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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) Sinkron : Jurnal dan Penelitian Teknik 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 JITK (Jurnal Ilmu Pengetahuan dan Komputer) 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) 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) JURTEKSI 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 Journal of Applied Data Sciences RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT DEVICE Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat 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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Pelatihan Pemanfaatan Mendeley Desktop Sebagai Program Istimewa Untuk Akademisi Dalam Membuat Citasi Karya Ilmiah Windarto, Agus Perdana; Hartama, Dedy; Wanto, Anjar; Parlina, Iin
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2018): Agustus
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v2i2.1319

Abstract

Desktop mendeley application is actually an application intended to facilitate the creation of citations and a list of libraries commonly used by the authors, so the authors will be pressed error in making the bibliography and facilitate in obtaining the writings to be cited. In addition to creating scientific papers, this application can also be used to manage the files of online journal articles that are the output of a scientific work. Furthermore, participants can utilize this application for the purpose of making a bibliography or collection of abstracts of certain fields of journal articles subscribed. Training activities undertaken in Community Service activities show that participants have a material understanding and the potential to make refernsi managers better and maximum by utilizing mendeley desktop applications.
Enhancing Autonomous Vehicle Navigation in Urban Traffic Using CNN-Based Deep Q-Networks Windarto, Agus Perdana; Solikhun, Solikhun; Wanto, Anjar
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.896

Abstract

This research proposes a CNN-based Deep Q-Network (CNN-DQN) model to enhance the navigation capabilities of autonomous vehicles in complex urban environments. The model integrates CNN for spatial abstraction with reinforcement learning to enable end-to-end decision-making based on high-dimensional sensor data. The primary objective is to evaluate the impact of CNN-DQN state abstraction on the quality and stability of the resulting policy. Using a grid-based simulator, the agent is trained on a synthetic dataset representing urban traffic scenarios. The CNN-DQN model consistently outperforms standard DQN in multiple metrics: cumulative reward increased by 14.3%, loss convergence accelerated by 22%, and mean absolute error (MAE) reduced to 0.028. Furthermore, the model achieved a Pearson correlation coefficient of 0.94 in predicted actions and demonstrated superior robustness under Gaussian noise perturbation, with reward loss limited to 6.18% compared to 18.7% in the baseline. Visualizations of CNN feature maps reveal spatial attention patterns that support efficient path planning. The action symmetry index confirms that the CNN-DQN agent exhibits consistent left-right decision behavior, validating its policy regularity. The novelty of this study lies in its combined use of deep spatial encoding and value-based reinforcement learning for structured, rule-based environments with real-time control implications. These findings indicate that CNN-enhanced reinforcement learning architectures can significantly improve autonomous navigation performance and robustness in dynamic urban settings.
Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Ahmad, Abdullah; Hartama, Dedy; Windarto, Agus Perdana; Wanto, Anjar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6411

Abstract

Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry.
Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World Setti, Sunil; Wanto, Anjar
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.205

Abstract

The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users are expected to help improve the economy and also education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict for the coming years so that the government can provide adequate facilities and pre-facilities in order to balance the growth of internet users and as a precautionary step when there is a decrease in the number of internet users. The data used in this study focus on data on the number of internet users in 25 countries in 2013-2017. The algorithm used is Artificial Neural Network Backpropagation. Data analysis was processed by Artificial Neural Network using Matlab R2011b (7.13). This study uses 5 architectural models. The best network architecture generated is 3-50-1 with an accuracy of 92% and the Mean Squared Error (MSE) is 0.00151674.
Workshop Pemanfaatan AI untuk Meningkatkan Literasi Digital Guru-Guru SMK dalam Proses Pembelajaran di Sekolah Achmad Daengs GS; Ni Luh Wiwik Sri Rahayu Ginantra; Teuku Afriliansyah; Anjar Wanto; Harly Okprana
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2024): Mei 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v4i1.2838

