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Febi Yanto
Teknik Informatika, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Transportation Other

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Implementasi AES ECB dan Hashing MD5/SHA-256 Pada Aplikasi Penyuratan Android Fajar Febriyadi; Fitra Kurnia; Nazruddin Safaat Harahap; Febi Yanto; Pizaini Pizaini
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4505

Abstract

The Riau Ministry of Religion Regional Office is still archiving assignment letters and official travel letters manually. The staff who take care of the correspondence section, namely personnel and legal unit staff, do not have an application that facilitates the activities of assignment letters and official travel letters to simplify filing and data containing certain information contained in letters which include assignment letters and official travel letters. Security is important because it relates to data. Therefore, a correspondence application was created to support the correspondence activities of the Riau Ministry of Religion Regional Office and make it easier for staff in the Civil Service and Legal unit to properly manage assignment letters and official travel letters as well as control books. Android application development uses the waterfall method and the ECB (Electronic Code Book) mode AES algorithm and MD5/SHA-256 hashing for security. By building this application, it will be easier for leaders and employees to exchange letters and confidential information, guaranteed security and the application built can be used by users easily. The results of the Black Box testing carried out on the application produced the expected output and the UAT test obtained a score of 89%. Application testing on sentences, Jpg, Png and PDF files has a fairly high level of security using statistical analysis methods, namely bit frequency testing, autocorrelation, 0/1 bit distribution, entropy.
Perbandingan Pembobotan Kata Menggunakan Naïve Bayes Classifier Terhadap Analisa Sentimen Permendikbud No 30 Tahun 2021 Jeki Dwi Arisandi; Elvia Budianita; Eka Pandu Cynthia; Febi Yanto; Yusra Yusra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4420

