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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Proceedings of KNASTIK Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Elektro INFORMAL: Informatics Journal Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika Jurnal Khatulistiwa Informatika Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Applied Information System and Management ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JUMANJI (Jurnal Masyarakat Informatika Unjani) JURTEKSI RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Ilmiah Mandala Education (JIME) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Systemic: Information System and Informatics Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Cyber Security dan Forensik Digital (CSFD) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) International Journal of Advances in Data and Information Systems International Journal of Marine Engineering Innovation and Research Edunesia : jurnal Ilmiah Pendidikan Journal of Innovation Information Technology and Application (JINITA) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma Techno Jurnal Pengabdian Informatika (JUPITA) Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Scientific Journal of Informatics Control Systems and Optimization Letters Signal and Image Processing Letters Scientific Journal of Engineering Research SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Edumaspul: Jurnal Pendidikan Methods in Science and Technology Studies JOCHAC
Abdul Fadlil
Ahmad Dahlan University
Religion Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Detection of Fuel Purity Using the TCS3200 Sensor Using the Euclidean Distance Function Muhamad Daffa Al Fitra; Abdul Fadlil
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i3.8260

Abstract

Petroleum is oil produced by nature. It widely consumed by two-wheeled and four-wheeled vehicles. This research was conducted in order to know the RGB value of each fuel oil. This study aims to examine the purity of each fuel oil. Calculations in this study were carried out using the Euclidean Distance function aiming to find accuracy from the similarity of the average value and standard deviation of each fuel oil. In this study, detecting the purity of fuel oil using the TCS3200 sensor using the Arduino Uno as microcontroller and for output using I2C LCD 16x2. Before detecting fuel oil, sensor calibration is carried out for each fuel oil. After performing the calibration, 30 data collected. Data processing was carried out after the data was obtained, a search was carried out for the average and standard deviation of the RGB values for each fuel oil. After obtaining the values of the mean and standard deviation, we recalculate using the Euclidean Distance function because we get the similarity of the values of the mean and standard deviation. In the calculation of the accuracy of the Euclidean Distance function, it is found that the matching value of Pertamax is 25, Pertamax Turbo is 21, and Dexlite is 28. In this calculation, an accuracy of 82% is obtained.
Identifying Hate Speech in Tweets with Sentiment Analysis on Indonesian Twitter Utilizing Support Vector Machine Algorithm Imam Riadi; Abdul Fadlil; Murni Murni
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22470

Abstract

Twitter had 24 million users in Indonesia at the beginning of 2023. Despite having fewer users than other platforms, its fast and instant nature makes Twitter a significant source of information dissemination. Tweets shared on Twitter offer various advantages. However, it also has negative consequences, including the dissemination of fake news, instances of cyberbullying, and the expression of hate speech. Specifically, hate speech employs offensive language to discriminate against an individual or group based on race, ethnicity, nationality, religion, gender, sexual orientation, or other personal attributes, leading to discord. Such behavior comes under the jurisdiction of various legal statutes, including the Constitution, the Criminal Code, and the ITE Law. The primary objective of this research is to categorize tweets shared on Twitter into hate speech and non-hate speech sentiments, utilizing a Support Vector Machine (SVM) algorithm based on a dataset of 5,000 tweets. This research involved data preprocessing, labeling, feature extraction using TF-IDF, model training (80%), and testing (20%). The final stage includes enhancing SVM parameters through GridSearch and cross-validation methods (GridSearchCV), followed by analysis using a Confusion Matrix with the Matplotlib Library. Radial Basis Function (RBF) kernels, defined by parameters C=10 and gamma=0.1, exhibited the highest performance among SVM models, boasting an 84% accuracy. The RBF kernel also attained 85% precision, 97% recall, and a 91% F1-score for hate speech identification. In conclusion, the evaluation of SVM kernel performance highlights the superiority of RBF kernels in achieving the highest accuracy, complemented by nuanced insights into hate speech precision, recall, and F1-score values across various kernel types.
Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk Klasifikasi citra Batik Pekalongan Rizky Andhika Surya; Abdul Fadlil; Anton Yudhana
Jurnal Informatika: Jurnal Pengembangan IT Vol 2, No 2 (2017): JPIT, Juli 2017
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v2i2.520

