Article Per Year (5 Year)
p-Index From 2020 - 2025
8.581
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saragih, Triando Hamonangan
Universitas Lambung Mangkurat
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Health Professions Industrial & Manufacturing Engineering 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

Published : 67 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 67 Documents
Search

 3   4   5   6   7 
1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network Faisal, Mohammad Reza; Adawiyah, Laila; Saragih, Triando Hamonangan; kartini, Dwi; Herteno, Rudy; Lumbanraja, Favorisen Rosyking; Handayani, Lilies; Solechah, Siti Aisyah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.458

Abstract

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.
The Comparison of Extreme Machine Learning and Hidden Markov Model Algorithm in Predicting The Recurrence Of Differentiated Thyroid Cancer Using SMOTE Aida, Nor; Saragih, Triando Hamonangan; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.467

Abstract

Differentiated thyroid cancer is the most common type of thyroid cancer; the types in this category are papillary, follicular, and hurthel cell carcinoma. Up to 20% of DTCs will experience recurrence, although this figure reduces to 5% in low-risk patients. There is still little research on thyroid cancer prediction using a machine learning approach, especially the prediction recurrence of DTCs. This research aims to compare the performance of the Extreme Learning Machine and the Hidden Markov Model using SMOTE in predicting the recurrence of DTCs. The dataset used in this research is differentiated thyroid cancer recurrence from Kaggle. This research methodology comprises preprocessing, data sharing, SMOTE, ELM and HMM modeling algorithms, and evaluation. ELM with SMOTE gets the best results at a ratio of 90:10 with 35 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. ELM modeling gets the best results at a ratio of 90:10 with 45 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. HMM modeling gets the best value at a ratio of 70:30 with two hidden states and two iterations, which get an accuracy value of 0.8696, precision 0.8696, recall 0.7944, and AUC 0.9575. Last, HMM modeling with SMOTE gets the best results at a ratio of 60:40 with two hidden states and two iterations, with an accuracy value of 0.8696, precision of 0.8832, recall of 0.7848, and AUC of 0.9174. Based on the results of this study, it can be concluded that ELM with SMOTE gets the best performance, followed by ELM without SMOTE, HMM without SMOTE, and finally, HMM with SMOTE. The implication is that ELM with SMOTE can produce high accuracy in predicting the recurrence of DTCs.
A Classification of Appendicitis Disease in Children Using SVM with KNN Imputation and SMOTE Approach Difa Fitria; Triando Hamonangan Saragih; Muliadi; Dwi Kartini; Fatma Indriani
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.470

Abstract

This study evaluates the effect of SMOTE and KNN imputation techniques on the performance of SVM classification models on a nearly balanced dataset. The results show that using SMOTE increases model precision but decreases recall. This shows the importance of careful consideration when choosing data processing strategies to achieve optimal classification model performance. This study evaluates the effect of the Synthetic Minority Over-sampling Technique (SMOTE) and K-Nearest Neighbors (KNN) imputation on the performance of Support Vector Machine (SVM) classification models on nearly balanced datasets. The results of this study noted that the use of SMOTE techniques in balancing the dataset led to a decrease in classification model accuracy from 87.26% to 85.99%. However, there was a slight increase in AUC-ROC, from 85.96% to 88.04%. The results of this study noted that the use of the SMOTE technique in balancing the dataset caused a decrease in the accuracy of the classification model from 87.26% to 85.99%. However, there was an improvement in the AUC-ROC, from 85.96% to 88.04%.
Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease Muhammad Mursyidan Amini; Mazdadi, Muhammad Itqan; Muliadi, Muliadi; Faisal, Mohammad Reza; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.561

Abstract

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.
The Impactness of SMOTE as Imbalance Class Handling for Myocardial Infarction Complication Classification using Machine Learning Approach with Data Imputation and Hyperparameter Ahmad Tajali; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.13

Abstract

Myocardial Infarction (MI) is a critical medical emergency characterized by the sudden blockage of blood flow to the heart muscle, often resulting from a blood clot in a coronary artery that has been narrowed by atherosclerotic plaque buildup. This condition demands immediate attention, as prolonged disruption of blood supply can cause irreversible damage to the heart muscle. Diagnosing MI typically involves a combination of methods, including a physical examination, electrocardiogram (ECG) analysis, blood tests to measure heart-specific enzymes, and imaging techniques such as coronary angiography. Early prediction of potential MI complications is crucial to prevent severe outcomes and improve patient prognosis. This study focuses on the early prediction of MI complications through the application of machine learning classification methods. We employed algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost to analyze patient medical records and accurately predict these complications. The selection of Support Vector Machine (SVM), Random Forest, and XGBoost in this study is driven by their proven effectiveness in handling complex classification problems. To manage incomplete datasets and preserve valuable information, data imputation techniques like K-Nearest Neighbors (KNN) Imputation, Iterative Imputation, and MissForest were applied.  KNN, Iterative, and MissForest imputations were chosen to handle missing data due to their effectiveness in preserving data integrity, which is crucial for accurate predictions in myocardial infarction complication studies. Additionally, Bayesian Optimization was utilized to fine-tune the hyperparameters of the models, thereby enhancing their predictive accuracy. The Iterative Imputation method yielded the best performance, particularly in SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and Area Under the Curve (AUC), while XGBoost attained 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. While XGBoost and MissForest proved to be the most successful pairing, the overall effectiveness of the models suggests that Iterative Imputation and Random Forest also have potential under certain conditions.
Classification of brain tumor based on shape and texture features and machine learning Rizki, M. Alfi; Faisal, Mohammad Reza; Farmadi, Andi; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Bachtiar, Adam Mukharil; Keswani, Ryan Rhiveldi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/27236g49

Abstract

Information from brain tumour visualisation using MRI can be used for brain tumour classification. The information can be extracted using different feature extraction techniques. This study compares shape-based feature extraction such as Zernike Moment (ZM), and Pyramid Histogram of Oriented Gradients (PHOG) with texture-based feature extraction such as Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG) in brain tumour classification. This research aims to find out which feature extraction is better for handling brain tumour images through the accuracy and f1-score produced. This research proposes to combine each feature based on its approach, i.e. ZM+PHOG for shape-based feature extraction and LBP+GLCM+HOG for texture-based feature extraction with default parameters from the library and modified parameters configured based on previous research. The dataset used comes from Kaggle and has three classes: meningioma, glioma, and pituitary. The machine learning classification models used are Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbours (KNN) with default parameters from the library. The models were evaluated using 10-fold stratified cross-validation. This research resulted in an accuracy and f1-score of 84% for texture-based feature extraction with modified parameters in RF classification. In comparison, shape-based feature extraction resulted in accuracy and f1-score of 70% and 68% with modified parameters in RF classification. From the results, it can be concluded that texture-based feature extraction is better in handling brain tumour images compared to shape-based feature extraction. This study suggests that focusing on texture details in feature extraction can significantly improve classification performance in medical imaging such as brain tumours
Analisis Perbandingan Metode Harmonic Mean dan Local Mean Vector Dalam Penyeleksian Tetangga Pada Algoritma KNN Said, Muhammad Al Ichsan Nur Rizqi; Faisal, Mohammad Reza; Kartini, Dwi; Budiman, Irwan; Saragih, Triando Hamonangan
Jurnal Sains dan Informatika Vol. 9 No. 2 (2023): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v9i2.376

Abstract

Algoritma K Nearest Neighbour (KNN) merupakan salah satu algoritma klasifikasi yang telah digunakan pada banyak penelitian, namun KNN memiliki beberapa kekurangan diantaranya adalah pada pemilihan jumlah tetangga terdekat. Jika jumlah tetangga terdekat terlalu kecil maka akan sensitif terhadap derau (noise) dan jika jumlah tetangga terdekat terlalu besar kemungkinan ada tetangga outlier dari kelas lain. Majority Voting juga merupakan metode yang sederhana dan ini bisa jadi masalah jika jarak bervariasi. Salah satu solusi untuk masalah outlier adalah menggunakan Local Mean Vector dengan menambahkan Harmonic Mean untuk membantunya. Penelitian ini bertujuan untuk mengetahui perbandingan kinerja teknik penyeleksian tetangga terakhir yang didapatkan menggunakan Local Mean Vector dan Harmonic Mean. Dari Hasil dari penelitian ini menunjukkan bahwa teknik penyeleksian tetanggal berbasis Local Mean Vector dan Harmonic Mean memberikan akurasi lebih baik yaitu sebesar 0,78 dibandingkan dengan teknik Majority Voting dengan akurasi sebesar 0.75.
The Enhancing Diabetes Prediction Accuracy Using Random Forest and XGBoost with PSO and GA-Based Feature Selection Dzira Naufia Jawza; Mazdadi, Muhammad Itqan; Farmadi, Andi; Saragih, Triando Hamonangan; Kartini, Dwi; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.626

Abstract

Diabetes represents a global health concern classified as a non-communicable disease, impacting more than 422 million people worldwide, with the number expected to increase each year. This study aims to evaluate the performance of the Random Forest and Extreme Gradient Boosting (XGBoost) classification algorithms on the diabetes disease dataset taken from Kaggle. To improve prediction accuracy, feature selection was carried out using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which are expected to filter the most relevant features. The study results showed that the Random Forest model without feature selection yielded an Area Under Curve (AUC) value of 0.8120, while XGBoost achieved an AUC of 0.7666. After applying feature selection with PSO, the AUC increased to 0.8582 for Random Forest and 0.8250 for XGBoost. The use of feature selection with GA gave better results, with an AUC of 0.8612 for Random Forest and 0.8351 for XGBoost. These results indicate that the increase in accuracy after feature selection using PSO ranges from 5.7% to 7.6%, while the increase with GA ranges from 6.1% to 8.9%, with GA providing more significant results. This study contributes to improving the accuracy of diabetes disease classification, which is expected to support the diagnosis process more quickly and accurately.
Performance Comparison of Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (H-ELM) Methods for Heart Failure Classification on Clinical Health Datasets Ichwan Dwi Nugraha; Triando Hamonangan Saragih; Irwan Budiman; Dwi Kartini; Fatma Indriani; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.904

Abstract

Heart failure is one of the leading causes of death worldwide and requires accurate and timely diagnosis to improve patient outcomes. However, early detection remains a significant challenge due to the complexity of clinical data, high dimensionality of features, and variability in patient conditions. Traditional clinical methods often fall short in identifying subtle patterns that indicate early stages of heart failure, motivating the need for intelligent computational techniques to support diagnostic decisions. This study aims to enhance predictive modeling for heart failure classification by comparing two supervised machine learning approaches: Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (HELM). The main contribution of this research is the empirical evaluation of HELM's performance improvements over conventional ELM using 10-fold cross-validation on a publicly available clinical dataset. Unlike traditional neural networks, ELM offers fast training by randomly assigning weights and analytically computing output connections, while HELM extends this with a multi-layer structure that allows for more complex feature representation and improved generalization. Both models were assessed based on classification accuracy and Area Under the Curve (AUC), two critical metrics in medical classification tasks. The ELM model achieved an accuracy of 73.95% ± 8.07 and an AUC of 0.7614 ± 0.093, whereas the HELM model obtained a comparable accuracy of 73.55% ± 7.85 but with a higher AUC of 0.7776 ± 0.085. In several validation folds, HELM outperformed ELM, notably reaching 90% accuracy and 0.9250 AUC in specific cases. In conclusion, HELM demonstrates improved robustness and discriminatory capability in identifying heart failure cases. These findings suggest that HELM is a promising candidate for implementation in clinical decision support systems. Future research may incorporate feature selection, hyperparameter optimization, and evaluation across multi-center datasets to improve generalizability and real-world applicability.
Evaluation of the Impact of SMOTEENN on Monkeypox Case Classification Performance Using Boosting Algorithms Siena, Laifansan; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Kartini, Dwi; Muliadi; Caesarendra, Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.77

Abstract

Monkeypox is a zoonotic disease with increasing global prevalence, posing a significant challenge in healthcare. Its widespread transmission necessitates more accurate detection systems to assist medical professionals in diagnosing and managing cases effectively. One of the main challenges in developing monkeypox prediction models is class imbalance in datasets, which can cause models to favor the majority class and reduce predictive accuracy for rarer cases. To address this issue, this study evaluates the effectiveness of the SMOTEENN resampling technique in improving the classification performance of monkeypox cases. Three boosting algorithms Gradient Boosting, XGBoost, and LightGBM were applied to a monkeypox dataset consisting of 25,000 samples. The data preprocessing steps included handling missing values, feature encoding, and feature scaling. The dataset was then balanced using SMOTEENN, a hybrid technique combining the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Additionally, hyperparameter tuning with GridSearchCV was performed to optimize model performance by systematically selecting the best parameter combinations. The results indicate that applying SMOTEENN significantly improved classification accuracy, achieving a maximum of 69%, with an F1-score of 67%. Compared to previous studies, the proposed approach demonstrated superior performance in handling class imbalance and enhancing classification robustness. These findings highlight the potential of SMOTEENN and boosting algorithms in medical data classification, particularly for infectious diseases with imbalanced datasets. This study contributes to the development of more reliable machine learning techniques for improving disease detection, classification accuracy, and overall model generalization. Future research should explore additional resampling techniques, deep learning architectures, and feature selection methods to further improve predictive performance in medical diagnostics.
Co-Authors AA Sudharmawan, AA Abadi, Friska Abdul Latief Abadi Abdullayev, Vugar Achmad Rizal Adawiyah, Laila Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Aida, Nor Ajwa Helisa Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Alfita Rakhmandasari Amelia Aditya Santika Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Athavale, Vijay Anant Athavale, Vijay Annant Bachtiar, Adam Mukharil Bachtiar, Adam Mukharil Difa Fitria Dina Arifah Diny Melsye Nurul Fajri Diny Melsye Nurul Fajri Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Erlianita, Noor Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Febrian, Muhamad Michael Friska Abadi Haekal, Muhammad Haekal, Muhammad Hafizah, Rini Hermiati, Arya Syifa Herteno, Rudy Huynh, Phuoc-Hai Ichwan Dwi Nugraha Indriani, Fatma Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Jumadi Mabe Parenreng Keswani, Ryan Rhiveldi Lilies Handayani M. Khairul Rezki Mafazy, Muhammad Meftah Mariana Dewi Muhamad Fawwaz Akbar Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Darmadi Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Ikhwan Rizki Muhammad Itqan Mazdadi Muhammad Mursyidan Amini Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Noryasminda Nugraha, Muhammad Amir Nurcahyati, Ica Nurlatifah Amini Okta Muthia Sari Purwoko, Agus Putra, Aditya Maulana Perdana Radityo Adi Nugroho Rahmat Ramadhani Rahmat Ramadhani Rahmatullah, Satrio Wibowo Ramadhani, Rahmat Ratna Septia Devi Regina Reza Faisal, Mohammad Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsha Farahdiba Saputro, Setyo Wahyu Siena, Laifansan Siti Aisyah Solechah Siti Napi'ah Suci Permata Sari Sulastri Norindah Sari Tajali, Ahmad Totok Wianto Vivi Nur Wijayaningrum Wahyu Caesarendra Wayan Firdaus Mahmudy Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yusuf Priyo Anggodo Zamzam, Yra Fatria