Abstract

Abstrak - Kekerasan seksual di lingkungan Pendidikan mengalami peningkatan kasus dari tahun ke tahun. Menurut data dari Komnas Perempuan periode 2015-2020 kasus kekerasan seksual di lingkungan Pendidikan menunjukkan bahwa lingkungan Pendidikan sudah tidak menjadi tempat yang aman bagi peserta didik. Berdasarkan data kasus yang diadukan kepada komnas perempuan pada tahun 2015-2020 kasus kekerasan seksual tertinggi terjadi di lingkungan Universitas sebanyak 27%, lalu diikuti oleh Pesantren atau Pendidikan berbasis agama sebanyak 19% dan sisanya terjadi di tingkat SMU/SMK sebanyak 15%, SMP 7%, di tingkat TK,SD,SLB dan Pendidikan berbasis Kristen masing-masing sebanyak 3%. Bentuk kekerasan seksual yang terjadi di lingkungan Pendidikan tersebut berupa pemerkosaan, pencabulan, dan pelecehan seksual serta kekerasan psikis dan diskriminasi dengan mengeluarkan siswa dari sekolah. Berbagai kasus tersebut mendorong pihak Kementrian Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia membuat Peraturan Menteri No 30 Tahun 2021 dengan tujuan untuk menangani berbagai kekerasan seksual yang selama ini masih terjadi di lingkungan Pendidikan. Namun setelah diterbitkannya Peraturan Menteri nomor 30 Tahun 2021 tersebut memunculkan beragam sentimen positif dan negatif dari masyarakat baik itu dari organisasi HAM dan organisasi keagamaan. Opini dari masyarakat tersebut dapat dijadikan bahan evaluasi bagi pemerintah untuk menilai kebijakan yang telah dibuat. Dalam penelitian ini membahas mengenai analisa sentimen Permendikbud no 30 tahun 2021 dengan melakukan perbandingan pembobotan kata menggunakan metode Naïve Bayes Classifier. Langkah awal yang penulis lakukan yaitu pengumpulan data dari media sosial Twitter sebanyak 468 data, kemudian memberikan pelabelan kelas data yang terdiri dari positif, negatif, dan netral lalu melakukan proses pembobotan menggunakan TF-IDF dan TF-RF yang bertujuan untuk melihat perbandingan proses pembobotan kedua metode tersebut. Berdasarkan dari proses dan hasil pengujian Confusion Matrix didapatkan akurasi terbaik dengan rasio 70:30 sebesar 73,94% dengan pembobotan TF-IDF.Kata Kunci: PERMENDIKBUD No 30 Tahun 2021, Kekerasan Seksual, Analisa Sentimen, Twitter, Naïve Bayes Classifier.Abstract - Sexual violence in the educational environment has increased in cases from year to year. According to data from Komnas Perempuan for the 2015-2020 period, cases of sexual violence in the educational environment show that the educational environment is no longer a safe place for students. Based on case data that was reported to Komnas Perempuan in 2015-2020 the highest cases of sexual violence occurred in universities as much as 27%, then followed by Islamic boarding schools or religion-based education as much as 19% and the rest occurred at the high school/vocational level as much as 15%, SMP 7 %, at the level of TK, SD, SLB and Christian-based education each as much as 3%. The forms of sexual violence that occur in the educational environment are in the form of rape, sexual abuse, and sexual harassment as well as psychological violence and discrimination by expelling students from school. These various cases prompted the Ministry of Education, culture, research, and Technology of the Republic of Indonesia to make Ministerial Regulation No. 30 of 2021 with the aim of dealing with various sexual violence that is still happening in the education environment. However, after the issuance of Ministerial regulation number 30 of 2021, it gave rise to various positive and negative sentiments from the community, both from human rights organizations and religious organizations. Public opinion can be used as evaluation material for the government to assess the policies that have been made. This study discusses the sentiment analysis of Minister of Education and Culture No. 30 of 2021 by comparing word weights using the Naïve Bayes Classifier method. The first step that the author took was collecting data from Twitter social media as much as 468 data, then labeling the data classes consisting of positive, negative, and neutral then carrying out a weighting process using TF-IDF and TF-RF which aims to compare the two weighting processes the method. Based on the process and results of the Confusion Matrix test, the best accuracy was obtained with a 70:30 ratio of 73.94% with TF-IDF weighting.Keywords: PERMENDIKBUD No 30 of 2021, Sexual Violence, Sentiment Analysis, Twitter, Naïve Bayes Classifier.
Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER Dicky Abimanyu; Elvia Budianita; Eka Pandu Cynthia; Febi Yanto; Yusra Yusra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4382

Abstract

Abstrak - Pesatnya peningkatan jasa internet saat ini, ada banyak informasi yang dihasilkan dalam jumlah besar secara terus menerus dalam waktu yang singkat. Akhir-akhir ini, analisis sentimen dengan menggunakan ulasan dan pesan telah menjadi topik penelitian yang populer dibicarakan di bidang Natural Language Processing. Selama bertahun-tahun, permainan online telah menjadi suatu aktivitas yang tidak bisa dipisahkan dari sebagian besar orang. Apex Legends adalah salah satu contoh game yang sangat popular di seluruh dunia. Untuk mendapatkan informasi bagaimana pendapat para pemain tentang permainan ini diperlukan analisis sentimen. Pada penelitian ini dilakukan analisis sentimen menggunakan bantuan aplikasi Orange Data Mining dengan metode VADER pada akun twitter Apex Legends menggunakan data sebanyak 500 tweet. Pengujian data dilakukan dengan membandingkan hasil yang didapat menggunakan metode VADER dengan hasil pengujian pakar, yaitu native speaker dari Canada dan Amerika. VADER mengklasifikasikan data yang didapatkan melalui twitter berdasarkan nilai compound yang didapat. Penelitian ini menghasilkan kesimpulan yaitu perbandingan dari pengujian menggunakan VADER dan pengujian pakar tidak berbeda jauh, yang mana total persentase dari penggunaan metode VADER untuk menganalisis sentiment dari twitter ini adalah : Positif = 18%, Negatif = 4,6%, Netral = 73,6%. Sedangkan   hasil pengujian pakar adalah : Positif = 27%, Negatif = 10,8%, Netral = 62,2%.Kata kunci: VADER, Apex Legends, Game, Twitter, Uji Pakar Abstract - With the rapid increase in internet services today, there is a lot of information produced in large quantities continuously in a short time. Recently, sentiment analysis using reviews and messages has become a popular research topic discussed in the Natural Language Processing field. Over the years, online gaming has become an activity that cannot be separated from most of the people. Apex Legends is one example of a game that is very popular around the world. To get information on how the players think about the game, sentiment analysis is needed. In this study, sentiment analysis was carried out using the Orange Data Mining application with the VADER method on the Apex Legends twitter account using 500 tweets (data). Data testing is done by comparing the results obtained using the VADER method with the results of expert testing, native speaker from Canada and America. VADER classifies the data obtained through twitter based on the compound value obtained. This study concludes that the comparison of testing using VADER and expert testing is not much different, where the total percentage of using the VADER method to analyze sentiment from Twitter is : Positive = 18%, Negative = 4,6%, Neutral = 73,6%. While the results of expert testing is : Positive = 27%, Negative = 10,8%, Neutral = 62,2%.Keywords : VADER, Apex Legends, Game, Twitter, Expert Test (Uji Pakar)
Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4424

