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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 JOURNAL OF SCIENCE AND SOCIAL RESEARCH 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 International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO Journal of Gender and Social Inclusion in Muslim Societies MAHESA : Malahayati Health Student Journal Fitrah: Journal of Islamic Education Jurnal Kolaboratif Sains Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Jurnal Pengabdian Kepada Masyarakat Itekes Bali JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Jurnal Ilmiah Kesehatan Mandala Waluya 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 Humanities Biochemistry, Genetics & Molecular Biology 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 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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Optimizing South Kalimantan Food Image Classification Through CNN Fine-Tuning Muhammad Ridha Maulidi; Fatma Indriani; Andi Farmadi; Irwan Budiman; Dwi Kartini
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30325

Abstract

South Kalimantan's rich culinary heritage encompasses numerous traditional dishes that remain unfamiliar to visitors and digital platforms. While Convolutional Neural Networks (CNNs) have demonstrated remarkable success in image classification tasks, their application to regional cuisine faces unique challenges, particularly when dealing with limited datasets and visually similar dishes. This study addresses these challenges by evaluating and optimizing two pre-trained CNN architectures—EfficientNetB0 and InceptionV3—for South Kalimantan food classification. Using a custom dataset of 1,000 images spanning 10 traditional dishes, we investigated various fine-tuning strategies to maximize classification accuracy. Our results show that EfficientNetB0, with 30 fine-tuned layers, achieves the highest accuracy at 94.50%, while InceptionV3 reaches 92.00% accuracy with 40 layers fine-tuned. These findings suggest that EfficientNetB0 is particularly effective for classifying regional foods with limited data, outperforming InceptionV3 in this context. This study provides a framework for efficiently applying CNN models to small, specialized datasets, contributing to both the digital preservation of South Kalimantan’s culinary heritage and advancements in regional food classification. This research also opens the way for further research that can be applied to other less documented regional cuisines. The framework presented can be used as a reference for developing automated classification systems in a broader cultural context, thus enriching the digital documentation of traditional cuisines and preserving the culinary diversity of the archipelago for future generations.
Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest Wardana, Muhammad Difha; Budiman, Irwan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4696

Abstract

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.
Cross-Temporal Generalization of IndoBERT for Indonesian Hoax News Classification Riadi, Agus Teguh; Indriani, Fatma; Mazdadi, Muhammad Itqan; Faisal, Mohammad Reza; Herteno, Rudi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4757

Abstract

The spread of hoaxes in digital media poses a major challenge for automated detection systems as language and topics evolve over time. Although Transformer-based models such as IndoBERT have demonstrated high accuracy in previous studies, their performance across different time periods remains underexplored. This study examines the cross-temporal generalization ability of IndoBERT for hoax news classification. The model was trained on labeled articles from 2018–2023 and tested on data from 2025 to evaluate its robustness against temporal distribution shifts. The results indicate high accuracy on similar-period data (99.67–99.89%) but a decrease on 2025 data (95.45–95.87%), with most errors occurring as false negatives in the hoax class. These findings highlight the impact of temporal distribution shifts on model reliability and underscore the importance of adaptive strategies such as periodic retraining and domain-based data augmentation. Practically, this model has the potential to assist social media platforms and government institutions in developing dynamic and time-adaptive hoax detection systems. The cross-temporal approach employed in this study also offers methodological innovation compared to conventional random validation, as it better reflects real-world conditions where misinformation patterns continually evolve.
Implementasi Principal Component Analysis (PCA) dan Gap Statistic untuk Clustering Kanker Payudara pada Algoritma K-Means Afifa, Ridha; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Indriani, Fatma; Muliadi, Muliadi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4015

Abstract

Breast cancer is one of the most common causes of death worldwide. Data mining can be utilized to detect breast cancer, where information is extracted from data to provide valuable insights. Clustering of breast cancer is conducted to assist medical professionals in grouping the characteristics of each cancer type. However, multicollinearity in breast cancer data can impact clustering results. To address this issue, dimensionality reduction through Principal Component Analysis (PCA) is employed. PCA can effectively handle multicollinearity issues and enhance computational efficiency. Additionally, the K-Means method has limitations in determining the optimal number of clusters. Therefore, the Gap Statistic method is employed to find the optimal K value suitable for breast cancer data. This study compares the evaluation results of the K-Means clustering model, the combined PCA-KMeans clustering model, and the combined PCA-GapStatistic-KMeans clustering model. The findings indicate that the evaluation results for the K-Means model with PCA dimensionality reduction and optimal Gap Statistic K are superior to the K-Means model without dimensionality reduction. The Gap Statistic suggests 2 clusters as the optimal number, with an evaluation result of 1.195513.
Analisis Hubungan Motivasi Kerja dan Stress Kerja Pada Perawat di Ruang Rawat Inap RS Setio Husodo Kisaran br Damanik, Cici Rahayu; Utami, Tri Niswati; Indriani, Fatma
MAHESA : Malahayati Health Student Journal Vol 4, No 8 (2024): Volume 4 Nomor 8 (2024)
Publisher : Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/mahesa.v4i8.15510

Abstract

ABSTRACT because nurses interact with patients around the clock, they make up the majority of healthcare professionals. Thus, motivation is what drives people to achieve. To determine the relationship between nurse work motivation and work stress in Setio Husodo hospital. By utilizing cross-sectional assessment design and quantitative assessment methods, this study examines work motivation and work stress using a modified questionnaire setia (2017) and work stress using a questionnaire derived from the thesis Nur Kholifatul Hidayati (2018). Likert scale method is used as a measure of work motivation and work stress. Setyo Husodo Kisaran hospital became the research location for this study. Total sampling is used in the sample. Up to 45 personnel from the inpatient department became a sample of the study. Data were analyzed using Spearman Rank Correlation in bivariate analysis and univariate analysis to determine the distribution of characteristics of respondents. Considering the correlation between work motivation and work stress in nurses at Setio Husodo hospital, the range is equal to -0.334 with a significance level of 0.025, it can be said that there is a relationship between the two. A comparison between a sample of 45 individuals with table values resulted in a table value of -0.334. Nurses at Setio Husodo Kisaran hospital had a substantial inverse relationship between work motivation and work stress. This implies that work-related stress decreases when work motivation increases and vice versa. Keywords: Job Stress, Work Motivation, Inpatient Nurse, Likert Scale, Hospital  ABSTRAK Karena perawat berinteraksi dengan pasien sepanjang waktu, mereka merupakan mayoritas profesional kesehatan. Dengan demikian, motivasi adalah apa yang mendorong orang untuk berprestasi. Tujuan untuk mengetahui hubungan antara motivasi kerja perawat dan stres kerja pada rangkaian rumah sakit Setio Husodo. Dengan memanfaatkan desain penilaian cross-sectional dan metode penilaian kuantitatif, penelitian ini mengkaji motivasi kerja dan stres kerja menggunakan kuisioner yang dimodifikasi Setia (2017) dan stres kerja menggunakan kuisioner yang berasal dari skripsi Nur Kholifatul Hidayati (2018). Metode skala likert digunakan sebagai alat ukur motivasi kerja dan stres kerja. Rumah Sakit Setio Husodo Kisaran menjadi lokasi penelitian untuk penelitian ini. Total sampling digunakan dalam sampel. Hingga 45 personel dari bagian rawat inap menjadi sampel penelitian. Data dianalisis dengan menggunakan Korelasi Spearman Rank pada analisis bivariat dan analisis univariat untuk mengetahui persebaran karakteristik responden. Mengingat korelasi antara motivasi kerja dan stres kerja pada perawat di RS Setio Husodo kisaran sama dengan -0,334 dengan tingkat signifikansi 0,025, maka dapat dikatakan terdapat keterkaitan antara keduanya. Perbandingan antara sampel sebanyak 45 individu dengan nilai tabel menghasilkan nilai rtabel sebesar -0,334. Perawat di RS Setio Husodo Kisaran memiliki hubungan terbalik yang substansial antara motivasi kerja dan stres kerja. Ini menyiratkan bahwa stres terkait pekerjaan berkurang ketika motivasi kerja meningkat dan sebaliknya. Kata Kunci: Stres Kerja, Motivasi Kerja, Perawat Rawat Inap, Skala Likert, Rumah Sakit 
Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants Fitriani, Karlina Elreine; Faisal, Mohammad Reza; Mazdadi, Muhammad Itqan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Prastya, Septyan Eka
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/t158qq37

Abstract

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
Implementation of Copeland Method on Wrapper-Based Feature Selection Using Random Forest For Software Defect Prediction Aryanti, Agustia Kuspita; Herteno, Rudy; Indriani, Fatma; Nugroho, Radityo Adi; Muliadi, Muliadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/2pgffc67

Abstract

Software Defect Prediction is crucial to ensure software quality. However, high-dimensional data presents significant challenges in predictive modelling, especially identifying the most relevant features to improve model performance. Therefore, efforts are needed to address these issues, and one is to apply feature selection methods. This study introduces a new approach by applying the Copeland ranking method, which aggregates feature weights from multi-wrapper methods, including Recursive Feature Elimination (RFE), Boruta, and Custom Grid Search, using 12 NASA MDP datasets. The study also applies Random Forest classification and evaluates the model using AUC and t-Test. In addition, this study also compares the accuracy and precision values produced by each method. The results consistently show that the Copeland ranking method produces superior results compared to other ranking methods. The average AUC value obtained from the Copeland ranking method is 0.7496, higher than the Majority ranking method with an average AUC of 0.7416 and the Optimal Rank ranking method with an average AUC of 0.7343. These findings confirm that applying the Copeland ranking method in wrapper-based feature selection can enhance classification performance in software defect prediction using Random Forest compared to other ranking methods. The strength of the Copeland method lies in its ability to integrate rankings from various feature selection approaches and identify relevant features. The findings of this research demonstrate the potential of the Copeland ranking method as a reliable tool for ranking features obtained from various wrapper-based feature selection techniques. The implementation of this approach contributes to improved software defect prediction and provides new insights for the development of ranking methods in the future
Dimensionality Reduction Using Principal Component Analysis and Feature Selection Using Genetic Algorithm with Support Vector Machine for Microarray Data Classification Kartini, Dwi; Badali, Rahmat Amin; Muliadi, Muliadi; Nugrahadi, Dodon Turianto; Indriani, Fatma; Saputro, Setyo Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/mr7x9713

Abstract

DNA microarray is used to analyze gene expression on a large scale simultaneously and plays a critical role in cancer detection. The creation of a DNA microarray starts with RNA isolation from the sample, which is then converted into cDNA and scanned to generate gene expression data. However, the data generated through this process is highly dimensional, which can affect the performance of predictive models for cancer detection. Therefore, dimensionality reduction is required to reduce data complexity. This study aims to analyze the impact of applying Principal Component Analysis (PCA) for dimensionality reduction, Genetic Algorithm (GA) for feature selection, and their combination on microarray data classification using Support Vector Machine (SVM). The datasets used are microarray datasets, including breast cancer, ovarian cancer, and leukemia. The research methodology involves preprocessing, PCA for dimensionality reduction, GA for feature selection, data splitting, SVM classification, and evaluation. Based on the results, the application of PCA dimensionality reduction combined with GA feature selection and SVM classification achieved the best performance compared to other classifications. For the breast cancer dataset, the highest accuracy was 73.33%, recall 0.74, precision 0.75, and F1 score 0.73. For the ovarian cancer dataset, the highest accuracy was 98.68%, recall 0.98, precision 0.99, and F1 score 0.99. For the leukemia dataset, the highest accuracy was 95.45%, recall 0.94, precision 0.97, and F1 score 0.95. It can be concluded that combining PCA for dimensionality reduction with GA for feature selection in microarray classification can simplify the data and improve the accuracy of the SVM classification model. The implications of this study emphasize the effectiveness of applying PCA and GA methods in enhancing the classification performance of microarray data.
Machine Learning Implementation for Sentiment Analysis on X/Twitter: Case Study of Class Of Champions Event in Indonesia Hafizah, Rini; Saragih, Triando Hamonangan; Muliadi, Muliadi; Indriani, Fatma; Mazdadi, Muhammad Itqan
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.v7i2.81

Abstract

Sentiment analysis on social media is becoming an important approach in understanding public opinion towards an event. Twitter, as a microblogging platform, generates a large amount of data that can be utilized for this analysis. This study aims to evaluate and compare the performance of three classification algorithms, namely Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), in sentiment analysis related to the Clash of Champions event in Indonesia. To represent the text data, two feature extraction techniques are used, namely Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). In addition, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle data imbalance, while model optimization is performed using GridSearchCV. The research dataset consists of 1,000 tweets collected through web scraping, then manually processed and labeled before model training and testing. The results showed that the TF-IDF technique provided superior results compared to BoW. The Random Forest model with TF-IDF achieved the highest accuracy of 91%, while XGBoost with TF-IDF had the highest Area Under the Curve (AUC) of 0.91. The findings confirm that the selection of appropriate feature extraction techniques and algorithms can improve accuracy in sentiment analysis. This study can be applied in public opinion monitoring and data-driven decision-making. Future research can explore word embedding techniques and transformer-based deep learning models to improve semantic understanding and accuracy of sentiment analysis.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
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) Ahmad Zaki Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anggi Cahya Utari Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Ashar, Yulia Khairina Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Aulia, Rizky Gunadi Azizah, Azkiya Nur Badali, Rahmat Amin Baharuddin Siregar, Baharuddin Baron Hidayat Barus, Nency Utami Br Batubara, Rini Warahmah Berutu, Marwiyah Br Barus, Nency Utami br Damanik, Cici Rahayu Carolina, Ayu Daffa Dhiba Oesraini DALIMUNTHE, NADIYAH RAHMA Darmansyah, Rendi Daulay, Rangga Muriansyah Dendy Fadhel Adhipratama Dendy Dewi Sri Wahyuni, Dewi Sri Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Effendi, Khairunnisa Fadilah, Sylva Qamara Nur Fahira Ramadhani Saragih Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Fitriani, Karlina Elreine Friska Abadi Ghinaya, Helma Gunawan, Muhammad Khair Gustara, Rizki Asih Hafizah, Rini Harahap, Dwi Adelia Putri Harahap, Helma Denisah Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Iqbal, M. Irwan Budiman Irwan Budiman Lauchan, Agil maritho Lilies Handayani Lubis, Masruroh M. Apriannur M. Khairul Rezki Mahmud Mahmud Mahmudah, Kunti Masyithah, Ruhul Maulana, Muhammad Rafly Alfarizqy 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 Putra Apriadi Siregar Putri Maimunah Putri, Adelia Radityo Adi Nugroho Ramadhanu, Suhada Rapotan Hasibuan Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rizian, Rizailo Akfa Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno rusmining, rusmining Safira, Putri Salianto Salianto, Salianto Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satou, Kenji Sa’diah, Halimatus Selvia Indah Liany Abdie Siregar, Nurul Syahputri Siregar, Siti Romaito Soesanto, Oni Sri Rahayu Suci Wulandari Sugiyarto Surono, Sugiyarto Tarigan, David Brando Pratama Triyoolanda, Anggun Umar Ali Ahmad Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha Yabani, Midfai YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya