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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Inspiratif Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control UICELL Conference Proceeding Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA Hearty : Jurnal Kesehatan Masyarakat Jurnal Biomedika dan Kesehatan Psikologi Konseling: Jurnal Kajian Psikologi dan Konseling Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Health Information : Jurnal Penelitian Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO MAHESA : Malahayati Health Student Journal Fitrah: Journal of Islamic Education Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Kolaboratif Sains Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Jurnal INFOTEL Jurnal Pengabdian Kepada Masyarakat Itekes Bali JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Jurnal Kesehatan Masyarakat Perkotaan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Holistik Jurnal Kesehatan
Fatma Indriani
Program Studi Ilmu Komputer, Fakultas Matematika Dan Pengetahuan Alam, Universitas Lambung Mangkurat, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Comparison of the Adaboost Method and the Extreme Learning Machine Method in Predicting Heart Failure Muhammad Nadim Mubaarok; Triando Hamonangan Saragih; Muliadi; Fatma Indriani; Andi Farmadi; Rizal, Achmad
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.v6i3.440

Abstract

Heart disease, which is classified as a non-communicable disease, is the main cause of death every year. The involvement of experts is considered very necessary in the process of diagnosing heart disease, considering its complex nature and potential severity. Machine Learning Algorithms have emerged as powerful tools capable of effectively predicting and detecting heart diseases, thereby reducing the challenges associated with their diagnosis. Notable examples of such algorithms include Extreme Learning Machine Algorithms and Adaptive Boosting, both of which represent Machine Learning techniques adapted for classification purposes. This research tries to introduce a new approach that relies on the use of one parameter. Through careful optimization of algorithm parameters, there is a marked improvement in the accuracy of machine learning predictions, a phenomenon that underscores the importance of parameter tuning in this domain. In this research, the Heart Failure dataset serves as the focal point, with the aim of demonstrating the optimal level of accuracy that can be achieved through the use of Machine Learning algorithms. The results of this study show an average accuracy of 0.83 for the Extreme Learning Machine Algorithm and 0.87 for Adaptive Boosting, the standard deviation for both methods is “0.83±0.02” for Extreme Machine Learning Algorithm and “0.87±0.03” for Adaptive Boosting thus highlighting the efficacy of these algorithms in the context of heart disease prediction. In particular, entering the Learning Rate parameter into Adaboost provides better results when compared with the previous algorithm. Our research findings underline the supremacy of Extreme Learning Machine Algorithms and Adaptive Improvement, especially when combined with the introduction of a single parameter, it can be seen that the addition of parameters results in increased accuracy performance when compared to previous research using standard methods alone.
A Comparative Study: Application of Principal Component Analysis and Recursive Feature Elimination in Machine Learning for Stroke Prediction Hermiati, Arya Syifa; Herteno, Rudy; Indriani, Fatma; Saragih, Triando Hamonangan; Muliadi; Triwiyanto, Triwiyanto
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.v6i3.446

Abstract

Stroke is a disease that occurs in the brain and can cause both vocal and global brain dysfunction. Stroke research mainly aims to predict risk and mortality. Machine learning can be used to diagnose and predict diseases in the healthcare field, especially in stroke prediction. However, collecting medical record data to predict a disease usually makes much noise because not all variables are important and relevant to the prediction process. In this case, dimensionality reduction is essential to remove noisy (i.e., irrelevant) and redundant features. This study aims to predict stroke using Recursive Feature Elimination as feature selection, Principal Component Analysis as feature extraction, and a combination of Recursive Feature Elimination and Principal Component Analysis. The dataset used in this research is stroke prediction from Kaggle. The research methodology consists of pre-processing, SMOTE, 10-fold Cross-Validation, feature selection, feature extraction, and machine learning, which includes SVM, Random Forest, Naive Bayes, and Linear Discriminant Analysis. From the results obtained, the SVM and Random Forest get the highest accuracy value of 0.8775 and 0.9511 without using PCA and RFE, Naive Bayes gets the highest value of 0.7685 when going through PCA with selection of 20 features followed by RFE feature selection with selection of 5 features, and LDA gets the highest accuracy with 20 features from feature selection and continued feature extraction with a value of 0. 7963. It can be concluded in this study that SVM and Random Forest get the highest accuracy value without PCA and RFE techniques, while Naive Bayes and LDA show better performance using a combination of PCA and RFE techniques. The implication of this research is to know the effect of RFE and PCA on machine learning to improve stroke prediction.
Analysis of Important Features in Software Defect Prediction Using Synthetic Minority Oversampling Techniques (SMOTE), Recursive Feature Elimination (RFE) and Random Forest Ghinaya, Helma; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi; Indriani, Fatma
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.v6i3.453

Abstract

Software Defect Prediction (SDP) is essential for improving software quality during testing. As software systems grow more complex, accurately predicting defects becomes increasingly challenging. One of the challenges faced is dealing with imbalanced class distributions, where the number of defective instances is significantly lower than non-defective ones. To tackle the imbalanced class issue, use the SMOTE technique. Random Forest as a classification algorithm is due to its ability to handle non-linear data, its resistance to overfitting, and its ability to provide information about the importance of features in classification. This research aims to evaluate important features and measure accuracy in SDP using the SMOTE+RFE+Random Forest technique. The dataset used in this study is NASA MDP D", which included 12 data sets. The method used combines SMOTE, RFE, and random forest techniques. This study is conducted in two stages of approach. The first stage uses the RFE+Random Forest technique; the second stage involves adding the SMOTE technique before RFE and Random Forest to measure the accurate data from NASA MDP. The result of this study is that the use of the SMOTE technique enhances accuracy across most datasets, with the best performance achieved on the MC1 dataset with an accuracy of 0.9998. Feature importance analysis identifies "maintenance severity" and "cyclomatic density" as the most crucial features in data modeling for SDP. Therefore, the SMOTE+RFE+RF technique effectively improves prediction accuracy across various datasets and successfully addresses class imbalance issues.
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%.
IDENTIFICATION OF K3 HAZARD RISK IN FIREFIGHTERS AT MEDAN CITY FIRE AND RESCUE SERVICE Indriani, Fatma; Gustara, Rizki Asih; Astuti, Yeni Ayu; Arianti, Tiara; Wulandari, Suci; Octavia, Mayang Dwi; Harahap, Helma Denisah; Amini, Aisah; Berutu, Marwiyah; Wati, Desi Indriani Rahma
HEARTY Vol 12 No 2 (2024): APRIL
Publisher : Fakultas Ilmu Kesehatan, Universitas Ibn Khaldun, Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/hearty.v12i2.16250

Abstract

Firefighters are particularly vulnerable to several work-related illnesses and injuries, including toxic gas poisoning, burns, respiratory distress, and bodily injury. Therefore, an important first step in implementing successful prevention tactics is to identify possible K3 hazards precisely and accurately. To prevent industrial accidents that endanger workers due to hazards arising in the Medan City Fire and Rescue Department, the purpose of this study is to identify hazards and evaluate risks using the HIRA method analysis. The research was conducted using qualitative methodology. Three data collection methods were used: documentation, in-depth interviews, and observation. The informants in this study amounted to 4 people consisting of 2 key informants and 2 main informants. Based on the results of hazard identification and risk assessment, there are 19 accident risks spread across 5 activities in the Medan City Fire and Rescue Service. Based on the results of the study, it was concluded that 10 potential hazards fall into the meaningful category, 4 potential hazards fall into the medium-risk category, 4 potential hazards fall into the low-risk category, and high-risk categories as many as 1 potential hazard. Based on these conclusions, suggestions were submitted to the Medan City Fire and Rescue Service to increase the provision of personal protective equipment (PPE) such as gloves, helmets, heat-resistant shirts, and pants, and SCBA was an anticipation of the proposal submitted to the Medan City Fire and Rescue Service. Medan City Fire and Rescue Officers are also required to always comply with established work rules to reduce risks and avoid work accidents.
Applying XGBoost-ADASYN in the Classification Process of Bank Customers Who Will Take Time Deposits Abdilah, Muhammad Fariz Fata; Mazdadi, Muhammad Itqan; Farmadi, Andi; Muliadi, Muliadi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.551

Abstract

Investment in the form of time deposits at banks offers stable returns. Identifying and attracting potential customers, however, poses challenges. This research enhances the predictive capabilities of deposit classification models by addressing data imbalance with a combination of XGBoost, ADASYN, and Random Search optimization techniques. The integration of ADASYN improves minority class representation, while Random Search efficiently optimizes model parameters. Our findings show a significant accuracy of 94.93%, benchmarked against baseline models, highlighting our method's effectiveness compared to traditional approaches. This hybrid model advances customer data analysis and achieves our research objectives. We discuss the integration challenges, including computational demands and technique selection. The research underscores the application of machine learning to address financial industry issues, emphasizing the impact of data preprocessing and feature engineering on performance. Future studies might explore AutoML to reduce complexity further and enhance model scalability, promising more innovation in customer data analysis.
The Factors Influencing Early Marriage among Women Triyoolanda, Anggun; Indriani, Fatma; Nurhayati
Journal La Medihealtico Vol. 4 No. 6 (2023): Journal La Medihealtico
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamedihealtico.v4i6.1939

Abstract

A marriage that is carried out prematurely or at a too young age is something that is contrary to the law on marriage. Objective: This research is to determine the relationship between factors that influence young marriage in young couples. Method: The study used a descriptive analytical design with a cross-sectional approach. A total of 97 couples in AFD 2 Bahbutong, Sidamanik District, Simalungun Regency, were selected as respondents through a simple random sampling technique. The dependent variable was early marriage, while the independent variables included self-will, family/parental support, education, and economic factors. Data were collected through questionnaires and analyzed using Pearson correlation. Results: The study showed that self-will had a strong positive relationship with early marriage (r=0.605, p<0.001). Family support had a moderate positive relationship (r=0.417, p<0.001), while education factors had a strong negative relationship (r=-0.650, p<0.001). Economic factors also had a moderate negative relationship with early marriage (r=-0.453, p<0.001). Conclusion: All independent variables showed a significant relationship with early marriage. Therefore, interventions to reduce early marriage need to be focused on increasing access to education, economic empowerment, and family awareness of the negative impacts of early marriage.Key words: Young marriage, own will, economic factors, family factors.
Hubungan antara usia dan masa kerja dengan kelelahan kerja pada sopir pengemudi travel Wati, Desi Indriani Rahma; Ashar, Yulia Khairina; Indriani, Fatma
Holistik Jurnal Kesehatan Vol. 19 No. 1 (2025): Volume 19 Nomor 1
Publisher : Program Studi Ilmu Keperawatan-fakultas Ilmu Kesehatan Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/hjk.v19i1.795

Abstract

Background: Work fatigue caused by work period and age factors is one of the occupational health and safety problems that can be a risk of accidents while driving. Purpose: To determine the relationship between length of service and age and work fatigue in travel drivers. Method: Quantitative research with a cross-sectional design approach, involving 33 Commanditaire Vennootschap Raffi Expres Travel Kutacane drivers, was conducted in July-August 2024. Results: The average age of respondents is 30 years with a standard deviation value of 5.0 in the age range of 25-35 years with a p-value of 0.009 (<0.05), so there is a relationship between age and work fatigue. In addition, a PR value of 7.368 was obtained, indicating that respondents aged ≥ 30 years have a 7.368 times greater risk of experiencing high levels of work fatigue compared to respondents aged > 30 years. There are 21 respondents (63.6%) who have a work period containing co (≥ 5 years) with a p-value of 0.027 and an OR value of 5.714. This indicates that a work period of ≥ 5 years is at risk of experiencing high work fatigue compared to respondents who have a work period of < 5 years. Conclusion: There is a significant relationship between length of service and age with work fatigue in travel drivers (p-value <0.05).   Keywords: Age; Driver; Job Fatigue; Length of Service.   Pendahuluan: Kelelahan kerja yang disebabkan oleh masa kerja dan faktor usia adalah salah satu permasalahan kesehatan dan keselamatan kerja yang dapat menjadi risiko terjadi kecelakaan pada saat bekerja mengemudi. Tujuan: Untuk mengetahui hubungan masa kerja dan usia terhadap kelelahan kerja pada sopir pengemudi travel. Metode: Penelitian kuantitatif dengan pendekatan desain cross sectional, melibatkan 33 sopir pengemudi travel CV Raffi Expres Travel Kutacane, dilakukan pada bulan Juli-Agustus 2024. Hasil: Rata-rata usia responden adalah 30 tahun dengan nilai standar deviasi 5.0 pada rentang usia 25-35 tahun dengan p-value 0.009 (<0.05), maka ada hubungan antara usia dengan kelelahan kerja. Selain itu, diperoleh nilai PR sebesar 7.368, menunjukkan bahwa responden berusia ≥ 30 tahun mempunyai risiko 7.368 kali lebih besar untuk mengalami tingkat kelelahan kerja yang tinggi dibandingkan dengan responden berusia >30 tahun. Terdapat 21 responden (63.6%) yang memiliki masa kerja berisiko (≥ 5 tahun) dengan p-value 0.027 dan nilai OR sebesar 5.714. Hal ini menunjukkan bahwa masa kerja ≥ 5 tahun berisiko mengalami kelelahan kerja yang tinggi dibandingkan dengan responden yang memiliki masa kerja < 5 tahun. Simpulan: Terdapat hubungan yang signifikan antara masa kerja dan usia terhadap kelelahan kerja pada sopir pengemudi travel (p-value <0.05).   Kata Kunci: Kelelahan Kerja; Masa Kerja; Sopir Travel; Usia.
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.
Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Indriani, Fatma; Nugroho, Radityo Adi; Faisal, Mohammad Reza; Kartini, Dwi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6100

Abstract

Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments.
Co-Authors Abdilah, Muhammad Fariz Fata Abdul Azis Abdullayev, Vugar Achmad Rizal Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Azizah, Azkiya Nur Badali, Rahmat Amin Baharuddin Siregar, Baharuddin Baron Hidayat Barus, Nency Utami Br Berutu, Marwiyah Br Barus, Nency Utami br Damanik, Cici Rahayu Carolina, Ayu DALIMUNTHE, NADIYAH RAHMA Darmansyah, Rendi Dendy Fadhel Adhipratama Dendy Dewi Sri Wahyuni, Dewi Sri Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Effendi, Khairunnisa Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Fitriani, Karlina Elreine Friska Abadi Ghinaya, Helma Gustara, Rizki Asih Hafizah, Rini Harahap, Helma Denisah Hartati Hartati Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Irwan Budiman Irwan Budiman Khairiyah Dwie Vanesa Lilies Handayani Lubis, Masruroh M. Apriannur M. Khairul Rezki Mahmud Mahmud Mawandri, Dwi Mohammad Mahfuzh Shiddiq Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muliadi Muliadi Muliadi Aziz Nafiz, Muhammad Fauzan Nita Arianty Nofi Susanti Nurhayani nurhayani Nurhayati Octavia, Mayang Dwi Oni Soesanto P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putri Maimunah Radityo Adi Nugroho Rapotan Hasibuan Reni Agustina Harahap Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rozaq, Hasri Akbar Awal Rudy Herteno Salianto Salianto, Salianto Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sa’diah, Halimatus Selvia Indah Liany Abdie Siregar, Nurul Syahputri Soesanto, Oni Sri Rahayu Suci Wulandari Triyoolanda, Anggun Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya