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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 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 Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CCIT (Creative Communication and Innovative Technology) Journal SPIRIT Progresif: Jurnal Ilmiah Komputer ILKOMNIKA: Journal of Computer Science and Applied Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Intelligent Computing and Health Informatics (JICHI) 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 RESLAJ: Religion Education Social Laa Roiba Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Chastine Fatichah
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Religion Arts Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Mandibular Image Segmentation and 3d Reconstruction using U-Net Model Mambaul Izzi; Chastine Fatichah; Hadziq Fabroyir
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 Muhammad Meftah Mafazy; Chastine Fatichah; Anny Yuniarti
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%.
Optimized Closed Frequent High Utility Itemset Mining Using OSR, OWL, and MSU Pruning on Retail Transaction Data Kinana Syah Sulanjari; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1311

Abstract

This research proposes the optimization of the Frequent Closed High-Utility Itemset Mining (FCHUIM) algorithm for retail transaction datasets using heuristic-based pruning techniques, Observed Support Ratio (OSR), Observed Weighted Lift (OWL), and Modified Subtree Utility (MSU). The algorithm aims to efficiently extract high-value itemsets that are both frequent and economically significant while minimizing redundant patterns through closed itemset mining. A real-world retail dataset from a consumer cooperative, comprising 56,274 transactions and 4,265 unique items, was used in the experiments. The study evaluates the effectiveness of each pruning technique, individually and in combination, across multiple scenarios of minimum support and utility thresholds. Results show that the proposed optimizations reduce the search space by up to 92.5%, significantly lowering execution time and memory usage. Sensitivity analyses reveal that the minimum utility parameter has a stronger impact on computational efficiency than minimum support, while scalability tests confirm the algorithm's ability to handle increasing dataset sizes with linear performance degradation. These findings confirm that the optimized FCHUIM algorithm is suitable for large-scale retail data mining applications, especially in scenarios requiring fast and concise pattern extraction. Future work may explore real-time integration into recommendation systems and adaptive thresholding for dynamic retail environments.
SentiBERT and Enhanced Bi-GRU for Weather-related Text Classification Using Lexical Features Mohamad Anwar Syaefudin; Arijal Ibnu Jati; Hilya Tsaniya; Chastine Fatichah; Diana Purwitasari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1320

Abstract

The growing volume of weather related content on social media platforms, especially Twitter, has highlighted the need for robust classification models that can handle noisy, ambiguous, and emotionally subtle language. However, existing models machine learning such as Support Vector Machines (SVM) often fail to effectively capture implicit sentiment and sequential context in short, real time texts. This study addresses the challenge of weather related text classification by proposing a hybrid architecture that combines SentiBERT, a sentiment aware transformer model, with an Enhanced BiGRU network equipped with Self Attention and LeakyReLU activation. Experiments were conducted using a five class(sunny, cloudy, rainy, extreme, other) dataset of weather related tweets with stratified cross validation across multiple deep learning models and tokenizers. Results show that the proposed SentiBERT + Enhanced BiGRU model outperformed all baselines, achieving 88.03% accuracy and 88.25% macro F1 score demonstrating its ability to better interpret contextual and emotional nuances. These findings imply that integrating sentiment specific embeddings with sequential modeling and lexical features offers a promising direction for future real time applications in climate monitoring and disaster alert systems.
Explainable BERT Embeddings for Veracity Assessment in Criminal Investigations Thoha Haq; Chastine Fatichah; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1327

Abstract

The binary classification of truth and lies is often a detriment in criminal investigations as statements are intentionally not entirely true nor entirely false. This ambiguity in the veracity of their claims demands more extensive methods such as explainable models. Explainable models, particularly SHapley Additive exPlanations (SHAP), can help dissect statements and narrow down information for a more thorough investigation. Data from the Miami University Deception Database, comprising of various statements and their veracity, was analyzed for its linguistic features. This research utilizes Bidirectional Encoder Representations from Transformers (BERT) Embeddings to provide contextual understanding of statements and Sentiment Lexicons to provide domain specific knowledge. Results show that the R² (coefficient of determination) of the 2-Gram embedding performed the best at 0.39 by being able to capture more context than the 1-Gram embedding while being more general than the 3-Gram and 4-Gram embeddings. Each variant of the BERT Embedding was proven to be much more effective than general word embedding such as GloVe, Word2Vec and FastText. SHAP values were able to capture key points of interest in a statement by narrowing down pivotal and decision-making points. These results highlight potential indicators of either deceptive or truthful language such as the word ‘something’ and ‘our’. These points of interest can help humans focus on key points of investigation and intervention.
Handling Ambiguity in App Review-Based Software Requirement Classification Using Multi-Label BERT Transfer Learning Stefani Tasya Hallatu; Muhammad Jerino Gorter; Andrea Bemantoro J; Diana Purwitasari; Chastine Fatichah; Hilya Tsaniya
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1333

Abstract

User-generated reviews on mobile applications represent a valuable yet ambiguous resource for classifying software requirements, particularly when multiple aspects—such as bugs, feature requests, and user experiences—are embedded within a single review. Although prior studies have shown the potential of transformer-based and multi-label models in improving text classification accuracy and efficiency, explicit handling of semantic ambiguity in multi-aspect reviews has not been addressed. This study proposes a multi-label classification approach using BERT-based transfer learning to manage ambiguity in app reviews. Each review is manually annotated with one or more relevant requirement categories. Preprocessing involves text cleaning, normalization, and BERT tokenization to convert reviews into structured representations. The classification model categorizes reviews into four classes: bug reports, feature requests, user experiences, and ratings. Evaluation results demonstrate strong performance, with F1-scores of 0.96 for bug reports, 0.95 for feature requests, 0.97 for ratings, and 0.80 for user experiences, confirming the model’s capability in capturing overlapping labels in ambiguous reviews. This approach offers a scalable and automated solution for extracting software requirements, enabling developers to better identify, categorize, and prioritize user needs from unstructured review data.
Deep Learning‑Based Sentiment Classification on Category Service and Resolution of Consumer Complaints in Digital Banking Kusuma, Irnayanti Dwi; Fatichah, Chastine
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.825

Abstract

The growth of digital banking in Indonesia has transformed customer interactions. It emphasizes the need to understand user sentiment and feedback. This study aims to analyze public perceptions of the Jenius digital banking application through sentiment analysis using deep learning methods enhanced by easy data augmented (EDA). The dataset written in Indonesian related to Jenius from Twitter. Data collected between August 2016 and August 2024 were manually annotated for sentiment polarity (positif, netral, negatif) and complaint handling categories (edukasi, konsultasi, fasilitasi, none). The EDA technique was used to enhance linguistic diversity and reduce class imbalance before training two deep learning models, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN). The results show that EDA + BiLSTM achieved an accuracy of 0.68, whereas EDA + CNN obtained 0.66. BiLSTM slightly outperforms CNN across precision, recall, and F1-score. These findings indicate that both models effectively handle augmented data, with the BiLSTM model demonstrating a better contextual understanding of Bahasa Indonesia. The integration of EDA significantly improves the robustness and performance of the model in sentiment and aspect-based classification. This study highlights the potential of EDA as a simple yet effective method for enhancing deep learning models.
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.
Enhancing Intraoral Dental Lesion Localization via Multi-Scale Ensemble Learning Using a Robust Weighted Box Fusion Approach Syarif, Hisyam; Fatichah, Chastine; Yuniarti, Anny; Zeng, Xinyou; Al-Haddad, Abdullah
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): Article In Press
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20127

Abstract

The early detection of dental diseases is essential for preventing severe oral health complications. However, automated lesion detection utilizing intraoral images remains highly challenging due to severe tooth overlap, occlusion, and visually similar anatomical structures. Under these complex conditions, conventional single-stage object detectors frequently produce redundant and inaccurate bounding boxes, which significantly degrades localization precision. To explicitly resolve this problem, this study proposes a robust multi-scale ensemble learning strategy that integrates bounding box predictions from YOLOv5 and YOLOv8 through a Weighted Boxes Fusion (WBF) mechanism. Unlike traditional post-processing techniques such as Non-Maximum Suppression (NMS) and Soft-NMS, the proposed method fuses overlapping bounding boxes by leveraging confidence-weighted spatial aggregation, thereby preserving critical detection information. Extensive experiments were conducted on a publicly validated intraoral image dataset comprising four distinct clinical classes: caries, cavity, cracks, and normal teeth. Quantitative evaluations demonstrate that the proposed WBF ensemble approach substantially outperforms single- model baselines. The integrated model achieves a mean Average Precision (mAP@0.5) of 66.14%, a Precision of 66.47%, and an Intersection over Union (IoU) of 90.83%, representing a massive improvement over the baseline mAP values of approximately 36 to 37%. Furthermore, rigorous statistical testing validates that these performance gains are highly significant (p < 0.05). Ultimately, these findings indicate that the proposed ensemble framework provides a reliable, high-precision solution for intraoral dental lesion localization, offering substantial viability for real-world clinical diagnostic applications.
Segmentasi Citra Dermoskopi Kanker Kulit Menggunakan Metode VGG-SegNet Ramadhani, Muhammad Rafi'; Fatichah, Chastine
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3464

