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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Elektron Jurnal Ilmiah Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Information System for Educators and Professionals : Journal of Information System Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal Sebatik ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech International Journal of Informatics and Computation Dinasti International Journal of Education Management and Social Science Systematics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Jurnal Manajemen Sains Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Analisis Perbandingan Model Bert Dan Xlnet Untuk Klasifikasi Tweet Bully Pada Twitter Radillah, Teuku; Veza, Okta; Defit, Sarjon
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024119096

Abstract

Fenomena bullying di media sosial, khususnya di Twitter, telah menjadi isu yang semakin memprihatinkan dengan dampak signifikan terhadap kesehatan mental pengguna. Dalam rangka mengatasi masalah ini, deteksi otomatis tweet yang mengandung konten bullying menjadi sangat penting. Penelitian ini bertujuan untuk membandingkan performa dua model pemrosesan bahasa alami terbaru, yaitu BERT (Bidirectional Encoder Representations from Transformers) dan XLNet, dalam klasifikasi tweet yang mengandung bullying. Metodologi penelitian ini melibatkan pengumpulan dataset tweet yang telah dilabeli sebagai bullying atau non-bullying. Proses preprocessing teks dilakukan untuk membersihkan dan menyiapkan data sebelum digunakan dalam pelatihan model. Kedua model, BERT dan XLNet, dilatih dan diuji menggunakan dataset yang sama. Evaluasi performa dilakukan dengan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kedua model memiliki kemampuan yang baik dalam mengidentifikasi tweet bullying, akan tetapi XLNet menunjukkan performa yang lebih unggul dibandingkan BERT dengan tingkat akurasi sebesar 95%. Dengan nilai presisi  = 100%, recall  = 0,87%, dan F1-score = 0,88%. XLNet mampu menangkap konteks dan nuansa bahasa yang lebih kompleks dalam tweet, yang berkontribusi pada akurasi klasifikasi yang lebih tinggi. Penelitian ini memberikan kontribusi penting dalam bidang deteksi bullying di media sosial dengan menunjukkan bahwa penggunaan model XLNet lebih efektif dibandingkan BERT. Temuan ini dapat membantu platform seperti Twitter dalam mengidentifikasi dan mencegah konten bullying, sehingga menciptakan lingkungan online yang lebih aman bagi pengguna, serta dapat digunakan sebagai dasar untuk pengembangan sistem deteksi bullying yang lebih canggih dan efisien di masa depan.   Abstract The phenomenon of bullying on social media, particularly on Twitter, has become an increasingly concerning issue with significant impacts on users' mental health. In order to address this issue, automatic detection of tweets containing bullying content is crucial. This study aims to compare the performance of two recent natural language processing models, namely BERT (Bidirectional Encoder Representations from Transformers) and XLNet, in the classification of tweets containing bullying. The research methodology involves collecting a dataset of tweets that have been labelled as bullying or non-bullying. Text preprocessing is done to clean and prepare the data before it is used in model training. Both models, BERT and XLNet, were trained and tested using the same dataset. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that both models have a good ability to identify bullying tweets, but XLNet shows superior performance compared to BERT with an accuracy rate of 95%. With precision = 100%, recall = 0.87%, and F1-score = 0.88%. XLNet is able to capture more complex context and language nuances in tweets, which contributes to higher classification accuracy. This research makes an important contribution to the field of bullying detection on social media by showing that the use of the XLNet model is more effective than BERT. These findings can help platforms like Twitter identify and prevent bullying content, thereby creating a safer online environment for users, and can be used as a basis for the development of more sophisticated and efficient bullying detection systems in the future.
Application of Deep Learning with CNN Method in Identifying Corn Leaf Diseases [Preview] Lengga S. Sandy; Sarjon Defit; Gunadi Widi N.
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.109

