Article Per Year (5 Year)
p-Index From 2021 - 2026
9.715
P-Index
This Author published in this journals
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 Teknik Informatika (JUTIF) 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 EXPLORER BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Computing and Informatics Research 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.
Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 152 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 152 Documents
Search

 12   13   14   15   16 
OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Imam Akbari; Dedy Hartama; Anjar Wanto
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.v11i2.7140

Abstract

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values ​​of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Model Hybrid CNN Mengintegrasikan NasNetMobile dan MobileNet untuk Meningkatkan Akurasi Klasifikasi White Blood Cell Sandi Putra Siregar; Anjar Wanto; Sundari Retno Andani
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2635

Abstract

Sel darah putih merupakan komponen vital dalam sistem kekebalan tubuh pada manusia yang berperan penting dalam melindungi tubuh dari serangan mikroorganisme penyebab penyakit. Variabilitas hasil dalam klasifikasi sel darah putih yang disebabkan oleh keterbatasan metode identifikasi manual masih menjadi isu kritis bagi akurasi system diagnostic berbasis citra. Dalam studi ini difokuskan untuk mengatasi permasalahan tersebut dengan merancang model jarignan saraf konvolusional (CNN) hybrid baru yang dinamakan SAN-Net, yang mengintegrasikan keunggulan arsitektur NASNetMobile dan MobileNet guna meningkatkan akurasi dalam klasifikasi jenis sel darah putih (basophil, erythroblast, monocyte, myeloblast, dan seg neutrophil). Model yang diusulkan dilatih menggunakan dataset citra sel darah putih yang dikumpulkan dari Kaggle kemudian dibandingakan dengan arsitektur standar yakni NASNetMobile. Hasil Pengujian menunjukkan bahwa model SAN-Net memberikan performa terbaik, dengan capaian akurasi, presisi, recall, dan Skor F1 sebesar 99,80%, serta secara signifikasi melampaui kinerja model pembanding. Temuan ini mengindikasikan bahwa potensi arsitektur deep learning modern dalam menghadirkan sistem klasifikasi sel darah putih otomatis dengan konsisten dan akurat, sehingga dapat meningkatkan efisiensi proses diagnosis.
Bird and Drone Image Classification Using ResNet CNN: A Deep Learning Approach for Aerial Surveillance Abdullah Ahmad; Anjar Wanto; Syed Muhammad Adnan
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.545

Abstract

Accurate classification of bird and drone images is crucial in supporting aerial surveillance and security systems, particularly to distinguish between natural objects such as birds and man-made objects such as drones. Manual classification methods have limitations in terms of speed and accuracy, thus necessitating a more efficient and reliable technology-based approach. This study aims to implement a ResNet-50 based Convolutional Neural Network (CNN) architecture to automatically classify bird and drone images. The dataset used was obtained from the Kaggle platform and consists of two classes: Bird and Drone, with a total of 22,407 images. The data was split into training (17,323 images), testing (844 images), and validation (1,740 images). All images underwent preprocessing and augmentation steps to enhance data quality and model training performance. The model was developed using the ResNet-50 architecture, which is well-regarded for handling complex image classification tasks. Evaluation results show that the model achieved an accuracy of 92%. For the Bird class, a precision of 0.83 and a recall of 0.99 were obtained, while for the Drone class, precision reached 0.99 and recall was 0.86. The average F1-score of 0.92 indicates that the model delivers balanced and reliable performance in the binary image classification task.
Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung Ade Ismiaty Ramadhona Ht. Barat; Wiwik Sri Astuti; Anjar Wanto; 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.
Analisis Sistem Pendukung Keputusan Pemilihan Merek Pasta Gigi Terbaik Menggunakan Metode SERVQUAL dan ORESTE Rahma Dhea Safitri; Anjar Wanto
BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 1 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v6i1.7713

Abstract

This study aims to address the problem of selecting the best toothpaste brand by developing a decision support system using a combination of the SERVQUAL and ORESTE methods. The problem raised is the difficulty consumers face in determining the most suitable product based on service quality. The SERVQUAL method is used to measure performance based on five dimensions of service quality, while ORESTE is used to rank alternatives without explicit weights. The system is implemented in the form of a data-driven evaluation model designed to mimic real-world conditions. The data reflects the perceived and expected values of several toothpaste brands, with the difference (GAP) calculated and processed using the ORESTE method to generate rankings. The results show that the Ciptadent brand received the highest preference with the lowest total ranking (6), followed by Oral-B and Colgate. The integration of these two methods enables a systematic and objective evaluation of overall service quality and can be used to support accurate consumer decision-making.
Refining CNN-Based Models for Multi-Class Corn Leaf Disease Classification Anjar Wanto; Poningsih Poningsih; Achmad Daengs GS; Silfia Andini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7400

Abstract

Corn leaf disease significantly impacts agricultural productivity and national food security, particularly in regions with high dependence on maize as a staple commodity. Manual disease identification remains challenging due to the need for expert agronomists, inconsistent environmental conditions, and visual similarities among disease patterns, often resulting in delayed decision-making and inaccurate control measures. Deep learning-based image classification has emerged as an effective solution for plant disease identification; however, existing models often face limitations regarding overfitting, poor generalization, and insufficient performance when applied to multi-class agricultural image datasets. Therefore, this research aims to develop an Improved EfficientNetB0 model for the multi-class classification of maize leaf diseases comprising Healthy, Leaf Blight, Leaf Rust, and Leaf Spot categories. A dataset of 4,000 images was used and processed through resizing, normalization, and augmentation techniques. Five CNN backbones; EfficientNetB0, MobileNetV2, ResNet50, DenseNet121, and InceptionV3—were initially evaluated, and EfficientNetB0 demonstrated the highest baseline performance. The model was subsequently enhanced through fine-tuning, regularization (dropout and batch normalization), and cosine learning rate scheduling. Experimental results show that the Improved EfficientNetB0 achieved superior performance with an accuracy of 0.9671, macro precision of 0.9665, macro recall of 0.9666, and macro F1-score of 0.9661, exceeding all baseline models. These findings demonstrate that the proposed framework effectively improves maize disease classification accuracy and contributes a robust solution for smart agriculture applications. Future work may integrate real-time deployment and mobile-based decision support for field-level monitoring.
Pemilihan Lokasi Supermarket Menggunakan Algoritma Penguin Search Optimization Yola Perdila Tamara; Dewi Santika; Anjar Wanto
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 3 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i3.2440

Abstract

Supermarket location selection is a critical challenge in strategic planning for modern retail businesses. Incorrect location decisions can lead to low customer visits and high operational costs. This study aims to determine the optimal supermarket location based on six key criteria: population, income, accessibility, distance to competitors, land rental costs, and traffic flow. To address this issue, this study implements the Penguin Search Optimization Algorithm (PeSOA), a metaheuristic inspired by the group hunting behavior of penguins in search of the best resources. A dataset of 50 location alternatives was processed using Python, and each criterion was normalized using the min-max method to standardize the scoring scale. The results show that the best location is at index 33 with a maximum fitness score of 4.3669. PeSOA achieved optimal convergence within 50 out of 100 iterations. The main advantage of PeSOA lies in its ability to explore solution space effectively with minimal parameters and deliver stable outcomes. This study confirms that PeSOA is an effective decision-support tool for retail location planning. These findings can be used as a foundation for developing intelligent algorithm-based decision support systems in the retail sector.
Global Inflation Forecasting Using Stacking Ensemble with Elastic Net Meta-Learner Integrating Random Forest, XGBoost, and LightGBM Wildhani, Fauriza; Wanto, Anjar; Damanik, Irfan Sudahri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5599

Abstract

Inflation dynamics have become increasingly complex due to economic volatility and nonlinear interactions, challenging the reliability of conventional forecasting models; therefore, this study develops a robust global inflation forecasting framework using a hybrid stacking ensemble that integrates Random Forest, XGBoost, and LightGBM as base learners with Elastic Net as a regularized meta-learner, applied to annual inflation data from 2000–2024 across five major economic blocs (G7, Europe, BRICS, ASEAN, and the Americas) after temporal feature engineering and time-series–preserving validation; the results demonstrate strong and consistent predictive performance, with very high accuracy in Europe (R² = 0.9282) and the G7 (R² = 0.9122), and the globally trained stacking model (R² = 0.7866) substantially outperforming the region-specific ASEAN model (R² = 0.5243), confirming the advantage of cross-country learning; this research advances informatics and computer science by providing a scalable and stable ensemble learning framework for macroeconomic time-series forecasting in volatile environments, supporting the development of AI-driven economic and policy analytics systems.
Optimization of ShuffleNetV2 Using Self-Knowledge Distillation for Cocoa Fruit Disease Classification Jasa, H.R Merdu Wira; Wanto, Anjar; Sormin, Rizky Khairunnisa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5649

Abstract

Timely cocoa fruit disease diagnosis is critical for field management, yet manual inspection is subjective and inconsistent, while many accurate deep learning models remain too computationally demanding for practical on-device use. This study aims to optimize cocoa fruit disease classification by applying self-knowledge distillation (Self-KD) to a lightweight ShuffleNetV2 architecture without increasing inference complexity. Using a three-class dataset (healthy, pod borer, and black pod rot) with preprocessing and class balancing, ShuffleNetV2 was selected as the baseline and trained with Self-KD, improving accuracy from 96.84% to 98.34% along with consistent gains in precision, recall, and F1-score. These results indicate that Self-KD provides a learning-level optimization that enhances robustness and prediction stability in lightweight CNNs, which is especially relevant for edge AI deployment in agricultural environments. Therefore, the proposed approach supports efficient, scalable, and sustainability-oriented AI (Green/Sustainable AI) for smart farming, with potential transferability to other crops that exhibit similar visual symptom patterns.
Integrasi Strategi Pre-processing Data untuk Optimalisasi Akurasi Algoritma Backpropagation Widodo Saputra; Saifullah Saifullah; Eka Irawan; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2743

Abstract

Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems
Co-Authors Abdi Rahim Damanik Abdullah Ahmad Abdullah Ahmad Achmad Noerkhaerin Putra Ade Ismiaty Ramadhona Ht Barat Ade Ismiaty Ramadhona Ht. Barat 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 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 Santika Dewi, Rafiqa Dinda Nabila Batubara Edu Wardo Saragih Efendi, Elfin Eka Irawan eko hartato Eko Hartato Eko Kurniawan Eko Purwanto 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 Hartama, Dedy Hartama, Dedy Heru Satria Tambunan Heru Satria Tambunan, Heru Satria 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 Imam Akbari 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 Jasa, H.R Merdu Wira 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 Mega Fatimah Rosana 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 Rahma Dhea Safitri 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 Saifullah Saifullah Samuel Palentino Sinaga Samuel Palentino Sinaga Sandi Putra Siregar 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 Silitonga, Hotmalina Silitonga, Hotmalina Siregar, Sandy Putra Solikhun Solikhun 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 Sundari Retno Andani Sunil Setti Surya Hendraputra Susi Fitryah Damanik Syafri Maradu Manurung Syafrika Deni Rizki Syahri Ramadhan Syed Muhammad Adnan Teuku Afriliansyah Tia Imandasari Titin Handayani Sinaga Tri Welanda Vasma Vitriani Sianipar Veithzal Rivai Zainal Venny Vidya utari Vitri Roma Sari Wendi Robiansyah Wida Prima Mustika Widodo Saputra Widya Tri Charisma Gultom Widyasuti, Meilin Widyasuti, Meilin Wildhani, Fauriza Winanjaya, Riki Wiwik Sri Astuti Yola Perdila Tamara Yuhandri Yuhandri, Yuhandri Yuri Widya Paranthy Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulia Almaida Siregar