Abstract

Skin cancer, particularly melanoma, has a high mortality rate, necessitating reliable early detection based on dermoscopic images. Accurate lesion segmentation is a crucial preprocessing step prior to deep learning–based classification. However, challenges remain in preserving lesion boundary details, handling variations in lesion size, and addressing limited data availability and hyperparameter optimization. This study proposes and evaluates various configurations of a hybrid VGG–SegNet model for skin lesion segmentation using the ISIC 2017 and ISIC 2018 datasets. The methodology includes image and ground truth validation, VGG16-based normalization, input image resolution variations (128×128, 160×160, and 256×256), and training–validation data splits of 50:50 and 70:30. The model is fine-tuned using skip connections and a multi-stage training scheme with a combination of Binary Cross-Entropy, Dice, and Focal Tversky Loss. The best-performing model achieves a Dice Coefficient of 0.899 and an Intersection over Union of 0.8177 on the validation set, demonstrating precise and efficient lesion segmentation.Keywords: Skin Cancer Segmentation; VGG-SegNet; ISIC; Fine Tuning AbstrakKanker kulit, khususnya melanoma, memiliki tingkat mortalitas tinggi sehingga memerlukan deteksi dini berbasis citra dermoskopi yang andal. Segmentasi lesi yang akurat merupakan tahap penting sebelum proses klasifikasi berbasis deep learning. Namun, tantangan masih muncul pada detail tepi lesi, variasi ukuran, serta keterbatasan data dan optimasi hyperparameter. Penelitian ini mengusulkan dan mengevaluasi berbagai konfigurasi model hibrida VGG–SegNet untuk segmentasi lesi kulit menggunakan dataset ISIC 2017 dan ISIC 2018. Tahapan meliputi validasi citra dan ground truth, normalisasi berbasis VGG16, variasi resolusi citra (128×128, 160×160, 256×256), serta pembagian data latih dan validasi dengan rasio 50:50 dan 70:30. Model di fine tuning menggunakan skip connection dan skema pelatihan bertahap dengan kombinasi Binary Cross-Entropy, Dice, dan Focal Tversky Loss. Model terbaik mencapai Dice Coefficient 0,899 dan IoU 0,8177 pada data validasi, menunjukkan segmentasi yang presisi dan efisien.Kata kunci: Segmentasi Kanker Kulit; VGG–SegNet; ISIC; Fine Tuning
Co-Authors Achmad Arwan Adhi Nurilham Aditya Bagusmulya Afrizal Laksita Akbar 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&#039;alif Akwila Feliciano Akwila Feliciano Al-Haddad, Abdullah Amalia Nurani Basyarah Amelia Devi Putri Ariyanto Amirullah Andi Bramantya Andika Pratama Andrea Bemantoro J Anisa Nur Azizah Anna Kholilah Anny Yuniarti Ardian Yusuf Wicaksono Ariana Yunita Arianto Wibowo Arif Sanjani, Lukman Arijal Ibnu Jati Ario Bagus Nugroho Arya Yudhi Wijaya Asmawati, Diah Avin Maulana Ayu Ismi Hanifah Benny Afandi Bilqis Amaliah Budi Pangestu Cahyaningtyas, Zakiya Azizah Daniel Oranova Siahaan Daniel Sugianto Daniel Swanjaya Darlis Heru Murti Darlis Herumurti Davin Masasih Deni Sutaji Desmin Tuwohingide Dhimas Pamungkas Wicaksono Diana Purwitasari Diana Purwitasari Diema Hernyka Satyareni Dimas Ari Setyawan 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 Fachrul Pralienka Bani Muhamad Fachrul Pralienka Bani Muhamad Faizin, Muhammad 'Arif Fajar, Aziz Fajrin, Ahmad Miftah Fandy Kuncoro Adianto Fandy Kuncoro Adianto Faried Effendy Fatonah, Nenden Siti FATRA NONGGALA PUTRA Febri Liantoni Febri Liantoni, Febri Fiqey Indriati Eka Sari Furqan Aliyuddien Ginardi, R.V. Hari Ginardi, Raden Venantius Hari Gou Koutaki Hadziq Fabroyir Handayani Tjandrasa Haniefardy, Addien Haq, Dina Zatusiva Hardika Khusnuliawati Hardika Khusnuliawati Hari Ginardi Hendra Mesra hidayat, dwi taufik Hilya Tsaniya Hilya Tsaniya Hisyam Syarif, Hisyam I Ketut Eddy Purnama Ilmi, Akhmad Bakhrul Imam Artha Kusuma Imamah Imamah Irzal Ahmad Sabilla Isye Arieshanti Ivan Agung Pandapotan Jayanti Yusmah Sari Johan Varian Alfa Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata Kinana Syah Sulanjari Kinana Syah Sulanjari Kusuma, Irnayanti Dwi Kusuma, Selvia Ferdiana Lukman Hakim M Rahmat Widyanto M. Rahmat Widyanto Machfud, M. Mughniy Mambaul Izzi Martini Dwi Endah Susanti Maulani, Irham Maulidiya, Erika Mauridhi Hery Purnomo Moch Zawaruddin Abdullah Mohamad Anwar Syaefudin Muhamad, Fachrul Pralienka Bani Muhammad Bahrul Subkhi Muhammad Fikri Sunandar Muhammad Jerino Gorter Muhammad Meftah Mafazy Muhammad Muharrom Al Haromainy Muhtadin Mustika Mentari Mutmainnah Muchtar Nafiiyah, Nur Nanik Suciati Nanik Suciati Narandha Arya Ranggianto Nazarrudin, Ahmad Ricky Nur Hayatin Nur Nafi’iyah Nur Nafi’iyah Nurilham, Adhi Nurina Indah Kemalasari Nursanti Novi Arisa 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 Ramadhani, Muhammad Rafi' Rangga Kusuma Dinata Rangga Kusuma Dinata Ratih Kartika Dewi Rendra Dwi Lingga P. Riduwan, Muhammad Riyanarto Sarno Rizal A Saputra Rizal A Saputra, Rizal A Rizal Setya Perdana Rizka Wakhidatus Sholikah 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 Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shofiya Syidada Siti Mutrofin Siti Mutrofin Siti Rochimah Stefani Tasya Hallatu Subali, Made Agus Putra Subhan Nooriansyah Subkhi, M. Bahrul Sudianjaya, Nella Rosa Suhariyanto Suhariyanto Surya Sumpeno Syah Dia Putri Mustika Sari Sylvi Novita Dewi Tanzilal Mustaqim Tesa Eranti Putri Thoha Haq Tsaniya, Hilya Tuwohingide, Desmin Umi Laily Yuhana, Umi Laily Umy Rizqi Vit Zuraida Wahyu Saputra, Vriza 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 Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zaenal Arifin, Agus Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas Zeng, Xinyou