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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 JITU : Journal Informatic Technology And Communication 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
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Real-Time Rice Leaf Disease Diagnosis: A Mobile CNN Application with Firebase Integration Azis, Abdul; Fadlil, Abdul; Sutikno, Tole
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4452

Abstract

Rice, the staple food for the majority of Indonesia's population, faces significant production threats from leaf diseases, which can decrease yields and jeopardize national food security. Traditional manual identification of these diseases is a major challenge for farmers, as it is often subjective, prone to misdiagnosis leading to incorrect treatments, time-consuming, demands specialized expertise, and is difficult to implement widely for effective real-time early prevention, allowing diseases to spread and significantly impact crop yields. This research addresses these challenges by developing an automated and easily accessible rice leaf disease diagnosis system. The system is manifested as a mobile application that integrates a Convolutional Neural Network (CNN) model, specifically utilizing the EfficientNetB0 architecture, for the classification of rice leaf images and leverages key Firebase services such as its Realtime Database for data synchronization and Cloud Storage for image management to ensure a scalable and responsive backend. The methodology involved several key stages. Firstly, the CNN model was developed by employing a transfer learning approach on the pre-trained EfficientNetB0 architecture. Secondly, the model underwent comprehensive testing using a dataset of 1,000 new rice leaf images, which were independently validated by agricultural experts. The results demonstrated that the developed CNN model achieved a global accuracy of 85.9%, with an average precision of 86.1% and recall of 85.9% (macro-average) in the expert validation testing phase with the 1,000 new images. However, the study also identified variations in the model's performance across different disease classes, highlighting areas that require further optimization to enhance detection effectiveness for specific types of rice leaf diseases. The primary benefit of this research is the provision of a practical rice leaf disease diagnosis tool that is readily accessible to farmers via a mobile application, empowering them with timely and accurate information for effective crop management. This can lead to reduced crop losses, improved yield quality, and contribute significantly to national food security. Furthermore, this research contributes to the field of applied machine learning and mobile computing in resource-constrained agricultural environments, offering valuable insights for the development of impactful informatics solutions.
Comparative Analysis Of Ant Lion Optimization And Jaya Algorithm For Feature Selection In K-Nearest Neighbor (Knn) Based Electricity Consumption Prediction Wahyusari, Retno; Sunardi, Sunardi; Fadlil, Abdul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4692

Abstract

The increase in demand for electrical energy is in line with increasing population, urbanization, industrial deployment, and technology. Accurate prediction of electrical energy consumption plays an important role in planning, analyzing, and managing electricity systems to ensure sustainable, safe, and economical electricity supply. K-Nearest Neighbors (KNN) is a simple and fast prediction algorithm based on the quality and relevance of the features used. This research proposes to improve the accuracy of energy consumption prediction through feature selection based on metaheuristic algorithms, namely Genetic Algorithm (GA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and Jaya Algorithm (JA). The dataset used is Tetouan City Power Consumption, with a preprocessing process of time feature extraction, min-max scaling normalization, and feature selection. The ALO+KNN and JA+KNN combinations delivered the best and most stable prediction performance, while TLBO+KNN performed poorly. GA+KNN showed the worst overall results among all combinations. The evaluation of model performance was based on RMSE, MAPE, and R² metrics. These findings highlight the importance of selecting a feature selection algorithm that aligns well with the characteristics of the model and dataset to enhance prediction accuracy.
Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry Syahra, Yohanni; Fadlil, Abdul; Yuliansyah, Herman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14907

Abstract

In today’s highly competitive retail landscape, businesses face increasing challenges in retaining customer loyalty and achieving sustainable growth. A common issue, particularly among small and medium-sized enterprises (SMEs), is the absence of a structured method for identifying and categorizing customers based on their value and behavior. This study addresses the challenge by implementing a data-driven customer segmentation approach using Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means clustering algorithm. The research utilized 2,353 transaction records from 369 unique customers collected over three years from a local retail business. After preprocessing and normalizing the RFM values using Min-Max scaling, the Elbow Method was applied to determine the optimal number of clusters, resulting in four distinct customer segments. Cluster 3, labeled “Loyal Customers,” consisted of customers with high purchase frequency and very high spending; Cluster 1 (“Potential Loyalists”) included those with moderate activity; Cluster 0 represented “At-Risk Customers,” and Cluster 2 comprised “One-Time Buyers.” This segmentation framework supports the development of targeted Customer Relationship Management (CRM) strategies, such as loyalty programs and re-engagement campaigns. However, the approach also has limitations, including potential data bias due to the use of static transaction records and the challenge of interpreting clusters without qualitative customer feedback. Despite these constraints, the study demonstrates the practical utility of combining RFM analysis with clustering techniques to extract actionable insights in environments with limited technical infrastructure.
Mask Detection System at the Entry of a Room Herdiyanto, Erik; Fadlil, Abdul
Signal and Image Processing Letters Vol 5, No 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i1.49

Abstract

This study focuses on automatic mask detection tools that can open doors in a room to minimize violations of health protocols, one of which is the use of masks during the pandemic. The method used in this study is the CNN classification method. Where the CNN calcification method has several stages in it, including pre-processing, training, and testing. In the pre-processing, all image data used will be labeled using Labeling.axe. The training process at CNN uses TensorFlow framework version 1.15. In the testing process, the test and data testing will be carried out in real-time by entering new images and models that are made and then a classification process is carried out on objects caught by the camera, classified images are marked with boxes and names of data classes. This data class is divided into two, namely data on wearing masks and without masks. The results of the test were carried out by entering 200 facial image data. The system can correctly detect as much as 190 times from 200 data tested with an Accuracy rate of 95%. Based on the test results, it shows that the resulting model is good and suitable for the classification process of recognizing mask detection images. However, to produce a better model requires data with more variety and a larger amount of data.
Real Time Clock (RTC) Module Based Dance Humanoid Robot Timer System Amiruddin, Nanda Fahmi; Fadlil, Abdul
Signal and Image Processing Letters Vol 5, No 2 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i2.56

Abstract

The Indonesian Dance Robot Contest (KRSTI) is a competition for the manufacture, design, and programming of dance robots with elements of the arts and culture of the dance department, especially the archipelago. The obstacle faced by the robot is when the robot is required to stop according to time on the music but there is a movement that appears when the time has been declared over. The method used is using the RTC module. The DS3231 type RTC module is a circuit that functions to store time and date with accuracy and precision and is integrated with the AT24c32 eeprom serial for other data storage purposes. The results of time research testing on this robot are running well, the first results obtained are that the robot can adjust the time when it runs. Furthermore, at the time of pause the RTC module does not interfere with the running of the robot. Finally, the success rate of the robot stopping at the specified time is 100%, the robot can be tested with time according to the user's wishes.
Penggunaan Teknologi Tools Powerpoint dan Canva untuk Media Informasi Riadi, Imam; Fadlil, Abdul; Andrianto, Fiki; Elvina, Ade; Fanani, Galih; Nasution, Dewi Sahara
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 6 No 2 (2022): Mei
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v6i2.11781

Abstract

Program pemberdayaan masyarakat ini bertujuan untuk menggunakan media Power Point dan Canva sebagai media penyampaian informasi yang inovatif dan meningkatkan soft skill desain. Pelatihan ini diikuti 15 mahasiswa baru program studi Sistem Informasi STIMIK Muhammadiyah Jakarta. Program ini melakukan 2 sesi pertemuan online melalui Zoom Meating karena Universitas mempraktikkan home learning, ada kebijakan tersebut terkait wabah Covid19. Pertemuan pertama memberikan materi tentang perkembangan teknologi dan media informasi sebagai tambahan wawasan. Pertemuan kedua memberikan tutorial tentang cara menggunakan Power Point dan tools Canva untuk mendesain informasi, yaitu desain sertifikat, desain undangan, desain animasi PPT, dan konten digital lainnya. Hasil tes pretest dan postest menunjukkan bahwa nilai presentase mahasiswa baru meningkat sebesar 13%. Hasil ini mengindikasikan adanya sedikit peningkatan dalam pengetahuan wawasan dan soft skill tentang teknologi media informasi. 
MoLLe: A Hybrid Model for Classifying Diseases in Chili Plants Using Leaf Images Khoirunnisa, Itsnaini Irvina; Fadlil, Abdul; Yuliansyah, Herman
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29071

Abstract

Purpose: Leaf diseases are often early indicators of problems in plants. More detailed image information with feature extraction on leaves can improve accuracy. However, MobileNetV2 tends to be less than optimal in capturing the fine texture characteristics of leaves. This research aims to propose a classification model for diseases in chili plants based on leaf images using MobileNetV2 with Local Binary Pattern (LBP), with three fully connected layers (220-120-60 neurons) using the ReLU activation function, referred to as MoLLe. Methods: This research consists of six stages. It begins with a dataset collected from chili farms comprising 900 images, which are then preprocessed into 3,600 images. Next, LBP feature extraction is performed. After that, a comparison between the benchmark architecture and the proposed architecture is conducted. A softmax layer is used to perform three-class classification. The MoLLe model was tested with the MobileNetV2 and MobileNetV2+LBP benchmark architectures and evaluated using a confusion matrix. Result: Based on the evaluation conducted, using batch size 32, learning rate 0.001, and 20 epochs, the MoLLe model experienced early stopping at epoch 11, achieving an accuracy of 0.97 training data, 0.84 validation data, and 0.91 testing data. The evaluation results showed consistent precision, recall, and F1-score values of 0.91, indicating the model's balanced ability to identify the three disease classes. Novelty: The novelty of this research lies in the integration of MobileNetV2 and LBP with modifications to three fully connected layers, which not only reduces the number of training parameters but also accelerates the detection process. This research makes an essential contribution to the development of more efficient and effective plant disease detection systems, with experimental results showing that MoLLe outperforms the benchmark architecture.
Pengenalan Dan Pelatihan UI/UX Serta Jenjang Karir Di Masa Depan untuk Siswa Siswi SMK Informatika Wonosobo Fadlil, Abdul; Murinto; Firdaus, Asno Azzawagama; Rifaldi, Dianda
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2023): Desember
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i3.20285

Abstract

Artikel ini menyajikan kegiatan pengabdian yang dilaksanakan pada 12 Juni 2023 di SMK Informatika Wonosobo, Jawa Tengah. Kegiatan tersebut difokuskan pada pengenalan desain UI/UX dan pelatihan terkait desain UI/UX untuk membantu siswa mempersiapkan karir di bidang tersebut di masa depan. Sebanyak 20 orang siswa ikut serta dalam kegiatan ini yang didampingi oleh pihak sekolah. Peserta menunjukkan antusiasme yang tinggi selama kegiatan berlangsung. Kegiatan berupa sosialisasi dan tanya jawab hingga praktik langsung ini memang baru kali pertama diselenggarakan pada SMK Informatika Wonosobo tersebut sehingga siswa belum memiliki pemahaman mengenai desain UI/UX. Hal tersebut terlihat dari peningkatan skor akhir yang signifikan dalam evaluasi pra dan pasca pembekalan menggunakan pre test dan post test dengan metode perhitungan likert. Skor akhir meningkat dari 44,2% pada pre test menjadi 93,6% pada post test. Hasil ini menunjukkan bahwa kegiatan pengabdian ini berhasil meningkatkan pemahaman dan pengetahuan peserta dalam bidang desain UI/UX. Pihak sekolah mengharapkan kegiatan serupa dapat tetap dilaksanakan di SMK Informatika Wonosobo guna meningkatkan pengetahuan dan pemahaman siswa mengenai dunia kerja.
Performance Comparison of Learned Features from Autoencoder and Shape-Based Hu Moments for Batik Classification Dzulqarnain, Muhammad Faqih; Fadlil, Abdul; Riadi, Imam
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4827

Abstract

Batik classification depends critically on effective feature extraction to capture the unique geometric and visual characteristics of batik patterns. This study compares two distinct feature extraction methods for batik classification: learned features extracted via a convolutional autoencoder, and shape-based handcrafted features derived from Hu Moments. While autoencoders automatically learn complex latent representations that adapt to intricate pattern variations, Hu Moments provide invariant shape descriptors robust to rotation, scaling, and translation. The methodology involves extracting Hu Moment features and autoencoder latent features from the same batik image dataset, followed by evaluation with identical classifiers to ensure a fair comparison. Experimental results reveal key trade-offs: Hu Moments offer robustness and interpretability in capturing shape geometry, whereas autoencoder features better model complex, non-linear patterns. These findings highlight the complementary strengths of classical and learned feature extraction techniques, offering valuable insights for optimizing batik classification. This research advances feature extraction methodologies in cultural heritage image analysis, with broader applicability to pattern-rich domains like batik classification.
Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost Sarmini, Sarmini; Sunardi, Sunardi; Fadlil, Abdul
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46308

Abstract

Fraud detection in imbalanced datasets, where fraudulent transactions represent a small fraction of total data, presents a major challenge for machine learning models. Traditional classifiers often perform poorly in such scenarios due to their bias toward the majority class. This study investigates the effectiveness of two data augmentation techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Generative Adversarial Networks (CGAN) in improving fraud detection performance. Both methods are applied to balance the dataset, and their impact is evaluated using two classifiers: Random Forest (RF) and XGBoost. The models are tested across three versions of the dataset: the original imbalanced data, the SMOTE-augmented data, and the CGAN-augmented data. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC. Results indicate that both augmentation techniques enhance the models' ability to detect fraudulent transactions compared to the original dataset. Notably, CGAN outperforms SMOTE in terms of recall and F1 score, suggesting its ability to generate more diverse and realistic synthetic samples. While SMOTE creates new samples through interpolation, CGAN uses an adversarial process involving a generator and a discriminator, resulting in more complex data representations. The study also finds that XGBoost combined with CGAN yields the highest performance, effectively capturing intricate fraud patterns. In contrast, SMOTE, though beneficial, shows limited capacity in improving recall. This research highlights the importance of advanced augmentation techniques like CGAN in addressing class imbalance and improving fraud detection systems. It also opens pathways for future exploration of deep learning-based augmentation and classification methods in fraud detection.
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 Anwarudin, Aang 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, Eko 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 Yudhana , Anton Yulianto, Dinan Yulianto, Muhammad Anas Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah Yuwono Fitri Widodo Zein, Herman Zein, Wahid Alfaridsi Achmad Zulhijayanto -