Abstract

Diseases in maize plants are one of the main factors contributing to yield reduction, ultimately impacting food security and the sustainability of the agricultural sector. Technological advancements have enabled digital image processing to become a widely applied method in various fields, including object identification, pattern detection, and plant disease classification with the support of Artificial Intelligence (AI). This study utilizes maize leaf images as a dataset, which is then processed using the Convolutional Neural Network (CNN) method based on the ResNet-50 architecture. This architecture is known for its superior ability to extract deep visual features, thereby enhancing classification accuracy. The CNN model operates by identifying and analyzing key features in images, such as color, texture, and damage patterns on maize leaves, to detect the type of disease affecting the plants. The methodology in this research involves several crucial stages, starting from collecting maize leaf image datasets directly from agricultural fields, preprocessing data to improve image quality for optimal model training, and finally, training and evaluating the CNN model’s performance. The initial dataset consisted of 1,199 images, which, after image analysis, was reduced to 870 for processing. These were then split into 552 images for training, 87 for testing, and 261 for validation. The CNN model evaluation with 87 test images showed that the highest precision was achieved in the Rust Disease class (100%). Meanwhile, the highest recall was found in the Normal Leaf class (99%), indicating that the model correctly identified all normal leaf samples. However, the recall for the Fall Armyworm Disease class was 89%. This model accurately identifies damage patterns, making it a potential tool for automatic early disease detection in plants. Thus, this research can serve as a foundation for further developments in AI-based applications to enhance efficiency and accuracy in plant disease diagnosis, ultimately supporting agricultural productivity and sustainable food security.
Penerapan IoT pada Alat Temperature Monitoring System Cold Chain Box Vaccine Menggunakan Sensor DS18B20 Putra, Akmal Darman; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.589

Abstract

Unit Pelaksana Teknis (UPTD) Farmasi Dinas Kesehatan Kabupaten Siak  Office Pharmacy is fully responsible for maintaining the quality of the vaccine until the vaccine is distributed, the process of storing vaccines in the cold chain box has a problem, namely, it is not equipped with a real-time temperature monitoring device that can provide a warning to pharmacists if the cold chain box temperature rises due to internal or external damage. In addition to the problems mentioned, there is another problem, namely the temperature recording process is still done manually every 2 hours on the log sheet by pharmacists. Based on this, the purpose of this study is to develop technological innovation in the pharmaceutical field, namely by creating an IoT-based temperature monitoring tool that is integrated with the Telegram application and the Blynk IoT application. The research methods used in this study are the waterfall method, experiments, and UML modeling. The process of running this system begins when the sensor is inserted into the cold chain box then the sensor will send temperature data to the Blynk IoT application to be displayed in real-time. The performance of this technology works with the provision that if the temperature does not comply with the provisions, a warning will appear on the Blynk IoT and Telegram applications, the temperature data is then saved and will be used as a basis for making a report by the pharmacist. This research produces a temperature monitoring device using a DS18B20 temperature sensor and an ESP8266 microcontroller with an accuracy rate of more than 95% and this system can also provide real-time temperature data information and warnings via telegram. This research is expected to contribute to the pharmaceutical field as well as the benefits and convenience for pharmacists in monitoring temperature and recording temperature data
Penerapan Algortima K-Means Clustering untuk Optimalisasi Persediaan Liquid Vape Berdasarkan Data Penjualan Selfi Melisa; Defit, Sarjon; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.620

Abstract

Liquid vape is a liquid in an electronic cigarette (vape) device that contains a mixture of Propylene Glycol (PG), Vegetable Glycerin (VG), flavorings, and contains nicotine. As the use of vapes increases as an alternative to conventional cigarettes, efficient stock management becomes a challenge for vape shops to be able to meet customer needs without experiencing excess or shortage of inventory. Good stock management in a retail business is very important to maintain a balance between demand and product availability. This research aims to optimize liquid vape supplies by analyzing sales patterns. This research method is K-Means Clustering which includes several stages, namely determining the number of clusters, determining the centroid point randomly, calculating the closest distance between data and the centroid using the Euclidean method, grouping data into each cluster, updating the centroid until it is stable, and evaluating the results. The data used in the research is liquid vape sales data from June to November 2024 with a total of 68 product samples. Data processing was carried out manually and testing used RapidMiner software to measure the level of accuracy of the clustering results. The research results show that the K-Means Clustering algorithm is successful in grouping products into three categories: very popular, best selling, and not very popular. 51 products are in the low-selling category, 13 products are in the best-selling category, and 4 products are in the very best-selling category, with a Davies Bouldin value of 0.374%. The application of K-Means Clustering is effective in grouping products according to demand, helps determine the ideal stock amount, reduces the risk of product excesses or shortages, and increases operational efficiency
Penerapan Metode Neural Network untuk Prediksi Harga Bawang Putih di Kota Singkawang Fadlul Hamdi; Hendro Budiantoro; Rafika Sani; Rezki Rusydi; Sarjon Defit
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 12, No 2 (2024): Vol. 12, No 2, Juni 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v12i2.128039

Abstract

Bawang putih adalah komoditas penting dalam perekonomian Kota Singkawang. Penelitian ini bertujuan untuk menerapkan metode Neural Network dalam meramalkan harga bawang putih di kota tersebut. Data harga bawang putih dari Badan Pusat Statistik Kota Singkawang untuk periode tahun 2016-2023 digunakan dalam penelitian ini. Setelah melalui proses analisis dan pengolahan data, model Neural Network dilatih menggunakan data historis untuk memprediksi harga bawang putih di masa mendatang. Hasil prediksi menunjukkan bahwa harga bawang putih cenderung stabil selama dua tahun ke depan, dengan nilai tetap pada angka 30,701 untuk bulan 1 tahun 2024, 30,303 untuk bulan 2 tahun 2024, dan seterusnya hingga tahun 2025. Penelitian ini memberikan wawasan penting bagi para pelaku pasar dalam mengantisipasi perilaku pasar dan pengambilan keputusan di sektor bawang putih di Kota Singkawang.Kata kunci : bawang putih, harga, prediksi, Neural Network, Kota Singkawang Garlic is an important commodity in the economy of Singkawang City. This research aims to apply the Neural Network method in forecasting the price of garlic in the city. Garlic price data from the Central Bureau of Statistics of Singkawang City for the period 2016-2023 is used in this study. After going through the data analysis and processing process, the Neural Network model was trained using historical data to predict future garlic prices. The prediction results show that the price of garlic tends to stabilise over the next two years, with a fixed value of 30.701 for month 1 of 2024, 30.303 for month 2 of 2024, and so on until 2025. This research provides important insights for market players in anticipating market behaviour and decision-making in the garlic sector in Singkawang City.Keywords: garlic, price, prediction, Neural Network, Singkawang City
Sistem Pakar Metode Backward Chaining untuk Optimalisasi Pelayanan Pemberian Informasi Obat: Studi Kasus Puskesmas Lasi Kabupaten Agam Putra, Surya Dwi; Putri, Dhena Marichy; Defit, Sarjon; Sumijan, Sumijan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.1-7.2023

Abstract

Drug information service is an assistance service to handle the needs of pharmacists related to medicines consumed by patients at the Lasi Health Center, Agam Regency. Nowadays, most of drug information services always require pharmacists to carry out their services, although there is limited number of pharmacists for providing drug information services at the Lasi Health Center, Agam Regency. This study aims to optimize drug information services so that the services can be carried out without the direct presence of a pharmacist. The data used in this study were drug prescription data available at the Pharmacy of Lasi Health Center Agam for the last 12 months and drug information services provided by pharmacists at the Lasi Health Center Agam Regency. This study used the backward chaining method to identify the drugs prescribed to the patients. The result achieved by this study were 356 Rules that could be applied directly to drug information services, with an accuracy rate of 100%. The rules generated using the backward chaining method can be used to optimize drug information services at the Lasi Health Center in Agam Regency without having to be served directly by pharmacists.
Multi-Process Data Mining with Clustering and Support Vector Machine for Corporate Recruitment Zain, Ruri Hartika; Randy Permana; Sarjon Defit
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6197