Abstract

This activity aims to equip UISU Siantar Private Vocational School teachers with knowledge and skills in utilizing Artificial Intelligence (AI) to increase learning effectiveness. This activity was carried out over two days with various sessions, including basic AI theory, the use of AI applications in learning, and direct practice in implementing AI in the classroom. The activity focuses on implementing AI workshops to increase vocational school teachers' digital literacy, especially at UISU Siantar Private Vocational School. This program is driven by rapid technological developments and the need to improve the quality of education through the integration of advanced technology, as well as equipping vocational school teachers with knowledge and skills in utilizing AI for various aspects of learning, including curriculum design, student evaluation, and classroom management. The team delivered the activity workshop in 2 ways, face-to-face and virtual, via the Zoom application. A pre-test and post-test were carried out on participants to measure the workshop's effectiveness. The average pre-test score was 60, while the average post-test score increased to 71.9. The analysis results show a significant increase in the level of teacher understanding. This increase indicates that the workshop successfully increased the digital literacy of vocational school teachers so that they are better prepared to integrate AI technology into the learning process at school.
Sistem Informasi Pengajuan Judul Tugas Akhir Berbasis Online pada Amik Tunas Bangsa Pematang Siantar Saputra, Widodo; Hardinata, Jaya Tata; Wanto, Anjar; Okprana, Harly
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 8, No 1 (2023): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v8i1.9645

Abstract

Tujuan dari pembuatan sistem informasi adalah untuk membantu manusia dalam banyak hal, termasuk pengolahan data serta penyampaian informasi, hal ini akan lebih baik jika menggunakan sistem informasi berbasis online. Sistem informasi tugas akhir menjadi pilihan penulis dikarenakan masih belum adanya sistem informasi tugas akhir   yang bersifat online pada AMIK Tunas Bangsa Pematangsiantar. Hal ini menyebabkan beberapa masalah seperti lambatnya penyampaian informasi, mahasiswa/i harus memberikan formulir pengajuan judul ke kampus dan lain sebagainya. Sistem ini menyediakan solusi bagi mahasiswa/i dalam proses merancang tugas akhir. Sistem ini meliputi pengajuan judul, pengumuman, judul diterima atau ditolak, informasi pembimbing dan lainnya. Penulis mencoba membangun sistem ini dengan menggunakan bahasa pemrograman PHP dan Database MySQL agar dapat di realisasikan secara online. Dengan adanya sistem ini maka akan membantu mahasiswa dalam melaksanakan prosedur tugas akhir dengan mudah karena dapat diakses dimanapun dan kapanpun.
Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung Ht. Barat, Ade Ismiaty Ramadhona; Astuti, Wiwik Sri; Wanto, Anjar; Solikhun, Solikhun
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.631

Abstract

Corn is one of the major agricultural commodities that plays a strategic role in national food security. However, its productivity often declines due to leaf diseases such as Blight, Common Rust, and Gray Leaf Spot. Manual disease detection is considered inefficient and prone to human error, especially on a large scale. This study aims to develop an automated deep learning-based system for accurate classification of corn leaf diseases. The proposed model utilizes the Convolutional Neural Network (CNN) architecture VGG16 with a transfer learning approach. The dataset comprises 1,200 labeled images of corn leaves categorized into four disease classes, obtained from Kaggle. Image augmentation techniques were applied to improve data diversity and enhance model generalization. The performance of VGG16 was compared with VGG16 Baseline architecture and MobileNetV2. Experimental results show that VGG16 with transfer learning achieved the highest classification accuracy of 96.25%, outperforming the baseline VGG16 (92.92%) and MobileNetV2 (84.58%). These findings demonstrate the effectiveness of VGG16-based transfer learning in automating corn leaf disease detection, supporting the implementation of precision agriculture technology.
ANALYSIS OF THE BACKPROPAGATION ALGORITHM IN PREDICTING WATER VOLUME OF PDAM TIRTAULI PEMATANG SIANTAR CITY Ramadani, Saputra; Wanto, Anjar; Safii, M.
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2893

Abstract

Abstract: Increasing living standards cause an increase in the need for drinking water. However, current water supply estimates are still not optimal, with water production sometimes being more or less than requirements. To estimate the amount of water, an appropriate method is needed. The method used in this research is the back propagation algorithm artificial neural network method. When developing forecasts, past data is necessary to produce accurate results. This research aims to develop a predictive model that can estimate the volume of water that will be used by PDAM Tirtauli in the future. It is hoped that this predictive model can help PDAMs in planning more efficient water supply management and can reduce the potential for water supply shortages in the future. This research uses water distribution data for the 2015-2022 period. Training data starts in 2015-2021, testing data starts in 2016-2022. In this research, results were obtained using the Matlab R2011a application. In this research, the 5 architectures used are architecture 6-53-1, 6-58-1, 6-61-1, 6-81-1, 6-87-1. Based on these five architectures, the best architecture was obtained, namely architecture 6-87-1 with a root mean square error test value of 0.00010031 and an accuracy of 92%. The results achieved in 2023 are the total water volume of PDAM Tirtauli Pematangsiantar of 189,610,426.                                                                                            Keywords: backpropagation; distribution; PDAM; prediction; water   Abstrak: Meningkatnya taraf hidup menyebabkan meningkatnya kebutuhan akan air minum. Namun, perkiraan pasokan air saat ini masih belum optimal, dengan produksi air kadang-kadang lebih atau kurang dari kebutuhan. Untuk memperkirakan jumlah air diperlukan suatu metode yang sesuai. Metode yang digunakan dalam penelitian ini adalah metode jaringan syaraf tiruan algoritma back propagation. Saat mengembangkan perkiraan, data masa lalu diperlukan untuk menghasilkan hasil yang akurat. Penelitian ini bertujuan untuk mengembangkan model prediktif yang dapat memperkirakan volume air yang akan digunakan oleh PDAM Tirtauli di masa mendatang. Model prediktif ini diharapkan dapat membantu PDAM dalam perencanaan pengelolaan pasokan air yang lebih efisien dan dapat mengurangi potensi kekurangan pasokan air pada masa yang akan datang. Penelitian ini menggunakan data sebaran air periode 2015-2022. Data pelatihan dimulai pada tahun 2015-2021, data pengujian dimulai pada tahun 2016-2022. Pada penelitian ini diperoleh hasil dengan menggunakan aplikasi Matlab R2011a. Pada penelitian ini 5 arsitektur yang digunakan adalah arsitektur 6-53-1, 6-58-1, 6-61-1, 6-81-1, 6-87-1. Berdasarkan kelima arsitektur tersebut diperoleh arsitektur terbaik yaitu arsitektur 6-87-1 dengan nilai uji root mean square error sebesar 0,00010031 dan mendapatkan akurasi sebesar 92%. Hasil yang dicapai pada tahun 2023 adalah total volume air PDAM Tirtauli Pematangsiantar sebesar 189.610.426. Kata Kunci: air; backpropagation; distribusi; PDAM; prediksi 
Comparative Study of Mobilenet and Resnet for Watermelon Leaf Disease Classification Using Deep Learning Ahmad, Abdullah; Wanto, Anjar; Windarto, Agus Perdana; Poningsih, Poningsih
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7543

Abstract

Watermelon leaf diseases, caused by various factors such as fungi, viruses, and bacteria, can have a significant impact on agricultural yields. To increase the amount and quality of watermelon produced, early diagnosis of this disease is essential. This study aims to compare the performance of two Convolutional Neural Networks (CNN) architectures included in Deep Learning, namely MobileNet and ResNet, in classifying watermelon leaf diseases using a dataset taken from Kaggle. This dataset consists of 1000 watermelon leaf images with three conditions, namely Downy Mildew (380 images), Healthy (205 images), and Mosaic Virus (415 images). ). 95% accuracy, 96% precision, 94% recall, and 95% f1-score are the results of the MobileNet model. In contrast, the ResNet model performs better, with 97% accuracy, 96% precision, 97% recall, and 97% f1-score. The study's findings show that ResNet outperforms MobileNet in the classification of watermelon leaf illnesses, despite both models' excellent and effective performance for automatic plant disease detection applications.
OPTIMIZATION OF SVM ALGORITHM FOR OBESITY CLASSIFICATION WITH SMOTE TECHNIQUE AND HYPERPARAMETER TUNING Nur, Khairun Nisa Arifin; Wanto, Anjar; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6878