Abstract

Abstrak - Kasus kecurangan pedagang mencampur daging sapi dengan daging babi masih terjadi hingga saat ini. Membedakan daging sapi dan babi dapat dilakukan dengan mengamati secara langsung satu persatu, tetapi hal ini dapat dilakukan oleh para ahli, Tetapi secara kasat mata masih sulit membedakannya. Perilaku pedagang seperti ini sangat merugikan konsumen khususnya pemeluk agama Islam karena berkaitan dengan makanan yang halal atau haram. Pada penelitain ini menggunakan metode Deep Learning untuk klasifikasi citra dengan Convolutional Neural Network (CNN) arsitektur ResNet-50. Jumlah data sebanyak 457 citra yang terbagi menjadi 3 kelas, yaitu daging babi, daging oplosan dan daging sapi. Setiap kelas memiliki ukuran gambar yang sama yaitu 300 x 300 pixel. Pembagian data menggunakan split data dengan perbandingan 70% data uji : 30% data uji, 80% data latih : 20% data uji, dan 90% data latih : 10% data uji. Hasil dari pengujian model dengan Confusion Matrix menunjukkan performa klasifikasi tertinggi dengan 100% accuracy, 100% precision, dan 100% recall, pada data citra asli dengan penggunaan batch size 32, 0.001 learning rate, epoch 75 dan split data 90% : 10%.Kata kunci: Convolutional Neural Network, Daging Babi dan Sapi, Deep Learning, Klasifikasi Citra, ResNet  Abstract - Traders mixing beef and pork are still committing fraud today. Although professionals can discern between beef and pork by watching them one by one, it is still impossible to do so with the naked eye. This kind of behavior is very detrimental to consumers, especially Muslims because it is related to halal or haram food. This research uses Deep Learning method to classify images with Convolutional Neural Network (CNN) ResNet-50 architecture. The number of data is 457 images which are divided into 3 classes, namely pork, mixed meat and beef. Each class has the same image size, which is 300 x 300 pixels. data distribution using split data with a comparison of 70% training data: 30% test data, 80% training data: 20% test data, and 90% training data: 10% test data. The results of model testing using the Confusion Matrix show the highest classification performance with 100% accuracy, 100% precision, and 100% recall, on the original image data using batch size 32, 0.001 learning rate, epoch 75 and split data 90%: 10%..Kata kunci: Convolutional Neural Networ, Deep Learning, Image Classification, Pork and Beef, ResNet
Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology Harni, Yulia; Afrianty, Iis; Sanjaya, Suwanto; Abdillah, Rahmad; Yanto, Febi; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3625