Abstract

Having an efficient and accurate recruitment process is very important for a company to attract candidates with professionalism, a high level of loyalty, and motivation. However, the current selection method often faces problems due to the subjectivity of assessing prospective employees and the long process of deciding on the best candidate. Therefore, this research aims to optimize the recruitment process by applying data mining techniques to improve efficiency and accuracy in candidate selection. The method used in this research utilizes a multi-process Data Mining approach, which is a combination of clustering and classification algorithms sequentially. In the initial stage, the K-Means algorithm is applied to cluster candidates based on administrative selection data, such as document completeness and reference support. Next, a classification model was built using a Support Vector Machine (SVM) to categorize the best candidates based on the results of psychological tests, medical tests, and interviews. The experimental results show that the SVM model produces high evaluation scores, with an AUC of 87%, Classification Accuracy (CA) of 90%, F1-score of 89%, Precision of 91%, and Recall of 90%. With these results, it can be concluded that this model is able to improve accuracy in the employee selection process and help companies make more measurable and data-based recruitment decisions.
IMPLEMENTASI DECISION TREE DALAM PENGAMBILAN KEPUTUSAN UNTUK PEMBERIAN BEASISWA Zia Rahimi, Hadisha; Defit, Sarjon; Veri, Jhon
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13580

Abstract

Pemberian beasiswa merupakan salah satu upaya untuk mendukung akses pendidikan bagi siswa yang berprestasi dan membutuhkan bantuan finansial. Namun, proses seleksi penerima beasiswa yang dilakukan secara manual sering sekali memakan waktu lama, kurang efisien, dan berpotensi menimbulkan ketidak tepatan dalam penentuan penerima yang layak. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan menggunakan Decision Tree guna membantu proses seleksi penerima beasiswa secara lebih terstruktur, transparan, dan tepat sasaran. Metode Decision Tree Algoritma C4.5 digunakan dalam penelitian ini karena mampu mengolah data dalam jumlah besar serta menghasilkan pohon keputusan yang mudah dipahami dan kemudahannya dalam melakukan klasifikasi. Proses pengolahan data dilakukan melalui beberapa tahap, termasuk pengumpulan data, preprocessing, perhitungan entropy dan gain, serta pembentukan pohon keputusan. Data yang dikumpulkan diklasifikasikan berdasarkan kategori tertentu sebelum dianalisis menggunakan metode C4.5 untuk membangun pohon keputusan. Hasil penelitian menunjukkan bahwa metode Decision Tree dapat mengklasifikasikan siswa yang layak dan tidak layak menerima beasiswa dengan tingkat akurasi yang tinggi dibandingkan metode manual sebelumnya. Dengan adanya penelitian ini, diharapkan sekolah dapat lebih efisien dalam menyalurkan beasiswa kepada siswa yang benar-benar membutuhkan dan memastikan bahwa beasiswa diberikan kepada siswa yang benar-benar memenuhi kriteria.
Development of Euclidean Distance Algorithm for ANFIS Optimization in IoT-based Pond Water Quality Prediction Dahria, Muhammad; Defit, Sarjon; Yuhandri, Yuhandri
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26497

Abstract

Pond water quality is a pivotal factor that influences the productivity and health of biota in aquaculture systems. The monitoring and prediction of water quality parameters, including temperature, pH, and dissolved oxygen (DO) levels, are imperative for maintaining optimal environmental conditions. The objective of this research is to develop the Euclidean Distance algorithm as an optimization method in adaptive neuro-fuzzy inference system (ANFIS) modeling to enhance the accuracy of internet of things (IoT)-based pond water quality prediction. Water quality parameter data is collected in real-time using IoT sensors connected to an ESP32 microcontroller and transmitted to a cloud storage platform for analysis. Subsequently, the data undergoes a series of processing steps, including min-max normalization and feature selection based on Euclidean distance. This process aims to generate a more representative and relevant subset of data for the subsequent model training process. The ANFIS model was trained using the optimized data and evaluated using MSE, MAD, MRSE and MAPE metrics. The training process involving four data sharing scenarios demonstrated a reduction in error when compared to the model that lacked optimization, specifically: The following proportions were determined: 50% versus 50% (0.11824 versus 0.15536), 70% versus 30% (0.18666 versus 0.19454), 80% versus 20% (0.17843 versus 0.18833), and 90% versus 10% (0.22477 versus 0.22859). The findings indicate that the incorporation of the Weighted Euclidean Distance algorithm within the IoT-based prediction system can markedly enhance the efficiency and precision of the ANFIS model.
Development Extraction of Regional Features of Pleural Cavity Objects in Pneumothorax Lung X-ray Images by Dilation and Erosion Morphology Marfalino, Hari; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3387

Abstract

Image processing is a solution in the development of chest X-ray technology, starting from the image segmentation process as a preprocessing stage to separate the image object from the original background. Spontaneous pneumothorax (SP) is a type of air collection in the pleural cavity that develops without trauma. The diagnosis of pneumothorax has a sensitivity of approximately 25 to 75% using an anteroposterior chest x-ray, which still provides a dubious picture of pneumothorax. However, the development of the Region Feature algorithm with a new algorithm, namely RM Multy, has improved the accuracy. The RM Multy algorithm can calculate the area of the object, allowing it to produce the area of infiltration in the right lung, left lung, and the lung as a whole. The Region Feature results of the Pneumothorax obtained with the detected image area as many as 19 areas, for the pixel size of each area are 145, 355, 110, 31, 31, 52, 30, 36, 54, 122, 58, 23, 476, 77, 192, 24, 168, 263, 41 and 44. So the total pixels for 19 areas is 2301. The area converted to mm2 is 2301 x 0.04 mm2 = 92.04 mm2. Classification results on lungs with Pneumothorax and Normal by detection process with RM Multy using the CNN algorithm with an accuracy of 96.43%. This accuracy confirms the success of the system, which has been processed using a new algorithm. Therefore, further development is needed to improve detection accuracy in pneumothorax cases with smaller area sizes.
Co-Authors Abdul Azis Said Abuzar Gafari Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zaki Ahmad Zamsuri, Ahmad AHMADI Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Ali Ikhwan Alkhairi, Putrama Alvi Dwi Wahyuni Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andri Nofiar Angga Putra Juledi Anisya Anisya Anthony Anggrawan Antoni Antoni Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Billy Hendrik Bob Subhan Riza Bosker Sinaga Boy Sandy Dwi Nugraha.H Breinda, Engla Brestina Gultom Bufra, Fanny Septiani Chairun Nas Cyntia Trimulia Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Defi Pebriyanti Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devia Kartika Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Dzil Hidayati Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Eka Sofianti Elda, Yusma Elfiswandi, Elfiswandi eriwandi Eva Rianti Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febi Nur Salisah Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus Firdaus Firdaus, Muhammad Bambang Firna Yenila Fitri Safnita Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Ghea Paulina Suri Gunadi W Nurcahyo Gunadi Widi N. Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ika Melinia Sapitri Fitriyanti Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arif Wisky Ismail Virgo Istianingsih, Nanik Iswandi Saputra Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Junadhi Junadhi Junadhi, Junadhi Kamelia Sari, Rima Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Larissa Navia Rani, Larissa Lengga S. Sandy Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Iqbal Zuqron M. Syahputra Mardayatmi, Suci Mardian, Zurni Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen Muhammad Dahria Muhammad Habib Yuhandri Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa Okfalisa Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Ramdani Bayu Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rafki, Rafnelly Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Ramadhanu, Agung - Randy Permana Refina Afindania, Pipin Resnawita, R Retno Devita Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Richi Andrianto Rico Anggara Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila Sabil, Muhammad Said, Abdul Azis Saiful Nurarif Sandrawira Anggraini Sani, Rafikasani Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Silfia Andin Sintia Sintia Siregar, Diffri Solihin Siregar, Fajri Marindra Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti, Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva tukino, tukino Tukino, Tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yuli Hartati Yulihartati, Sandra Yusma Elda Z Zulvitri Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z