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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK Journal of Science IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Mikrotek Jurnal Simantec Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Jurnal Inspiration Briliant: Jurnal Riset dan Konseptual Journal of Development Research Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi INTEGER: Journal of Information Technology Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal ULTIMATICS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CCIT (Creative Communication and Innovative Technology) Journal SPIRIT ILKOMNIKA: Journal of Computer Science and Applied Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) Melek IT: Information Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal Research of Social Science, Economics, and Management Sewagati Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Chastine Fatichah
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS Febriani, Kristina; Fatichah, Chastine
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1209

Abstract

Forecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5.11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1.07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 and R-Square 0.88, RNN with MAPE value of 5.19%, MAE 2.91 and R-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error values among the other models. With forecasting results that have a small error rate, it can help management in making decisions to minimize costs in coal ordering and warehouse management.
UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Baskoro, Fajar
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1214

Abstract

In geographic analysis, land cover plays an important role in everything from environmental analysis to sustainable planning methods and physical geography studies. The Indonesian National Standard (SNI 7645:2014) classifies vegetation analysis based on density. There are four categories of vegetation density index: non-vegetation, bare, medium, and high. Technically, vegetation data can be obtained through remote sensing. Satellite and UAV data are two types of data used in remote sensing to collect information. This research will analyze land cover based on vegetation density information that can be collected through remote sensing. Based on vegetation density information from remote sensing, the information can help in land processing, Land Cover Classification is carried out based on vegetation density. Convolutional neural networks (CNN) have been trained extensively to apply their properties to land cover classification. This research will evaluate features extracted from Convolutional Neural Networks (ResNet 50, Inception-V3, DenseNet 121) which have previously been trained and continued with Decision Tree algorithms, Random Forest, Support Vector Machine and eXtreme Gradient Boosting to perform classification. From the comparison results of classification tests between machine learning methods, Support Vector Machine is superior to other machine learning methods. This is proven by the accuracy results obtained at 85% with feature extraction using ResNet-50 where the processing time is 8 minutes. Followed by the second-best model, namely ResNet-50 with XGBoost which obtained accuracy results of 82% with a processing time of 55 minutes. Meanwhile, the use of feature extraction using the DenseNet-121 method was obtained using a combination of the Support Vector Machine method and the XGBoost method with the accuracy obtained being 81%.
Mandibular Image Segmentation and 3d Reconstruction using U-Net Model Izzi, Mambaul; Fatichah, Chastine; Fabroyir, Hadziq
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1245

Abstract

Penelitian ini bertujuan untuk meningkatkan presisi dan efisiensi dalam segmentasi citra mandibula dan rekonstruksi 3D menggunakan model U-Net. Segmentasi otomatis dengan U-Net menangani tantangan metode manual yang memakan waktu. Struktur Encoder-Decoder pada U-Net memungkinkan pembelajaran fitur citra medis yang kompleks dengan akurasi tinggi, menghasilkan segmentasi yang konsisten dan presisi. Hasil penelitian menunjukkan bahwa Res U-Net mencapai performa segmentasi yang unggul dengan Dice Similarity Coefficient (DSC) sebesar 95,37%, meskipun memerlukan waktu komputasi yang lebih lama. Sementara itu, U-Net standar menawarkan efisiensi komputasi yang lebih tinggi dan cocok untuk aplikasi real-time meskipun akurasinya sedikit lebih rendah. Integrasi segmentasi dengan rekonstruksi 3D meningkatkan visualisasi anatomi mandibula, memperbaiki efektivitas perencanaan bedah, serta menyediakan alat simulasi interaktif untuk perawatan personal dan pelatihan profesional. Penggunaan standar DICOM memfasilitasi aksesibilitas antar perangkat medis, mendukung interoperabilitas sistem perawatan kesehatan. Studi ini menyimpulkan bahwa Res U-Net optimal untuk kebutuhan presisi tinggi, sedangkan U-Net lebih cocok untuk aplikasi dengan pemrosesan cepat. Temuan ini diharapkan dapat memajukan teknologi segmentasi dan visualisasi medis yang andal dan efektif dalam praktik klinis.
Audio Feature Analysis and Selection for Deception Detection in Court Proceedings Mafazy, Muhammad Meftah; Fatichah, Chastine; Yuniarti, Anny
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1250

Abstract

Deception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model's accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%.
Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript Faizin, Muhammad 'Arif; Suciati, Nanik; Fatichah, Chastine
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1256

