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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal IMPACT: Implementation and Action Journal of Electronics, Electromedical Engineering, and Medical Informatics Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat (Mediteg) Jurnal Abdimas Madani dan Lestari (JAMALI) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Madaniya Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Dodon Turianto Nugrahadi
Lambung Mangkurat University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor Dwi Kartini; Andi Farmadi; Muliadi muliadi; Dodon Turianto Nugrahadi; Pirjatullah Pirjatullah
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2965

Abstract

Pneumonia adalah penyakit menular yang menyerang saluran pernapasan bagian bawah dan merupakan salah satu penyebab utama kematian pada anak-anak di bawah lima tahun. Pneumonia mudah menyerang balita yang disebabkan oleh berbagai mikroorganisme yang ada di lingkungan seperti virus, bakteri, jamur dan bakteri mikro. Penelitian ini menggunakan K-Nearest Neighbor (KNN) untuk klasifikasi pneumonia pada pasien berdasarkan gejala yang dialami. Metode klasifikasi KNN dilakukan dengan membandingkan jarak objek antara data tes dan objek keseluruhan pada data pelatihan berdasarkan data riwayat medis pasien. Perbandingan persentase data pelatihan dan data pengujian yang digunakan adalah 90:10, 80:20, dan 70:30 untuk menghitung nilai jarak terdekat dari data pengujian dengan data pelatihan keseluruhan dengan jumlah k yang digunakan. Matriks kebingungan digunakan untuk mengukur hasil tes klasifikasi Pneumonia untuk balita dengan kombinasi jumlah data pelatihan dan data pengujian pada jumlah nilai k = {1, 3, 5, 7, 9, 11}, akurasi tertinggi, presisi, penarikan, dan nilai ukuran-F diperoleh. 0,86, 0,89, 1, dan 0,91 untuk data pelatihan 90%, 10% data pengujian dengan nilai k = 3.
Deep Learning-Based Lung Sound Classification Using Mel-Spectrogram Features for Early Detection of Respiratory Diseases Midfai Yabani; Mohammad Reza Faisal; Fatma Indriani; Dodon Turianto Nugrahadi; Dwi Kartini; Kenji Satou
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1256

Abstract

Respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia remain among the leading causes of death globally. Traditional diagnostic approaches, including auscultation, rely heavily on the subjective expertise of medical practitioners and the quality of the instruments used. Recent advancements in artificial intelligence offer promising alternatives for automated lung sound analysis. However, audio is an unstructured data format that must be converted into a suitable format for AI algorithms. Another significant challenge lies in the imbalanced class distribution within available datasets, which can adversely affect classification performance and model reliability. This study applied several comprehensive preprocessing techniques, including random undersampling to address data imbalance, resampling audio at 4000 Hz for standardization, and standardizing audio duration to 2.7 seconds for consistency. Feature extraction was then performed using the Mel Spectrogram method, converting audio signals into image representations to serve as input for classification algorithms based on deep learning architectures. To determine optimal performance characteristics, various Convolutional Neural Network (CNN) architectures were systematically evaluated, including LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-50, and ResNet-152. VGG-16 achieved the highest classification accuracy of the tested models at 75.5%, demonstrating superior performance in respiratory sound classification tasks. This study demonstrates the potential of AI-based lung sound classification systems as a complementary diagnostic tool for healthcare professionals and the general public in supporting early identification of respiratory abnormalities and diseases. The findings suggest that automated lung sound analysis could enhance diagnostic accessibility and provide more valuable support for clinical decision-making in respiratory healthcare applications
Feature extraction and machine learning methods for biometric recognition based on fusion of ECG and fingerprint Hafiz Ilhami; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Irwan Budiman; Andi Farmadi; Dwi Kartini; Puput Dani Prasetyo Adi; Jumadi Mabe Parenreng
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10541

Abstract

This research introduces a multimodal biometric authentication framework by amalgamating electrocardiogram (ECG) and fingerprint modalities through the utilization of diverse feature extraction methodologies and machine learning classifiers. The proposed methodology aspires to augment precision and mitigate spoofing vulnerabilities in contrast to traditional single-modality systems. Among the feature extraction techniques assessed—grayscale, binary, Sobel edge detection, and minutiae—Naïve Bayes (NB) in conjunction with minutiae features exhibited superior performance, attaining an accuracy rate of 96.25%. Supplementary experiments employing random forest (RF) and support vector machine (SVM) also revealed commendable classification efficacy, underscoring the robustness of the fusion methodology. This investigation provides a pragmatic and secure biometric framework by harnessing complementary biometric characteristics to enhance authentication dependability. The proposed system presents promising applications in real-world contexts, particularly concerning mobile security and healthcare access control. Future research endeavors will tackle challenges associated with ECG signal variability, computational efficiency, and extensive deployment.
Evaluating CNN Robustness for Face Mask Classification under Environmental Variations Bagaskara Ridho Vandio; Fatma Indriani; Andi Farmadi; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2617

Abstract

Purpose – This study aims to analyze and compare the performance of ResNet50 and MobileNetV3 for multi-class face mask classification under various environmental conditions. Design/methods/approach – ResNet50 and MobileNetV3 are trained using transfer learning for three-class face mask classification and evaluated under normal conditions and environmental variations, including illumination changes, blur, low compression, and rotation. Findings – Experimental results show that ResNet50 achieves an accuracy of 94.32% under normal conditions, slightly outperforming MobileNetV3 at 94.10%. Under environmental variations, the largest performance degradation is observed under darkening and blur conditions, while low compression and rotation have relatively minor effects. ResNet50 demonstrates higher robustness across most perturbation settings, whereas MobileNetV3 provides competitive performance with substantially better computational efficiency. Research implications/limitations – This study is limited to a controlled evaluation using synthetic environmental perturbations on a single dataset and does not consider broader dataset diversity. Therefore, the findings should be interpreted within the evaluated experimental conditions. Originality/value – This study provides a comparative analysis of model robustness under controlled environmental perturbations, highlighting the trade-off between robustness and computational efficiency for face mask classification systems.
Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis Dodon Turianto Nugrahadi; Friska Abadi; Rudy Herteno; Muliadi Muliadi; Muhammad Alkaff; Muhammad Alvin Alfando
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p283-293

Abstract

Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Depression Level Classification Using Compact Cross-Domain Feature Engineering on Sleep, Physical Activity, and Demographic Data Nila Yoga Tama Nurwati; Fatma Indriani; Friska Abadi; Dodon Turianto Nugrahadi; Rudy Herteno
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.356

Abstract

Depression is a common mental health disorder and a major public health concern, and early identification of depressive symptoms using population survey data can support exploratory risk analysis. However, many previous studies formulated depression prediction as a binary classification task. They used broad predictor sets, while multiclass depression-level classification with compact and interpretable cross-domain features remains less explored. This study developed a compact cross-domain feature engineering approach for classifying depression levels using sleep, physical activity, and demographic data from NHANES 2017–2018. A total of 5,068 respondents were included after preprocessing and PHQ-9 label construction. The target variable was divided into three classes: no-to-minimal depression, mild depression, and depression. Twenty raw predictors were transformed into 15 engineered features representing sleep patterns, sleep-related problems, physical activity, sedentary behavior, and interactions with age and income. Logistic Regression with class_weight = balanced was evaluated using stratified 5-fold cross-validation and compared with several baseline classifiers. The Final 15 FE Only scenario achieved an accuracy of 0.6215 ± 0.0091, macro F1-score of 0.4501 ± 0.0104, balanced accuracy of 0.5146 ± 0.0179, and depression-class recall of 0.6122 ± 0.0622. Compared with Raw Features, depression-class recall increased from 0.5360 ± 0.0541 to 0.6122 ± 0.0622, although the improvement was not statistically significant. These findings indicate that compact cross-domain features can improve sensitivity toward the depression class in an interpretable Logistic Regression setting, but overall predictive gains remain modest. The proposed model is more suitable for exploratory and population-level screening support rather than a stand-alone clinical diagnosis
Evaluasi Usability Sistem Informasi Manajemen Kepegawaian Kalimantan Selatan Berdasarkan ISO 9241-11 Maulana, Syarif; Saputro, Setyo Wahyu; Abadi, Friska; Turianto Nugrahadi, Dodon; Reza Faisal, Mohammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Penelitian ini menyajikan evaluasi usability secara komprehensif terhadap Sistem Informasi Manajemen Kepegawaian (SIMPEG) Provinsi Kalimantan Selatan berdasarkan kerangka ISO 9241-11. Evaluasi dilakukan dengan mengintegrasikan Performance Measurement, System Usability Scale (SUS), dan Retrospective Think-Aloud (RTA) untuk menilai efektivitas, efisiensi, dan kepuasan pengguna. Sebanyak 25 partisipan menyelesaikan tujuh skenario tugas yang merepresentasikan fungsi utama sistem. Hasil pengujian menunjukkan tingkat Task Completion Rate sebesar 96% dan Overall Relative Efficiency sebesar 91%, yang mengindikasikan efektivitas dan efisiensi sistem yang tinggi. Skor rata-rata SUS sebesar 79,7 menempatkan sistem pada kategori “baik” dengan tingkat akseptabilitas “dapat diterima”. Analisis inferensial menggunakan uji korelasi Spearman menun-jukkan tidak terdapat hubungan signifikan antara efisiensi objektif dan kepuasan subjektif (r = 0,129; p = 0,538), yang mengindikasikan bahwa dimensi usability dapat bersifat independen dalam konteks sistem pemerintahan. Uji Friedman menunjukkan adanya perbedaan signifikan waktu penyelesaian antar skenario tugas (χ²(6) = 141,071; p < 0,001), yang mengidentifikasi adanya bottleneck usability pada modul tertentu. Temuan kualitatif dari RTA memperkuat hasil kuantitatif dengan mengungkap kendala pada aspek kemudahan belajar, konsistensi navigasi, dan integrasi fitur. Penelitian ini memberikan kontribusi melalui integrasi analisis deskriptif dan inferensial dalam evaluasi usability sistem pemerintahan sebagai baseline pengembangan sistem selanjutnya.   Abstract   This study presents a comprehensive usability evaluation of the Civil Service Management Information System (SIMPEG) of South Kalimantan Province, based on the ISO 9241-11 framework. The evaluation integrates Performance Measurement, the System Usability Scale (SUS), and the Retrospective Think-Aloud (RTA) protocol to assess the system's effectiveness, efficiency, and user satisfaction. A total of 25 participants completed seven task scenarios representing the system’s core functions. The testing results showed a Task Completion Rate of 96% and an Overall Relative Efficiency of 91%, indicating high levels of effectiveness and efficiency. The average SUS score of 79.7 places the system in the “Good” category with an “Acceptable” level of usability. Inferential analysis using Spearman’s correlation test revealed no significant relationship between objective efficiency and subjective satisfaction (r = 0.129; p = 0.538), suggesting that usability dimensions may be independent in the context of government systems. The Friedman test indicated a significant difference in task completion times across scenarios (χ²(6) = 141.071; p < 0.001), identifying specific modules as usability bottlenecks. Qualitative findings from the RTA supported the quantitative results by uncovering challenges related to learnability, navigation consistency, and feature integration. This study contributes by integrating descriptive and inferential analysis in the usability evaluation of government systems, serving as a baseline for future system development.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Kevin Yudhaprawira Halim; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Rudy Herteno; Irwan Budiman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26354

Abstract

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification. 
3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification Mohammad Reza Faisal; Dodon Turianto Nugrahadi; Irwan Budiman; Muliadi Muliadi; Mera Kartika Delimayanti; Septyan Eka Prastya; Imam Tahyudin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26091

Abstract

Social media contain various information, such as natural disaster reports. Artificial intelligence is used to identify reports from eyewitnesses early for disaster warning systems. The artificial intelligence system includes a text classification model with feature extraction and classification algorithms. Word embedding-based feature extraction is widely used for 1-dimensional (1D) and 2-dimensional (2D) data, suitable for traditional or deep learning algorithms. However, applying feature extraction to 3-dimensional (3D) data for text classification is limited. Previous studies focused on word embedding for 1D, 2D, and 3D outputs with convolutional neural network (CNN). Yet, using 3D data and CNN did not perform well. Despite using CNN and 3D variants, identifying natural disaster reports remains below 80% accuracy. This research aims to improve identifying earthquakes, floods, and forest fires with 3D data and hybrid CNN long short-term memory (LSTM). The study found models with accuracies of 83.38%, 83.72%, and 89.03% for each disaster type. Hybrid CNN LSTM significantly enhanced identification compared to CNN alone, supported by statistical tests with P value less than 0.0001.
Functional Evaluation of the Logia Dashboard Using Boundary Value Testing and Cause-Effect Graph Techniques Muhammad Rizky Aulia Ramadhan; Friska Abadi; Dodon Turianto Nugrahadi; Setyo Wahyu Saputro; Rudy Herteno
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3121

Abstract

The Logia Dashboard is a web-based information system used to manage rehabilitation plant data on post-mining land. As an alpha-stage system, Logia requires thorough functional and performance evaluation to ensure that all input validations, logical processes, and system responses operate correctly before wider implementation. This study aims to evaluate the functional reliability and performance of the Logia Dashboard by applying a combined approach of Boundary Value Testing (BVT) and Cause-Effect Graph (CEG) techniques, supported by performance testing using Google Lighthouse. The research design adopts a black-box testing approach. BVT is applied to validate input boundaries on critical features, including login, data editing, QR code generation, and account creation. Meanwhile, CEG is used to model logical relationships between input conditions and system outputs to generate systematic test cases. A total of 39 optimized functional test cases were executed in a controlled local environment. Performance testing was conducted using Lighthouse by measuring key metrics such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS). The functional testing results show that 37 out of 39 test cases passed, yielding a success rate of 94.87%. Two failed cases were identified in the login feature, indicating weaknesses in input validation feedback. Performance testing produced an average Lighthouse score of 97, demonstrating that the system has excellent load speed and interface stability, although minor layout instability was detected on certain pages. These results indicate that the combined application of BVT and CEG is effective for detecting boundary-related and logical input errors in alpha-stage web systems. The findings also provide concrete recommendations for improving login validation and interface stability, supporting further development of the Logia Dashboard toward a more reliable and robust system for post-mining land management.
Co-Authors Abdul Gafur Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Aji Triwerdaya Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Apriana, Susi Ardiansyah Sukma Wijaya Arfan Eko Fahrudin Arifin Hidayat Azwari, Ayu Riana Sari Azwari, Ayu RianaSari Bachtiar, Adam Mukharil Badali, Rahmat Amin Bagaskara Ridho Vandio Bahriddin Abapihi Bedy Purnama Cahyadi, Rinova Firman Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emy Iryanie, Emy Faisal Murtadho Fajrin Azwary Fatma Indriani Fatma Indriani Fatma Indriani Fhadilla Muhammad Fitra Ahya Mubarok Fitria Agustina fitria Fitriani, Karlina Elreine Fitrinadi Friska Abadi Friska Abadi Gunawan Gunawan Gunawan Gunawan Hafiz Ilhami Hariyady Hariyady Herteno, Rudy Heru Kartika Candra, Heru Kartika Huynh, Phuoc-Hai Ichsan Ridwan Imam Tahyudin Indah Ayu Septriyaningrum Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Ismail Didit Samudro Julius Tunggono Jumadi Mabe Parenreng Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kartika, Najla Putri Kenji Satou Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Khusnul Rahmi Maulidha Liling Triyasmono Luu Duc Ngo M Kevin Warendra M. Apriannur Martalisa, Asri Mera Kartika Delimayanti Mera Kartika Delimayanti Midfai Yabani Miftahul Muhaemen Mohammad Reza Faisal Mohammad Reza Faisal Moses Okechukwu Onyesolu Muhamad Ihsanul Qamil Muhammad Alkaff Muhammad Alkaff Muhammad Alvin Alfando Muhammad Anshari Muhammad Haekal Muhammad Hasan Muhammad Ikhwanul Hakim Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Rafi Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Aulia Ramadhan Muhammad Rofiq Muhammad Sholih Afif Muhammad Solih Afif Muliadi Muliadi Muliadi MULIADI -, MULIADI Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Musyaffa, Muhammad Hafizh Nafis Satul Khasanah Nahdhatuzzahra Nahdhatuzzahra Ngo, Luu Duc Nila Yoga Tama Nurwati Noor Hidayah Nursyifa Azizah Ori Minarto Padhilah, Muhammad Pirjatullah Pirjatullah Pirjatullah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Puput Dani Prasetyo Adi Puput Dani Prasetyo Adi Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani, Rahmat Reza Faisal, Mohammad Riadi, Putri Agustina Rifki Izdihar Oktvian Abas Pullah Rifki Riza Susanto Banner Rizal, Muhammad Nur Rizki Amelia Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Saman Abdurrahman Saragih, Triando Hamonangan Selvia Indah Liany Abdie Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro sholih Afif Siti Napi'ah Soesanto, Oni Sri Cahyo Wahyono Sri Rahayu Sri Redjeki Sri Redjeki Syarif Maulana, Syarif Totok Wianto Totok Wiyanto Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utomo, Edy Setyo Wahyu Dwi Styadi Wardana, Muhammad Difha Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yudha Sulistiyo Wibowo Zamzam, Yra Fatria