Abstract

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.
Steganografi Gambar Menggunakan Metode Least Significant Bit Pada Citra Dengan Operasi XOR Adha, Martin; Yanto, Febi; Handayani, Lestari; Pizaini, Pizaini
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5262

Abstract

One way to secure secret messages from other unauthorized parties is steganography. One of the most widely used methods in steganography is Least Significant Bit. This research uses images as cover images and secret images. The image is resized to a resolution of 512x512 pixels, The cover image uses an RGB channel image and the secret image also uses an RGB channel image. In this research, LSB will be combined with triple XOR so that it can increase the security of this message hiding method. Triple XOR is used to provide extra security to images that have a secret image (Stego Image) inserted. In this research, several tests were also carried out, including testing the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), for robustness testing it was also carried out by making modifications to the stego image such as resizing, compressing, and adding and reducing contrast. The results of this research's PSNR testing are very good, namely approximately 49 dB and lower MSE. With the PSNR and MSE results, it can be proven that the LSB method has a good level of imperceptibility. In experiments on image resistance to modification, several experimental results show that secret image extraction in the stego image failed to be extracted, and from several experiments such as adding and reducing contrast, image rotation and lossless compression, the image inserted in the stego image was successfully extracted.
Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data Permata, Rizkiya Indah; Yanto, Febi; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5381

Abstract

Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Hatta, M Ilham; Afrianty, Iis; Afriyanti, Liza
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4233

Abstract

Kidney tumors are the third most common after prostate and bladder tumors, accounting for around 208,500 cases (2%) of all cancer cases globally. Renal Cell Carcinoma constitutes 85% of these cases, transitional cell cancer 12%, and other types 2%. In Indonesia, the incidence is 3 per 100,000 people, with a male-to-female ratio of 3.2:1. Ultrasound, CT scans, and MRI are used to detect, diagnose, and assess kidney tumors, with CT scans being crucial for evaluating complex lesions, both cystic and solid. This study uses the Image Enhancement Contrast Stretching technique to improve CT-Scan image quality for deep learning classification using the EfficientNet-B0 architecture. The dataset is split into training, validation, and testing sets in an 80:20 ratio. Hyperparameters include Adamax and RAdam optimizers with learning rates of 0.01, 0.001, and 0.0001. The highest performance was achieved using the Image Enhancement Contrast Stretching technique with the RAdam optimizer and a learning rate of 0.01, resulting in 100% accuracy, precision, recall, and F1-score. For the original dataset using the Adamax optimizer with a 0.01 learning rate, the highest performance was 99.12% accuracy, 98.28% precision, 100% recall, and 99.13% F1-score. This technique significantly enhances the performance of kidney tumor classification models.
Pengaruh Contrast Limited Adaptive Histogram Equlization dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Jannata, Nanda; Handayani, Lestari; Cynthia, Eka Pandu
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4235

Abstract

The human excretory system, comprising the kidneys, ureters, and bladder, plays a crucial role in maintaining overall body health by filtering blood and eliminating waste products, including water and toxins. However, kidneys are susceptible to various diseases, such as kidney tumors, which present a significant global health challenge, with over 430,000 new cases reported in 2020. This research focuses on using CT-scan imaging techniques to analyze and assess kidney tumors. The study employs the Image Enhancement Contrast Limited Adaptive Histogram Equalization (CLAHE) method to enhance the quality of Kidney Tumor CT-Scan images for deep learning classification using the MobileNetV2 Architecture. The dataset, consisting of 4,560 images, is divided into training, validation, and testing sets in an 80:20 ratio. Applying CLAHE with a clip limit of 20 and an 8x8 tile grid significantly improves evaluation metrics compared to non-CLAHE datasets, achieving an impressive f1-score of 99.56% and accuracy of 99.56%. This improvement is achieved using the Adam optimizer with a learning rate of 0.01. These findings underscore the efficacy of CLAHE in enhancing the model's performance in kidney tumor classification. They are particularly valuable for radiologists as they enhance diagnostic accuracy and efficiency, potentially reducing diagnostic errors and improving patient outcomes.
Peringkas teks otomatis pada artikel berbahasa indonesia menggunakan metode maximum marginal relevance Idhafi, Zaky; Agustian, Surya; Yanto, Febi; Safaat H, Nazruddin
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6311

Abstract

Automated text summarization is a method for retrieving the essence of one or more text documents. Automatic Text Summarizer is needed for a faster and more efficient process of reading, searching, and understanding information. This study proposes the Maximum Marginal Relevance method to carry out the text summarization process automatically. The method was developed and tested on each of the 150 Indonesian article documents. The summary is generated from the similarity score between sentences calculated using cosine similarity. MMR's performance in producing summaries was evaluated using ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which compares them to gold-generated summaries. Test results for a compression rate of 50% gave F1 scores on ROUGE-1, ROUGE-2, and ROUGE-L at 71.86%, 64.18%, and 71.56%, respectively. In comparison, the test results with a compression rate of 30% produced F1-scores for ROUGE-1, ROUGE-2, and ROUGE-L, respectively 62.95%, 53.61%, and 62.47%. Compared to previous studies, this study produced better scores.
Co-Authors Abdul Haris Abdussalam Al Masykur Adha, Martin Afiana Nabilla Zulfa Afriyanti, Liza Afroni, Hallend Agustina, Auliyah Alfitra Salam Alwis Nazir Andri Andri Aprilia, Risma Arif Mudi Priyatno Ariq At-Thariq Putra Baehaqi citra ainul mardhia putri Dafwen Toresa Dea Ropija Sari Destri Putri Yani Dewi, Nurika Dicky Abimanyu Dimas Ferarizki Dwitama, Raja Zaidaan Putera Dzaky Abdillah Salafy Edriyansyah Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Fadhilah Syafria Fajar Febriyadi Fajri Fahreza Azeta Faris Apriliano Eka Fardianto Faris Fauzan Ray T Fauziyyah, Laila Nurul Fitra Kurnia Fitri Insani Fitri Insani Gusman, Deddy Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Hallend Afroni Hanif, Wan Muhammad Harni, Yulia Hatta, M Ilham Hidayat, Rizki Ichsan Permana Putra Idhafi, Zaky Iis Afrianty Iis Afrianty Ikhsan Hidayat Ikhwanul Akhmad DLY Illahi, Ridho Iqbal Salim Thalib Irma Welly, Irma Irsyad , Muhammad Isnan Mellian Ramadhan Iwan Iskandar Iwan Jannata, Nanda Jasril Jasril Jasril Jasril Jasril Jasril Jeki Dwi Arisandi Kurniansyah, Juliandi Lestari Handayani Lestari Handayani Lisnawita Lisnawita M Fikry M Ikhsan Maulana M. Afdal M. Fadil Martias Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Morina Lisa Pura Muhammad Affandes Muhammad Fahri Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Haiqal Dani Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nadila Handayani Putri Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Niken Aisyah Maharani Herwanza Nining Erlina Novriyanto Novriyanto Nurika Dewi Okta Silvia M Permata, Rizkiya Indah Pizaini Pizaini Prananda, Alga Pratama, Dandi Irwayunda Putra, Wahyu Eka Putri Ayuni, Desy Putri Zahwa Rahma Shinta Rahmad Abdillah Rahman, Muhammad Taufikur Rahmat Al Hafiz Raja Joko Musridho Reski Mai Candra Reski Mai Candra Reski Mai Candra Rometdo Muzawi, Rometdo Roni Setyawan RR. Ella Evrita Hestiandari Sandy Ilham Hakim Syasri Sarah Lasniari Sarah Lasniari Shahira, Fayza Siti Ramadhani Sofiyah, Wan Sugandi, Hatami Karsa Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Ulfah Adzkia Wang, Shir Li Wijaya, Andy Huang Wirdiani, Putri Syakira Yenggi Putra Dinata Yuli Novita Sari, Yuli Novita Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra