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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan 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 Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Rang Teknik Journal 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 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 JUKI : Jurnal Komputer dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) 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 Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Implementasi Algoritma C4.5 untuk Memprediksi Tingkat Ketepatan Kelulusan Mahasiswa Sari, Imrah; Defit, Sarjon; Sumijan, S
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.506

Abstract

Timeliness of graduation not only reflects the competence of graduates but also affects the assessment of study programme accreditation. To achieve this goal, it is important to predict and classify the timeliness of graduation to support more effective academic decision making. In this research, the Knowledge Discovery in Database (KDD) process is used, which aims to find knowledge from big data. One of the main stages in KDD is data mining, which focuses on pattern extraction with various algorithms. This research uses the C4.5 algorithm, a classification method that builds a decision tree to identify attributes that affect the timeliness of student graduation. This study uses data from students in 2017, 2018, and 2019 from the Bachelor of Nursing and Bachelor of Public Health study programmes at Syedza Saintika University, with a total sample of 46 student records. The C4.5 algorithm is applied to form a decision tree model, which produces classification rules based on attributes such as Grade Point Average (GPA), Study Programme, Gender, and Region of Origin. The results of the C4.5 algorithm implementation show a prediction accuracy of 89.13%, with GPA as the most dominant factor in influencing graduation accuracy. This research proves that the C4.5 algorithm is effective in predicting the timeliness of student graduation.
Penerapan Algoritma K-Means Untuk Klasterisasi Akseptor Keluarga Berencana Modern di Sumatera Barat Wicaksono, Putut; Defit, Sarjon; Nurcahyo, Gunadi Widi
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.497

Abstract

The Regulation of the National Population and Family Planning Agency number 11 of 2020 concerning modern contraceptive methods including the Female Operation Method (MOW)/female sterilization, Male Operation Method (MOP)/male sterilization, IUD/spiral/Intrauterine Contraceptive Device (IUD), implant/implant, injection, pill, and condom. This study aims to apply and test the K-Means algorithm by measuring the level of accuracy in clustering Districts/Cities based on the use of modern contraceptives. The method used in this study is the K-Means Clustering algorithm to produce 3 clusters, namely district/city clusters with high, medium, and low acceptor usage. The stages of the K-Means Clustering algorithm are as follows: Determining the number of clusters, Determining the initial centroid point randomly, Calculating the closest distance between data and centroid, Grouping data into each cluster, If the cluster changes, the process continues to the next iteration, if there is no change, the iteration process is stopped. The data set processed in this study came from the BKKBN of West Sumatra Province. This study used a data set of 383,609 from 19 districts/cities based on the use of modern contraceptives. The results of this study indicate that the performance of the K-Means method in cluster analysis produces 3 clusters consisting of low modern contraceptive users of 5 districts/cities in cluster 0 or 26.32%, moderate modern contraceptive users of 7 districts/cities in cluster 1 or 36.84%. users of modern contraceptives are high as many as 7 districts/cities in cluster 2 or 36.84%. Therefore, this study can be a reference for district/city governments in intervening in population control and family planning programs.
Leveraging K-Nearest Neighbors with SMOTE and Boosting Techniques for Data Imbalance and Accuracy Improvement Lubis, Adyanata; Irawan, Yuda; Junadhi, Junadhi; Defit, Sarjon
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.343

Abstract

This research addresses the issue of low accuracy in sentiment analysis on Israeli products on social media, initially achieving only 64% using the K-NN algorithm. Given the ongoing Israeli-Palestinian conflict, which has garnered widespread international attention and strong opinions, understanding public sentiment towards Israeli products is crucial. To improve accuracy, the study employs SMOTE to handle data imbalance and combines K-NN with boosting algorithms like AdaBoost and XGBoost, which were selected for their effectiveness in improving model performance on imbalanced and complex datasets. AdaBoost was chosen for its ability to enhance model accuracy by focusing on misclassified instances, while XGBoost was selected for its efficiency and robustness in handling large datasets with multiple features. The research process includes data pre-processing (cleaning, normalization, tokenization, stopwords removal, and stemming), labeling using a Lexicon-Based approach, and feature extraction with CountVectorizer and TF-IDF. SMOTE was applied to oversample the minority class to match the number of instances in the majority class, ensuring balanced representation before model training. A total of 1,145 datasets were divided into training and testing data with a ratio of 70:30. Results demonstrate that SMOTE increased K-NN accuracy to 77%. Interestingly, combining K-NN with AdaBoost after SMOTE achieved 72% accuracy, which, although lower than the 77% achieved with SMOTE alone, was higher than the 68% accuracy without SMOTE. This discrepancy can be attributed to the added complexity introduced by AdaBoost, which may not synergize as effectively with SMOTE as XGBoost does, particularly in this dataset's context. In contrast, K-NN with XGBoost after SMOTE reached the highest accuracy of 88%, demonstrating a more effective combination. Boosting without SMOTE resulted in lower accuracies: 68% for KNN+AdaBoost and 64% for KNN+XGBoost. The combination of K-NN with SMOTE and XGBoost significantly improves model accuracy and reliability for sentiment analysis on social media.
Analisis Sentimen pada Ulasan Handphone dengan Algoritma FP-Growth Marmay, Roza; Lidya, Leony; Defit, Sarjon
Jurnal Penelitian Inovatif Vol 5 No 1 (2025): JUPIN Februari 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jupin.993

Abstract

Masyarakat dewasa ini lebih sering melakukan pembelian produk melalui daring. Hal ini tentu membutuh-kan kecermatan dalam membaca ulasan guna mendapatkan produk yang baik. Sentimen analisis dilakukan untuk menganalisa ulasan pengunjung dari komentar sebuah produk dalam media sosial. Penelitian kali ini difokuskan kepada ulasan produk handphone merk asus zenfone2 yang diambil dari amazon.com guna mengetahui sentimen pengunjung website terkait produk yang dipilih. Proses pengolahan data tersebut dimulai dari pemilahan ulasan yang didapat menjadi perkalimat untuk mempermudah proses selanjutnya. Untuk mendapatkan noun dan adjective dari kalimat tersebut, dilakukan tahapan preprocessing seperti lower case, tokenisasi, lemmatization, serta POS tagging. Noun yang didapat dari prepocessisng tersebut digunakan dalam algoritma FP-growth untuk menemukan fitur pada handphone asus zenfone2 yang sering dibicarakan. Sedangkan adjective yang di dapat, akan digunakan untuk mendeteksi apakah kalimat yang mengandung fitur tersebut bernilai positif atau negatif. Hasil dari analisis ini dapat digunakan customer dalam mempertimbangkan produk tersebut tanpa harus membaca ulasan satu persatu.
The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification Rahmi, Nadya Alinda; Defit, Sarjon; Okfalisa, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3092

Abstract

This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification of academic papers into eight distinct fields: decision support systems, information technology, data science, technology education, artificial intelligence, expert systems, image processing, and information systems. The research dataset comprises reviews of scientific papers ranging from 150 to 500 words, collected from the repository of Universitas Putra Indonesia YPTK Padang. The classification process involved the application of the TF-IDF method for feature extraction, followed using various machine learning algorithms including SVM, MNB, KNN, and RF, with and without the integration of SMOTE for data balancing and Optuna for hyperparameter optimization. The results show that combining SMOTE with Optuna significantly improves the accuracy, precision, recall, and F1-score of the models, with the SVM algorithm achieving the highest accuracy at 90%. Additionally, the research explored the effectiveness of ensemble methods, revealing that hard voting combined with SMOTE and Optuna provided substantial improvements in classification performance. These findings underscore the importance of hyperparameter tuning and data balancing in optimizing machine learning models for text classification tasks. The implications of this research are broad, suggesting that the methodologies developed can be applied to various text classification tasks in different domains. Future research should consider exploring other hyperparameter tuning techniques and ensemble methods to further enhance model performance across diverse datasets.
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.
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
Optimasi Seleksi Ekstrakurikuler Siswa Menggunakan Metode Profile Matching: Studi Kasus di SMP Negeri 1 Kerinci Zuqron, M. Iqbal; Defit, Sarjon; Widi Nurcahyo, Gunadi
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2211

Abstract

Penerapan metode Profile Matching dalam pengelompokan minat dan bakat ekstrakurikuler siswa di SMP Negeri 1 Kerinci. Pemilihan ekstrakurikuler yang tepat bagi siswa merupakan tantangan tersendiri bagi sekolah, terutama karena belum adanya sistem pendukung keputusan yang terkomputerisasi. Selama ini, pemilihan dilakukan secara manual berdasarkan aspek tinggi badan, berat badan, fleksibilitas, dan kecepatan, yang sering kali tidak objektif dan memakan waktu lama. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem berbasis komputer yang dapat membantu menentukan ekstrakurikuler siswa secara lebih efektif dan efisien. Metode Profile Matching digunakan untuk mencocokkan kompetensi individu dengan standar kompetensi ekstrakurikuler. Proses ini dilakukan dengan mengidentifikasi gap antara nilai profil siswa dan nilai target yang telah ditentukan untuk setiap ekstrakurikuler. Perhitungan dilakukan dengan menentukan bobot pada faktor utama (core factor) dan faktor pendukung (secondary factor), yang masing-masing diberi persentase pengaruh sebesar 60% dan 40%. Dari hasil perhitungan, sistem dapat secara otomatis merekomendasikan ekstrakurikuler yang paling sesuai untuk setiap siswa. Hasil penelitian menunjukkan bahwa sistem berbasis Profile Matching ini dapat meningkatkan akurasi pemilihan ekstrakurikuler hingga 85% dibandingkan dengan metode manual. Selain itu, implementasi sistem berbasis web dengan bahasa pemrograman PHP membantu mempercepat proses seleksi dan meminimalkan subjektivitas dalam pengambilan keputusan. Dengan adanya sistem ini, diharapkan proses seleksi ekstrakurikuler dapat dilakukan dengan lebih objektif, akurat, dan efisien. Persentase keakuratan: 85% (berdasarkan perhitungan metode dan hasil perbandingan dengan sistem manual).
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
Co-Authors Abdul Azis Said Adawiyah, Quratih Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Afriyadi, Iqbal Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zamsuri, Ahmad Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andin, Silfia Andri Nofiar Angga Putra Juledi Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Bosker Sinaga Breinda, Engla Bufra, Fanny Septiani Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devita, Retno Dhena Marichy Putri Dila, Rahmah Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus, Muhammad Bambang Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra, Guslendra Hadiyanto, Tegas Halifia Hendri Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Hidayat, Rahmadani Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Juledi, Angga Putra Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Syahputra Mardayatmi, Suci 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 TAJUDDIN Muhammad, L. J. Mulyanda, Sandy Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi, Nadya Alinda Ramadhan, Mukhlis Ramadhanu, Agung Ramdani Bayu Putra Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Rusdianto Roestam Rustam, Camila S Sumijan Said, Abdul Azis Sandrawira Anggraini Sani, Rafikasani Saputra, Dhio Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Siregar, Diffri Solihin Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Ghea Paulina Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki, Syafrika Deni Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva 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 Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulvitri, Z Zuqron, M. Iqbal Zurni Mardian