Abstract

The imbalance of Javanese script data in ancient manuscript recognition poses a challenge due to the limited availability of data. A potential approach to addressing this issue is the use of Generative Adversarial Networks (GAN). This study evaluates the effectiveness of synthetic data generated using Enhanced Balancing GAN (EBGAN) in mitigating data imbalance. Various evaluation scenarios are conducted, including: (i) assessing the impact of syn-thetic data as augmentation, (ii) evaluating the sufficiency of synthetic data for recognition models, (iii) analyzing minority class oversampling with different selection strategies, and (iv) evaluating model generalization through cross-validation. Quantitative analysis of the generated synthetic data, based on Fréchet Inception Distance (FID) and Structural Similarity Index (SSIM), as well as visual assessment, indicates that the quality of synthetic data closely resembles real data. Additionally, experimental results demonstrate that combining real and synthetic data improves accuracy, precision, recall, and F1-score. The oversampling strategy for synthetic data has proven effective in meeting the data sufficiency requirements for training recognition models. Meanwhile, selecting minority classes and determining threshold values based on percentage, distribution, and model performance in oversampling can serve as guidelines for enhancing script recognition performance. Compared to previous methods, the use of EBGAN has been shown to produce more diverse synthetic data with better visual quality. However, further research is still needed to optimize GAN performance in supporting script recognition.
PREDICTION OF MULTIVARIATE TIME SERIES DATA USING ECHO STATE NETWORK AND HARMONY SEARCH Al Haromainy, Muhammad Muharrom; Fatichah, Chastine; Saikhu, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1051

Abstract

Multivariate time series data prediction is widely applied in various fields such as industry, health, and economics. Several methods can form prediction models, such as Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). However, this method has an error value more significant than the development method of RNN, namely the Echo State Network (ESN). The ESN method has several global parameters, such as the number of reservoirs and the leaking rate. The determination of parameter values dramatically affects the performance of the resulting prediction model. The Harmony Search (HS) optimization method is proposed to provide a solution for determining the parameters of the ESN method. The HS method was chosen because it is easier to implement, and based on other research, the HS method gets the optimum value better than other meta-heuristic methods. The methods compared in this study are RNN, ESN, and ESN-HS. Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are used to measure the error rate of forecasting results. ESN got a smaller error value than RNN, and ESN-HS produced a minor error value among the other trials, namely 0.782e-5 for RMSE and 0.28% for MAPE. The HS optimization method has successfully obtained the appropriate global parameters for the ESN prediction model.
DETECTION AND CLASSIFICATION OF RED BLOOD CELLS ABNORMALITY USING FASTER R-CNN AND GRAPH CONVOLUTIONAL NETWORKS Bramantya, Amirullah Andi; Fatichah, Chastine; Suciati, Nanik
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1118

Abstract

Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory’s in determining further medical actions. Convolutional Neural Networks (CNN) is a commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN requires large number of labeled training data. A classification of RBCs abnormalities in limited data is a challenge. In this research we explore a semi-supervised learning using Graph Convolutional Networks (GCN) to classify RBCs abnormalities with limited number of labeled sample images. The proposed method consists of 3 stages, i.e., extraction of Region of Interest (ROI) of RBCs from blood images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. The experiment was conducted on a publicly accessible blood sample image dataset to compare classification performance of pretrained CNN models (Resnet-101 and VGG-16) and GCN models (Resnet-101 + GCN and VGG-16 + GCN). The experiment showed that the GCN model build on VGG-16 features (VGG-16  + GCN) produced the best accuracy of 95%.
Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Ranggianto, Narandha Arya; Purwitasari, Diana; Fatichah, Chastine; Sholikah, Rizka Wakhidatus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5170

Abstract

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.
Leveraging Convolutional Block Attention Module (Cbam) For Enhanced Performance In Mobilenetv3-Based Skin Cancer Classification Priambodo, Anas Rachmadi; Fatichah, Chastine
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4546

Abstract

As the incidence of skin cancer continues to rise globally, effective automated classification methods become crucial for early detection and timely intervention. Lightweight neural networks such as MobileNetV3 offer promising solutions due to their minimal parameters, making them suitable for environment with low resource. This study aims to develop an automated multiclass skin cancer classification system by enhancing MobileNetV3 with the Convolutional Block Attention Module (CBAM). The primary goal is to achieve high classification accuracy without significantly increasing computational demands. We employed Bayesian optimization to automatically fine-tune model parameters and applied targeted data augmentation techniques to address class imbalance. CBAM was integrated to highlight diagnostically relevant regions within images. The proposed method was evaluated using the ISIC 2024 SLICE-3D dataset, which includes over 400,000 dermatoscopic images categorized into benign, basal cell carcinoma, melanoma, and squamous cell carcinoma classes. Preprocessing involved standardized resizing, normalization, and extensive geometric and photometric augmentations. Results demonstrated that our method achieved an accuracy of 98.97%, precision of 98.99%, recall of 98.97%, and an F1-score of 98.98%, surpassing previous state-of-the-art models by 1.86–6.52%. Remarkably, this improvement was achieved with minimal additional parameters due to the effective integration of CBAM. These results represent an advancement in automated medical image analysis, particularly for low resource settings, by combining lightweight CNNs with attention mechanisms and systematic hyperparameter exploration. 
Optimized Hybrid CNN-Residual BiLSTM with Adaptive Prediction System for Enhanced Gas Turbine Performance Forecasting Pratama, Andika; Fatichah, Chastine
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.10226

Abstract

Accurately forecasting critical performance parameters, such as Compressor Discharge Pressure (PCD), in gas turbines is a strategic imperative for ensuring operational reliability and energy efficiency, particularly in vital facilities like Central Processing Plants (CPPs). However, achieving reliable forecasts presents significant analytical challenges due to the complex multivariate, non-linear, and noisy nature of industrial sensor data, compounded by dynamic operational loads. This study introduces and validates an integrated analytical framework centered on a systematically optimized Hybrid Convolutional Neural Network-Residual Bi-Directional Long Short-Term Memory (CNN-Residual BiLSTM) architecture. This hybrid design synergistically leverages CNN layers for multi-scale temporal pattern extraction and Residual BiLSTM blocks for robust long-range dependency modelling, enhanced by residual connections for training stability. The framework emphasizes rigorous data pre-processing and the selection of a comprehensive feature set, incorporating thermodynamic, electrical, and operational control signals to provide a holistic view of the turbine's state. Automated hyperparameter optimization via the Optuna framework is employed to maximize the model's predictive potential. Empirical validation demonstrates that the optimized configuration's performance is superior to that of baseline models (RMSE = 0.0611, MAE = 0.0298, R² = 0.9601), confirming the framework's contribution to advancing data-driven performance diagnostics and predictive maintenance (PdM) strategies for gas turbines.
Co-Authors Achmad Arwan Adhi Nurilham Aditya Bagusmulya, Aditya afrizal laksita akbar, afrizal laksita Agung Prasetya Agus Subhan Akbar, Agus Subhan Agus Zainal Arifin Agus Zainal Arifin Ahmad Hayam Brilian, Ahmad Hayam Ahmad Saikhu Ahmad Syauqi Ahmad Syauqi Aini, Nuru Ainul Mu'alif Akwila Feliciano Akwila Feliciano Amalia Nurani Basyarah Amelia Devi Putri Ariyanto Andika Pratama Anisa Nur Azizah Anna Kholilah Anny Yuniarti Ardian Yusuf Wicaksono Ariana Yunita Arianto Wibowo Arif Sanjani, Lukman Ario Bagus Nugroho Arisa, Nursanti Novi Arya Yudhi Wijaya Aryo Harto, Aryo Asmawati, Diah Ayu Ismi Hanifah Benny Afandi Bilqis Amaliah Bramantya, Amirullah Andi Budi Pangestu Cahyaningtyas, Zakiya Azizah Christian Sri kusuma Aditya, Christian Sri kusuma Daniel Oranova Siahaan Daniel Sugianto Daniel Swanjaya Darlis Heru Murti Darlis Herumurti Davin Masasih Deni Sutaji Desmin Tuwohingide Dewi Rosida Dhimas Pamungkas Wicaksono Diana Purwitasari Diana Purwitasari Diema Hernyka Satyareni Dimas Renggana, Christiant Dini Adni Navastara, Dini Adni Djoko Purwanto Dwi Kristianto Dwi Taufik Hidayat edy susanto Eha Renwi Astuti Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha Eko Prasetyo Esa Prakasa Evan Tanuwijaya Evelyn Sierra Evy Kamilah Ratnasari Fabroyir, Hadziq Fachrul Pralienka Bani Muhamad Fachrul Pralienka Bani Muhamad Faida Royani Faizin, Muhammad 'Arif Fajar Baskoro Fajar, Aziz Fajrin, Ahmad Miftah Fandy Kuncoro Adianto Fandy Kuncoro Adianto Faried Effendy Farosanti, Lafnidita FATRA NONGGALA PUTRA Febri Liantoni Febri Liantoni, Febri Febriani, Kristina Fiqey Indriati Eka Sari Furqan Aliyuddien Ginardi, R.V. Hari Ginardi, Raden Venantius Hari Gou Koutaki Handayani Tjandrasa Haniefardy, Addien Haq, Dina Zatusiva Hardika Khusnuliawati Hardika Khusnuliawati Hari Ginardi Hendra Mesra hidayat, dwi taufik Hilya Tsaniya I Ketut Eddy Purnama Ilmi, Akhmad Bakhrul Imam Artha Kusuma Imamah Imamah Irfan Subakti, Misbakhul Munir Irzal Ahmad Sabilla Isye Arieshanti Ivan Agung Pandapotan Izzi, Mambaul Jayanti Yusmah Sari Johan Varian Alfa Junaidi Junaidi Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata Kusuma, Selvia Ferdiana Lukman Hakim M Rahmat Widyanto M. Rahmat Widyanto Machfud, M. Mughniy Mafazy, Muhammad Meftah Mamluatul Hani’ah Maulana, Avin Maulani, Irham Maulidiya, Erika Mauridhi Hery Purnomo Mirza Galih Kurniawan, Mirza Galih Moch Zawaruddin Abdullah Mohammad Sholik Muhamad, Fachrul Pralienka Bani Muhammad Bahrul Subkhi Muhammad Fikri Sunandar Muhammad Muharrom Al Haromainy Muhammad Riduwan Muhtadin Mustika Mentari Mutmainnah Muchtar Nafiiyah, Nur Nanik Suciati Nanik Suciati Narandha Arya Ranggianto Nazarrudin, Ahmad Ricky Nenden Siti Fatonah Nenden Siti Fatonah Nur Hayatin Nur Nafi’iyah Nur Nafi’iyah Nurilham, Adhi Nurina Indah Kemalasari Nursuci Putri Husain Nurwijayanti nuzula, Muhammad Iqbal firdaus Pradany, Latifa Nurrachma Priambodo, Anas Rachmadi Putra, Ramadhan Hardani R Dimas Adityo R. Dimas Adityo R. V. Hari Ginardi R.V Hari Ginardi R.V. Hari Ginardi Rachmad Abdullah Rahayu, Putri Nur Ramadhan Rosihadi Perdana Rangga Kusuma Dinata Rangga Kusuma Dinata Ratih Kartika Dewi Rendra Dwi Lingga P. Riyanarto Sarno Rizal A Saputra Rizal A Saputra, Rizal A Rizal Setya Perdana Rizka Wakhidatus Sholikah, Rizka Wakhidatus Rizqa Raaiqa Bintana Rozi, Fahrur RR. Ella Evrita Hestiandari Rully Soelaiman Safhira Maharani Safhira Maharani Sahmanbanta Sinulingga Salim Bin Usman Salim Bin Usman Sambodho, Kriyo Santoso, Bagus Jati Sarimuddin, Sarimuddin Septiyan Andika Isanta Setyawan, Dimas Ari Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shofiya Syidada Siti Mutrofin Siti Mutrofin Siti Rochimah Subali, Made Agus Putra Subhan Nooriansyah Subkhi, M. Bahrul Sudianjaya, Nella Rosa Suhariyanto Suhariyanto Surya Sumpeno Susanti, Martini Dwi Endah Syah Dia Putri Mustika Sari Sylvi Novita Dewi Tanzilal Mustaqim Tesa Eranti Putri Tsaniya, Hilya Tursina, Dara Tuwohingide, Desmin Umi Laily Yuhana, Umi Laily Umy Rizqi Vit Zuraida Wahyu Saputra, Vriza Wattiheluw, Fadli Husein Welly Setiawan Limantoro Wibowo, Prasetyo Wijoyo, Satrio Hadi Wilda Imama Sabilla Yoga Yustiawan Yosi Kristian Yudhi Purwananto Yuhana, Umi Laili Yuita Arum Sari Yulia Niza Yulia Niza Yunan Helmi Mahendra, Yunan Helmi Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zaenal Arifin, Agus Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas