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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM INFORMASI BISNIS Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Disease Prevention and Public Health Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik CommIT (Communication & Information Technology) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Jurnal sistem informasi, Teknologi informasi dan komputer Sinergi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JURNAL NASIONAL TEKNIK ELEKTRO KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO Emerging Science Journal JIKO (Jurnal Informatika dan Komputer) Jurnal CoreIT Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) MUST: Journal of Mathematics Education, Science and Technology IT JOURNAL RESEARCH AND DEVELOPMENT Insect (Informatics and Security) : Jurnal Teknik Informatika Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan JRST (Jurnal Riset Sains dan Teknologi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Informatika Universitas Pamulang Applied Information System and Management ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Ilmiah Mandala Education (JIME) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Jurnal Pengabdian Masyarakat Bumi Raflesia Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat JUKI : Jurnal Komputer dan Informatika JITU : Journal Informatic Technology And Communication Journal of Innovation Information Technology and Application (JINITA) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Pengabdian Masyarakat Indonesia Jurnal Nasional Pengabdian Masyarakat Jurnal Puan Indonesia Jurnal Informatika Teknologi dan Sains (Jinteks) Techno SIENNA Engineering Science Letter Jurnal Informatika: Jurnal Pengembangan IT Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika Al'Adzkiya International of Computer Science and Information Technology Journal Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika JOCHAC Jurnal Komputasi Genta Mulia Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Sunardi, Sunardi
Universitas Ahmad Dahlan Yogyakarta
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Comparison of LSTM and Random Forest for Hydrogen Production Prediction in Alkaline Water Electrolysis Ulil Amri Ulil; Ardi Pujiyanta; Sunardi Sunardi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13254

Abstract

Hydrogen has emerged as a promising clean energy carrier capable of supporting the transition toward sustainable energy systems. In alkaline water electrolysis systems, accurate prediction of hydrogen production is essential for improving system monitoring, operational efficiency, and future control strategies. This study aims to compare the performance of Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms in predicting hydrogen production based on operational parameters of an alkaline water electrolysis system. The dataset used in this study consists of more than 100,000 operational data samples collected from laboratory-scale electrolysis experiments, including voltage, current, temperature, and hydrogen gas pressure measurements. Hydrogen production was calculated using the ideal gas law and used as the target variable for model development. Prior to model training, the dataset underwent preprocessing, including data cleaning, normalization, and train-test splitting. The predictive performance of both models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Experimental results show that the Random Forest model achieved superior performance with an MAE of 0.0085, RMSE of 0.0106, and R² of 0.9905, while the LSTM model obtained an MAE of 0.0302, RMSE of 0.0361, and R² of 0.7907. The findings indicate that Random Forest is more effective than LSTM in modeling the relationship between operational parameters and hydrogen production in the investigated alkaline water electrolysis system. This study demonstrates the potential of machine learning approaches for accurate hydrogen production prediction and provides insights into the suitability of different predictive models for electrolysis-based hydrogen generation systems.
Review of Reliability of Solar Hybrid Generator System as Temporary Power Supply for Offshore Industry for Sustainable Platform Application of Environmentally Friendly Energy Sources Haryani Alamsyah; Ardiansyah; Sunardi
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1116

Abstract

This paper discusses the application of two combined power generation systems namely generator and Solar Panel in improving the efficiency of offshore power supply during downtime. Ensuring reliable and sustainable power in remote and limited range environments is critical for sustainable platform maintenance and sustainability. Traditional power sources, such as diesel generators, although reliable, have high carbon emissions and operational costs. Solar energy, although environmentally friendly, faces spatial constraints in offshore. A hybrid system combines photovoltaic (PV) panels and conventional generators, which provides an optimal balance between renewable energy and reliability. This study focuses on the system design, operational benefits, and its impact on wellhead platform sustainability, highlighting its efficiency and environmental sustainability.
IMPLEMENTASI STRATEGI TEAM ASSISTED INDIVIDUALIZATION UNTUK MENINGKATKAN PARTISIPASI SISWA DALAM PEMBELAJARAN Sunardi; Budi Santosa
Jurnal Genta Mulia Vol. 14 No. 2 (2023): JURNAL GENTA MULIA
Publisher : LPPM Universitas Cipta Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61290/gm.v14i2.526

Abstract

Rendahnya partisipasi belajar siswa kelas XI TKR 1 SMK Muhammadiyah 2 Andong pada mata pelajaran pemeliharaan mesin kendaraan ringan. Penelitian ini bertujuan untuk meningkatkan partisipasi belajar siswa XI TKR 1 SMK Muhammadiyah 2 Andong melalui strategi Team Assisted Individualization . Penelitian ini melibatkan 36 siswa dan dilakukan dengan satu siklus meliputi perencanaan, pelaksanaan, pengamatan dan refleksi. Metode penelitian yang digunakan yaitu Penelitian Tindakan Kelas atau yang lebih dikenal dengan (Classroom Action Reseach). Teknik pengumpulan data yang dilakukan ialah observasi, wawancara dan sumber lain yang dianggap mampu mengembangkan penelitian. Data yang diperoleh kemudian diambil kesimpulan dari reduksi dan penampilan data. Hasil penelitian ini menunjukan adanya peningkatan partisipasi siswa sebelum dan sesudah menggunakan model pembelajaran Team Assisted Indivudualization (TAI) partisipasi siswa rata-rata 15 anak (43%) menjadi 19 anak (52%).
Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost Sarmini Sarmini; Sunardi Sunardi; Abdul Fadlil
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.
Metadata Analysis and Encryption in Decentralized Messaging Applications using NIST SP 800-86 Firmansyah Firmansyah; Firmansyah Firmansyah; Imam Riadi; Sunardi Sunardi
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 12 No. 2 (2026): Oktober 2026
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v12i2.5702

Abstract

This study conducted a digital forensic analysis of metadata on two decentralized messaging applications: Session, based on Oxen onion routing with E2EE Libsodium, and BitChat, based on an ephemeral Bluetooth mesh network. The main objective was to compare the metadata leakage resilience of both applications and the effectiveness of the NIST SP 800-86 framework in the investigation process. The study used a forensic simulation approach on a rooted Android device in a controlled environment. The process was carried out according to the four phases of NIST SP 800-86: Collection, Examination, Analysis, and Reporting. The analyzed data included volatile data RAM dumps, BLE packets, persistent storage database SQLCipher, network artifacts, and system logs. The results showed significant differences. In Session, 47 message metadata were successfully extracted from the signal.db file along with attachment paths and onion routing hops with a 65% recovery rate. Meanwhile, BitChat only generated 12 hop events along with RSSI proximity data with a 25% recovery rate, mostly from volatile memory due to its panic wipe feature and ephemeral nature. Session is more vulnerable to timeline reconstruction, while BitChat excels in resistance to local forensics. This study concludes that no privacy-by-design application is completely immune to digital forensics. Session is better suited for conventional investigations, while BitChat provides better protection in high-risk offline scenarios. Recommendations are provided for users, investigators, and application developers to improve anti-forensic mechanisms.
Analisis Komparasi Prediksi Serangan DDoS Menggunakan Machine Learning Sunardi Sunardi; Suyahman Suyahman
Proceeding of Informatics Collaborations and Dessimenation Meeting Vol. 1 No. 1 (2025)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Serangan Distributed Denial of Service (DDoS) merupakan ancaman serius terhadap kestabilan jaringan komputer, yang dapat menyebabkan gangguan layanan pada sistem yang ditargetkan. Deteksi dini serangan DDoS sangat penting untuk meminimalkan dampaknya. Penelitian ini bertujuan untuk membandingkan kinerja empat algoritma machine learning—K-Nearest Neighbors (KNN), Random Forest (RF), XGBoost, dan Logistic Regression (LR)—dalam mendeteksi serangan DDoS menggunakan dataset besar dari Kaggle, yang terdiri dari lebih dari satu juta baris data. Dataset mencakup berbagai informasi lalu lintas jaringan, termasuk alamat IP sumber dan tujuan, port, panjang paket, serta jumlah paket yang dikirim dalam interval waktu tertentu. Hasil penelitian menunjukkan bahwa algoritma RF memiliki akurasi tertinggi, yaitu 99.83%, diikuti oleh KNN dan XGBoost dengan akurasi masing-masing 99.82%. Logistic Regression, meskipun menunjukkan akurasi yang baik sebesar 98.80%, memiliki performa yang lebih rendah dibandingkan algoritma lainnya karena keterbatasannya dalam menangani pola data non-linier. Berdasarkan hasil ini, dapat disimpulkan bahwa RF, KNN, dan XGBoost sangat efektif untuk deteksi serangan DDoS, sementara LR dapat digunakan sebagai alternatif dengan efisiensi komputasi yang lebih tinggi. Penelitian ini memberikan kontribusi signifikan terhadap pengembangan sistem deteksi DDoS berbasis machine learning yang lebih akurat dan efisien.
Machine Learning Algorithms for IT Project Risk Level Identification: A Multi Classifier Framework with Systematic Preprocessing and Per-Class Evaluation Pastima Simanjuntak; Sunardi Sunardi; Anton Yudhana
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 2 (2026)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v7i2.415

Abstract

The persistently high failure rate of Information Technology (IT) projects, driven by inadequate risk identification, remains a critical organizational challenge, and conventional risk assessment based on subjective expert judgment struggles to model the non-linear interdependencies in large-scale project data. This research proposes a multi-classifier machine learning (ML) framework for automated IT project risk level identification (Low, Medium, High), built on two contributions: a systematic, auditable preprocessing protocol (explicit target-leakage screening, a controlled before/after comparison of the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization) and a per-class ordinal-consistency evaluation that decomposes misclassifications by their severity relative to the ordinal risk structure. Five classifiers were evaluated on 10,919 historical IT project records: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The leakage screening identified and removed a variable that deterministically encoded the target label. After SMOTE and hyperparameter tuning, SVM achieved the best single-model performance (accuracy 83.65%, macro F1-score 0.759), followed by RF (82.14%, 0.731), LR (80.82%, 0.744), DT (73.72%, 0.669), and KNN (69.64%, 0.596); a five-model soft-voting ensemble reached 83.42% accuracy and a macro F1-score of 0.757. Across all sixteen model/stage configurations (7,165 aggregate misclassifications), the non-adjacent error rate, a High-risk project predicted as Low-risk or vice versa, was exactly zero, a reliability property invisible to aggregate accuracy or F1-score.
CNN-Based SIBI Sign Language Recognition Alphabet: Exploring the Impact of Hardware on Model Training Aris Rakhmadi; Anton Yudhana; Sunardi Sunardi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7071

Abstract

The recognition of Sign Language Alphabets (SLA) plays a vital role in human-computer interaction, especially for individuals with auditory disabilities. This study aims to evaluate the impact of different hardware configurations—specifically CPU, GPU, and memory setups—on the training efficiency and recognition performance of a Convolutional Neural Network (CNN)-based model for SLA using the SIBI dataset. The novelty of this research lies in its focus on hardware-aware deep learning optimization for Indonesian sign language (SIBI), an underexplored area. The model was trained on 3,468 labeled hand gesture images representing 24 SIBI alphabet signs. Experiments were conducted on CPU (Intel Xeon 2.00 GHz) and GPU (Nvidia Tesla T4) platforms using a consistent CNN architecture. The training time was significantly reduced by 45.5%, from 1 hour 39 minutes to just 54 minutes, while the accuracy remained consistent at 96.7%, showing no significant change between the two setups. These results demonstrate the significance of parallel processing and memory bandwidth in enhancing model convergence and generalization. The findings are relevant for real-time SLA deployment with hardware constraints on embedded or mobile platforms. Overall, the study underscores the importance of hardware optimization in accelerating CNN training and improving performance in sign language recognition systems.
Performance Comparison of SMOTE-Based Ran-dom Forest and Decision Tree Models for Chronic Kidney Disease Classification Rahmat Dwi Cahyono; Herman Yuliansyah; Sunardi
Engineering Science Letter Vol. 5 No. 03 (2026): Engineering Science Letter - Articles in Press
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002553

Abstract

Chronic Kidney Disease (CKD) is a chronic condition characterized by a gradual decline in kidney function and is often detected at an advanced stage. Previous studies have employed machine learning methods-such as Random Forest and Decision Trees-for CKD classification; however, there is no consensus regarding which algorithm is most optimal for CKD datasets. The large number of clinical variables in CKD data increases computational complexity, necessitating high-performance machine learning methods to improve classification accuracy and support early disease detection. This study aims to develop a CKD classification model and compare the performance of Decision Tree and Random Forest algorithms. The research utilized a health dataset obtained from Mendeley Data. The methodology involved data acquisition, exploratory data analysis, data preprocessing (including addressing class imbalance via the Synthetic Minority Over-sampling Technique, or SMOTE), and model development using Random Forest and Decision Trees, with evaluation performed via 10-fold stratified cross-validation. Model performance was assessed using confusion matrices and classification reports, covering metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that Random Forest yielded the best classification performance, achieving an accuracy of 0.9437. It is concluded that comparing Random Forest and Decision Tree methods-combined with SMOTE-is an effective approach for identifying the optimal algorithm to enhance accuracy and support decision-making systems for the early detection of CKD.
Klasifikasi Multi-Class Pada Kondisi Kesehatan Mental Berbasis Data Media Sosial Menggunakan Model BERT erlin elisa; Sunardi; Herman Yuliansyah
Jurnal Komputasi Vol. 14 No. 2 (2026): Jurnal Komputasi
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v14i2.368

Abstract

Kesehatan mental merupakan kondisi kesejahteraan psikologis yang memungkinkan individu mengelola stres, bekerja secara produktif, dan berinteraksi secara sosial. Peningkatan penggunaan media sosial menghasilkan data teks yang dapat merepresentasikan kondisi emosional dan psikologis pengguna. Berbagai penelitian telah mengembangkan klasifikasi kondisi kesehatan mental berbasis data media sosial menggunakan pendekatan machine learning dan deep learning. Namun, sebagian besar penelitian masih berfokus pada klasifikasi biner atau deteksi satu kondisi mental tertentu, sedangkan pendekatan multi-class untuk membedakan beberapa kondisi kesehatan mental secara bersamaan masih terbatas. Selain itu, kemiripan pola linguistik antar kondisi mental serta penggunaan bahasa informal pada media sosial menjadi tantangan dalam meningkatkan performa klasifikasi teks tidak terstruktur. Oleh karena itu, penelitian ini mengusulkan model Bidirectional Encoder Representations from Transformers (BERT) untuk mengklasifikasikan kondisi kesehatan mental berbasis data media sosial. Model BERT dipilih karena mampu memahami konteks bahasa secara bidirectional melalui mekanisme self-attention sehingga lebih efektif menangkap representasi semantik dibandingkan metode konvensional. Dataset yang digunakan adalah Reddit Mental Health dengan lima kategori, yaitu Stress, Depression, Bipolar Disorder, Personality Disorder, dan Anxiety. Tahapan penelitian meliputi preprocessing teks, tokenisasi menggunakan BERT tokenizer, pembagian data secara stratified, penanganan imbalanced data menggunakan class weighting dan focal loss, serta fine-tuning model. Hasil eksperimen pada 5.957 data menunjukkan bahwa konfigurasi hyperparameter terbaik memperoleh accuracy sebesar 80%, dengan rata-rata precision, recall, dan F1-score masing-masing sebesar 80%. Kategori Stress memperoleh F1-score tertinggi sebesar 87%, sedangkan kategori Depression sebesar 73%. Hasil penelitian menunjukkan bahwa model BERT efektif dalam memahami pola linguistik dan konteks emosional pada data media sosial tidak terstruktur.
Co-Authors Abd. Rasyid Syamsuri Abdul Djalil Djayali Abdul Fadlil Abdul Fadlil Abdul Fadlil Abdul Fadlil Abdul Fadlil Abdul Fadlil Abdul Hadi Achmad Dito Ahmad Azhar Kadim Ahmad Ikrom Ahmad Raditya Cahya Baswara Ahmad Syahril Mohd Nawi Aldi Bastiatul Fawait Fawait Alfian Ma’arif Alwas Muis Anggit Pamungkas Anton Yudhana Anton Yudhana Anton Yudhana Anton Yudhana Apik Rusdiarna Indra Praja Ardi Pujiyanta Ardiansyah Ardiningtias, Syifa Riski Ardiningtias Arief Setyo Nugroho Ariful Aziz Aris Rakhmadi Arizona Firdonsyah Asep Setyaji Aulia, Aulia Azrul Mahfurdz Azrul Mahfurdz Azrul Mahfurdz Azrul Mahfurdz Bambang Priambodo Bambang Subana Budi Santosa Deco Aprilliansyah Denis Prayogi Denis Prayogi Dewi Sahara Dewi Sahara Nasution Dewi Soyusiawaty Dewi Soyusiawaty Dian Novianti Doddy Teguh Yuwono Dwi Aryanto Dwi Aryanto Eko Aribowo Eko Handoyo Elisa, Erlin Ermin Ermin Evrynda Widyasari Puspa Dewi Faqihuddin Al-anshori Fatma Nuraisyah, Fatma Fiftin Noviyanto Fijaya Dwi Bima Sakti Putra Fijaya Dwi Bima Sakti Putra Fijaya Dwi Bimasakti Firdonsyah, Arizona Firmansyah Firmansyah Firmansyah Firmansyah Fitriah Fitriyani Tella Fitriyanto, Rachmad Furizal Furizal Furizal Furizal Gema Kharismajati Guguh Makbul Rahmadani Fitra H. Ahmad Hartanta, Agus Jaka Sri Hartini, Sri Haryani Alamsyah Herman Herman Herman Herman Herman Herman Herman Herman Herman Yuliansyah Hermawan Hermawan Hernawan Aji Nugroho Hikmatyar Insani Himawan I Azmi Ibnu Muakhori Ihyak Ulumuddin Iif Alfiatul Mukaromah Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Irhash Ainur Rafiq Irwansyah Irwansyah Irwansyah Irwansyah Jafri Din Janu Prasetyo januari audrey January Audrey Joko Supriyanto Joko Triyanto Kemal Thoriq Al-Azis Khoir, Syaiful Amrial Krisna Astianingrum Lina Handayani Luh Putu Ratna Sundari Lukman Reza Lukman Reza M Murinto M. Ihya A. Elfatih Mardhiatul Ihsaniah Miftahuddin Fahmi Mirza Sutrisno Mitra Adhimukti Mohd Azriq Abd Aziz Muchamad Kurniawan Muchlas, Muchlas Muchrisal Muchrisal Muchrisal Muflih, Ghufron Zaida Muh. Hajar Akbar Muhammad Amirul Mu'min Muhammad Fauzan Gustafi Muhammad Fauzan Gustafi Muhammad Kunta Biddinika Muhammad Kunta Biddinika Muhammad Muizz Mohd Nawawi Muhammad Nashiruddin Darajat Muhammad Nur Ardhiansyah Muhammad Sabiq Dzakwan Muhammad Sabiq Dzakwan Muntiari, Novita Ranti Murinto Murinto Musri Iskandar Nasution Muzakkir Pangri Nasirudin Nasirudin Nazuki Nazuki Nia Ekawati Nia Ekawati Nugroho, Hernawan Aji Nur Makkie Perdana Kusuma Nur Ratnawati Nuril Mustofa Nurul Azwanti Pahlevi, Ryan Fitrian Panggah Widiandana Pastima Simanjuntak Pradana Ananda Raharja Priyatno Priyatno Puji Ristianto Puriyanto, Riky Dwi Rachmad Fitriyanto Rachmad Very Ananda Saputra Rahmat Dwi Cahyono Raja Bidin Raja Hassan Rajunaidi Rajunaidi Rani Rotul Muhima Restu Prima Yudha Restu Prima Yudha Rezki Ramdhani Ricky Irawan Putra Rifkan Firdaus Rio Dwi Listianto Rio Ikhsan Alfian Rosmini Rosmini Rudy Ansari Rudy Ansari Rusydi Umar Rusydi Umar Rusydi Umar Rusydi Umar Saberi Mawi Sahiruddin Sahiruddin Saifullah, Shoffan Salsabilla Azahra Putri Saputro, Mochammad Yulianto Andi Sarmini Sarmini Septiyawan Rosetya Wardhana Sharipah Salwa Mohamed Son Ali Akbar Sri Rahayu Astari Sri Rahayu Astari Sri Winiarti Sri Winiarti Subrata, Arsyad Cahya Sukma Aji Supriyanto Suyahman Suyahman Syaiful Khoir Syed Abdullah Syed Abdullah Syifa Riski Ardiningtias Syifa'ah Setya Mawarni Syifa’ah Setya Mawarni Syifa’ah Setya Mawarni Tole Sutikno Tomy Chandra Mahendra Tresna Yudha Prawira Tri Antoro Tristanti, Novi Ulil Amri Ulil Ummi Syafiqoh Virasanty Muslimah Wahyu S Aji Watra Arsadiando Wawan Darmawan Wijaya, Setiawan Ardi Wilda Rina Hasibuan Yana Mulyana Yuniarti Lestari Yuwono Fitri Widodo