Abstract

Excessive fat accumulation that impairs personal health and raises the risk of chronic diseases is the hallmark of obesity, a global health issue. Decision Tree (DT) has been widely used for obesity classification, but it tends to suffer from overfitting and poor performance on imbalanced datasets. To overcome these limitations, this study proposes an optimization of the Support Vector Machine (SVM) algorithm using Synthetic Minority Over-sampling Technique (SMOTE) and Hyperparameter Tuning. SMOTE was applied to balance the class distribution, whereas Grid Search was utilized to determine the optimal combination of hyperparameters (C, gamma, and kernel). The dataset employed in this research comprises multiple features related to individual health and lifestyle, with obesity level as the target class. The experimental results demonstrate that the optimized SVM model demonstrated strong classification performance, attaining 97% in accuracy, precision, recall, and F1-score. This high performance is significant because it enables more accurate early detection of obesity risk, which can support timely medical intervention and personalized treatment planning, ultimately contributing to better public health outcomesThese findings suggest that incorporating SMOTE and Hyperparameter Tuning substantially improves SVM performance, establishing it as a robust approach for obesity classification and early risk detection.
Co-Authors Abdi Rahim Damanik Abdullah Ahmad Achmad Noerkhaerin Putra Adnan, Syed Muhammad Agung Pratama Agung Wibowo Agung Yusuf Pratama Agus Perdana Windarto Akbari, Imam Anan Wibowo Andi Sanggam Sidabutar Arifah Hanum Arifin Nur, Khairun Nisa Asro Pradipta Astuti, Wiwik Sri Ayu Artika Fardhani Azwar Anas Manurung Azwar Anas Manurung Bil Klinton Sihotang Cici Astria Damanik, Bahrudi Efendi Damayanti, Tri Febri 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 Fazira, Rizky Nazwa Febriyanto, R Tri Hadi Fikri Yatussa’ada Fitri Anggraini GS , Achmad Daengs Gumilar Ramadhan Pangaribuan Hardinata, Jaya T Harly Okprana Hartama, Dedy Hartama, Dedy Heru Satria Tambunan Heru Satria Tambunan, Heru Satria Ht. Barat, Ade Ismiaty Ramadhona 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 Satria Indra Satria Indra Satria Indri Sriwahyuni Purba Irawan Irawan Irfan Sudahri Damanik Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jaya Tata Hardinata Jeni Sugiandi Jonas Rayandi Saragih Jonas Rayandi Saragih Joni Wilson Sitopu Jufriadif Na`am, Jufriadif Juli Wahyuni Khairun Nisa Arifin Nur Khairunnissa Fanny Irnanda Kirana, Ika Okta M Mesran M Safii M. Safii M.Ridwan Lubis Manurung, Azwar Anas MARIA BINTANG 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 Napitupulu, Flora Sabarina Nasution, Rizki Alfadillah Nasution, Zulaini Masruro Nazlina Izmi Addyna Ni Luh Wiwik Sri Rahayu Ginantra Nur Ahlina Febriyati Nur Arminarahmah Nur Arminarahmah Nur, Khairun Nisa Arifin Nuraysah Zamil Purba Nurhayati Nurhayati Okprana, Harly Okta Andrica Putra Parlina, Iin Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih, Poningsih Putrama Alkhairi Rahmat W Sembiring Rahmat W. Sembiring Rahmat Zulpani Ramadani, Saputra 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 Safii, M. Safruddin Safruddin Saifullah Saifullah Samuel Palentino Sinaga Samuel Palentino Sinaga Sandy Putra Siregar Saputra Ramadani Saragih, Irfan Christian Saragih, Jonas Rayandi Saragih, Mhd. Billy Sandi Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Setti, Sunil Sigit Anugerah Wardana Silaban, Herlan F Silfia Andini, Silfia Silitonga, Hotmalina Silitonga, Hotmalina Siregar, Sandy Putra Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Suhada Suhada Suhada Suhada Sumarno Sumarno Sumarno Sumarno Sumarno Sumarno Sundari Retno Andani Sundari Retno Andani Sunil Setti Surya Hendraputra Susi Fitryah Damanik Syafri Maradu Manurung Syafrika Deni Rizki Syahri Ramadhan Teuku Afriliansyah Tia Imandasari Titin Handayani Sinaga Tri Welanda Vasma Vitriani Sianipar Veithzal Rivai Zainal 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