Abstract

Batik merupakan warisan budaya Indonesia yang harus kita jaga dan lestarikan. Proses melestarikannya yaitu dengan pendataan identitas batik tersebut secara komputerisasi. Proses tersebut diawali dengan pengenalan pola untuk mencari informasi dari citra batik tersebut menggunakan proses ekstraksi ciri dengan metode GLCM (Gray Level Co-Occurrence Matrix) dan Filter Gabor, kemudian proses klasifikasi menggunakan Jaringan Syaraf Tiruan. Penelitian ini membuat sistem ekstraksi ciri citra batik yang akan digunakan untuk proses selanjutnya yaitu klasifikasi yang dapat digunakan untuk pendataan citra batik, khususnya batik Pekalongan. Pada penelitian ini proses pengumpulan data melalui tiga cara, yaitu observasi, wawancara dan studi pustaka. Dalam pengimplementasiannya menggunakan Matlab 2010a. Pengujian menggunakan empat sampel citra batik tradisional Pekalongan, setiap citra dibagi menjadi beberpa bagian dan selanjutnya diuji dengan metode tersebut. Hasil penelitian ini telah menghasilkan beberapa niai metode GLCM dan hasil citra proses ekstraksi ciri metode Filter Gabor yang dapat digunakan untuk proses klasifikasi citra batik.
Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application Fadlil, Abdul; Fathurrahman, Haris Imam Karim; Lin, Yu-Hao; Kamilah, Farhah; Sunardi, Sunardi
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.18879

Abstract

Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window.
Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis Abdul Fadlil; Imam Riadi; Fiki Andrianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.19798

Abstract

Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.
Sistem Pendukung Keputusan Pemilihan Lahan Penanaman Sirih Menggunakan Metode Analytical Hirarchy Process (AHP) Rezki; Umar, Rusydi; Fadlil, Abdul
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 8 No 1 (2024): JANUARY-MARCH 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v8i1.1421

Abstract

Land is a prerequisite that must be considered in betel leaf cultivation, as it significantly influences the quality of the grown betel leaves. Poor-quality land can result in decreased betel leaf harvest, reduced shelf life, susceptibility to rot, curling, and lower selling prices. Based on this issue, a system is needed to facilitate the community in selecting the appropriate land for betel leaf cultivation, which is the Decision Support System (DSS). In this research, the SPK utilizes the Analytic Hierarchy Process (AHP) method with criteria such as soil pH, air temperature, rainfall, soil elevation, and sunlight, as well as alternatives represented by the villages of Mariat Gunung, Klaru, Klamono, and Malasom. The ranking is determined based on a consistency ratio value of <0.1, which is interpreted as being appropriate according to the CR calculation theory. The highest-ranking land is found in Klaru Village (rank 1), followed by Mariat Gunung (rank 2), Klamono (rank 3), and Malasom (rank 4). In conclusion, the AHP method can be utilized for the decision-making process in selecting land for betel leaf cultivation
Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis Andrianto, Fiki; Fadlil, Abdul; Riadi, Imam
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9266

Abstract

Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in the future, sentiment analysis is needed. The analysis research tweets used were 4165 datasets using the python programming language. Sentiment analysis research stages include data collection, preprocessing, labeling, tf-idf weighting, split data, SVM model analysis and result evaluation. The data is then divided into 80% training data and 20% testing data, 50% training data and 50% testing data, 20% training data and 80% testing data. The results of the svm algorithm testing scenario obtained the highest optimization with an accuracy value of 97%, F1-score value on positive labels 88% and negative 98%, also obtained a positive recall value of 80% and negative 100% precision value on positive labels 98% and negative 97%, on 80% training data and 20% testing. It can be concluded that in this case, the linear svm algorithm is able to work to recognize models with a high level of accuracy so that in the future it can be used in similar cases.
Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm Jogo Samodro, Maulana Muhamammad; Biddinika, Muhammad Kunta; Fadlil, Abdul
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94575

Abstract

In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively.
PELATIHAN DATABASE ADMINISTRATOR SISWA SMK INFORMATIKA WONOSOBO Maftukhah, Ainin; Subandi, Rio; Umar, Rusydi; Fadlil, Abdul
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 29, No 4 (2023): OKTOBER-DESEMBER
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v29i4.49159

Abstract

Pentingnya pengelolaan database yang efektif dalam dunia digital yang terus berkembang. Pentingnya pelatihan database untuk siswa dalam mengelola dan mengimplementasikan database menggunakan perintah SQL. Kegiatan pemberdayaan umat dilakukan dengan urutan langkah-langkah sebagai berikut, pertama persiapan melakukan studi literatur dan membuat database yang mudah dipahami oleh siswa. Kedua menyiapkan alat dan bahan pelatihan pembuatan database pembelajaran untuk mengelola data siswa. Ketiga mengidentifikasi dan menyiapkan materi, pretest, dan postest yang akan diberikan kepada peserta saat kegitan. Hasil kegiatan pemberdayaan umat yang dilaksanakan pada hari Senin, 12 Juni 2023 secara offline diikuti 20 siswa-siswi dari kelas X hingga XI SMK Informatika Wonosobo.Kegiatan pemberdayaan umat yang diselenggarakan menghasilkan pretest dan postest, terdapat perubahan pemahaman dan keterampilan peserta pelatihan administrator database. Hal ini ditunjukkan dengan nilai prestes 46,8%, sedangkan postest 48,5%.
Klasifikasi Penyakit Diabetes dengan Algoritma Decision Tree dan Naïve Bayes samodro, maulana muhammad jogo; Biddinika, Muhammad Kunta; Fadlil, Abdul
RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Vol 6, No 2 (2023): RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer)
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/resistor.6.2.113-118

Abstract

Diabetes merupakan salah satu penyakit yang dapat menyebabkan kematian prematur. Pada tahun 2019 ada 463 juta orang terkena penyakit tersebut. Di tahun 2021penderita diabetes bertambah 74 juta menjadi 537 juta. Maka perlu adanya pengklasifikan guna mendeteksi dini penyakit diabetes. Machine learning adalah suatu sistem dengan latar belakang ilmu statistika dan matematika. ML mampu untuk melakukan pengklasifikasian kelas sehingga dapat diketahui prediksi terkena penyakit diabetes. Penelitian menggunakan 2 algoritma dalam machine learnig yaitu naïve bayes dan decision tree. Data penelitian sebanyak 520 data, dengan 300 data training, 220 data testing. Hasil penelitian menunjukkan dengan algoritma naïve bayes menghasilkan tingkat akurasi sebesar 90,45%. Algoritma decision tree mempunyai tingkat akurasi sebesar 96,36%.
Co-Authors Aang Anwarudin Abdul Azis Achmad Nugrahantoro Aditiya Dwi Candra Ahmad Naufal, Ahmad Ahmat Taufik Aji Pamungkas Akrom, Akrom Alfiansyah Imanda Putra Alfiansyah Imanda Putra Alfian Amiruddin, Nanda Fahmi Andrianto, Fiki Anggit Pamungkas Annisa, Putri Anton Yudhana Anwar Siswanto ANWAR, FAHMI ardi, Ardi Pujiyanta Arief Setyo Nugroho Arief Setyo Nugroho Arif Budi Setianto Arif Budiman Arif Budiman Arif Wirawan Muhammad Aris Rakhmadi Asep Ririh Riswaya Asno Azzawagama Firdaus Atmojo, Dimas Murtia Aulia, Aulia Az-Zahra, Rifqi Rahmatika Aznar Abdillah, Muhamad Bagus Primantoro Bashor Fauzan Muthohirin Basir, Azhar Budiman, Dheni Apriantsani Candra, Aditiya Dwi Darajat, Muhammad Nashiruddin Davito Rasendriya Rizqullah Putra Dewi Soyusiawaty Dewi Soyusiawaty Dhimas Dwiki Sanjaya Dian Permata Sari Dianda Rifaldi Dikky Praseptian M Dimas Murtia Atmojo Doddy Teguh Yuwono Dwi Susanto Dwi Susanto Edy Fathurrozaq Egi Dio Bagus Sudewo Eko Budi Cahyono Eko Prianto Eko Prianto Elvina, Ade Ermin Al Munawar Ermin Ermin Esthi Dyah Rikhiana Fahmi Anwar Fahmi Auliya Tsani Fahmi Auliya Tsani Fahmi Fachri Fanani, Galih Faqihuddin Al-anshori Faqihuddin Al-Anshori, Faqihuddin Fathurrahman, Haris Imam Karim Fauzi Hermawan Fiki Andrianto Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Yasin Fitri Muwardi Furizal Gusrin, Muhaimin Gustina, Sapriani Hafizh, Muhammad Nasir Haksono, Muhammad Rizky Hanif, Abdullah Hanif, Kharis Hudaiby Harman, Rika Helmiyah, Siti Hendril Satrian Purnama Herdiyanto, Erik Herman Herman Herman Yuliansyah, Herman Herman, - Ibnu Rifajar Ibrahim Mohd Alsofyani Ibrahim, Rohmat Ihyak Ulumuddin Ikhsan hidayat Ilhamsyah Muhammad Nurdin Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Irjayana, Rizky Caesar Irwansyah Irwansyah Izzan Julda D.E Purwadi Putra januari audrey Jayawarsa, A.A. Ketut Jogo Samodro, Maulana Muhamammad Joko Supriyanto Joko Supriyanto Kamilah, Farhah Kartika Firdausy Khoirunnisa, Itsnaini Irvina Kusuma, Nur Makkie Perdana Laura Sari Lestari, Yuniarti Lin, Yu-Hao Luh Putu Ratna Sundari M. Nasir Hafizh Maftukhah, Ainin Maulana Muhammad Jogo Samudro Mini, Ros Mohd Hatta Jopri Muammar Mudinillah, Adam Mufaddal Al Baqir Muh. Fadli Hasa Muhaimin Gusrin Muhajir Yunus Muhamad Daffa Al Fitra Muhamad Rosidin Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Johan Wahyudi Muhammad Kunta Biddinika Muhammad Ma’ruf Muhammad Nasir Hafizh Muhammad Nur Faiz Muhammad Nurdin, Ilhamsyah Muhammad Rizki Setyawan Mukti, Sindhu Hari Muntiari, Novita Ranti Murinto Murinto - Murinto Murinto Murni Murni Musliman, Anwar Siswanto Mustofa Mustofa Muthorihin, Bashor Fauzan Mutiara Titani Muwardi, Fitri Nasution, Dewi Sahara Nasution, Musri Iskandar Nilam Tri Astuti Nurwijayanti Pahlevi, Ryan Fitrian Ponco Sukaswanto Poni Wijayanti Prabowo Soetadji Prabowo, Basit Adhi Prayogi, Denis Priambodo, Bambang Putra, Fajar R. B Putri Annisa Putri Annisa Putri Purnamasari Putri Silmina, Esi Ramadhani, Muhammad Ramdhani, Rezki Razak, Farhan Radhiansyah Rezki Rezki Rifqi Rahmatika Az-Zahra Rizky Andhika Surya Rochmadi, Tri Roni Anggara Putra Rusydi Umar Rusydi Umar S Sunardi S, Sunardi Saad, Saleh Khalifah Safiq Rosad Saifudin Saifudin Saifullah, Shoffan Saleh khalifa saad Santi Purwaningrum Sarmini Sarmini Septa, Frandika Setyaputri, Khairina Eka Setyaputri, Khairina Eka Setyaputri, Khairina Eka Shinta Nur Desmia Sari Siswahyudianto Siti Helmiyah Sri Winiarti Subandi, Rio Sukaswanto, Ponco Sukma Aji Sulis Triyanto Sunardi Sunardi Sunardi Sunardi, Sunardi Surya Yeki Surya Yeki Syamsiar, Syamsiar Syarifudin, Arma Tole Sutikno Tresna Yudha Prawira Tri Ferga Prasetyo Tristanti, Novi Tuswanto Tuswanto Virdiana Sriviana Fatmawaty Wahju Tjahjo Saputro Wahyusari, Retno Winoto, Sakti Wintolo, Hero Wulandari, Cisi Fitri Yana Mulyana Yana Mulyana Yasidah Nur Istiqomah Yeki, Surya Yohanni Syahra Yossi Octavina Yuantoro, Jody Yulianto, Dinan Yulianto, Muhammad Anas Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah Yuwono Fitri Widodo Zein, Wahid Alfaridsi Achmad Zulhijayanto -