematang Siantar to understand the extent to which students from other programs choose academic expertise that aligns with the vision and mission of each program.
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 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 Sembiring, Rahmat Widia 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