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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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Rekomendasi Produk Berbasis Collaborative Filtering Menggunakan Factorization Machine Graph Convolutional Networks Sherly Rosa Anggraeni; Diana Purwitasari; Chastine Fatichah; Yoga Yustiawan
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 5 No 2 (2023): Volume 5, Nomor 2, Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v5i2.556

Abstract

Rekomendasi produk memiliki peran yang signifikan dalam berbagai industri, termasuk e-commerce, ritel, perhotelan, dan keuangan. Rekomendasi dapat meningkatkan kepuasan pelanggan dan penjualan dengan membantu pelanggan menemukan produk yang relevan. Pendekatan collaborative filtering digunakan dalam rekomendasi produk ini karena data yang tersedia hanya berfokus pada fitur pengguna. Pendekatan ini memanfaatkan data interaksi pengguna-produk untuk mengungkap pola dan kesamaan di antara para pengguna. Representasi graf digunakan untuk memodelkan hubungan interaksi pengguna-produk, yang memungkinkan pemodelan yang lebih komprehensif dari ketergantungan dan hubungan antara pengguna dan produk. Penelitian ini menggunakan GCN dalam kombinasi dengan Factorization machine (FM) untuk meningkatkan personalisasi rekomendasi. GCN menggunakan konvolusi graf untuk menyebarkan dan memperbarui node embedding berdasarkan hubungan ketetanggaan mereka. GCN memanfaatkan informasi lingkungan sekitar dan struktur graf yang lebih luas, untuk meningkatkan pemahaman tentang preferensi pengguna dan menghasilkan rekomendasi yang dipersonalisasi. GCN juga dapat mengatasi keterbatasan metode lain dengan mempertimbangkan hubungan yang lebih rinci antar produk dan fitur unik dari setiap produk. FM mempertimbangkan interaksi antara fitur pengguna dan fitur produk, sehingga memahami preferensi pengguna secara lebih mendalam. Diharapkan dengan mengintegrasikan kekuatan GCN dan FM, rekomendasi produk dapat memberikan pengalaman pengguna yang lebih menarik dan menyenangkan.
Pemanfaatan Teknologi Informasi dalam Penyusunan Materi Pembelajaran Berbasis Multimedia Interaktif pada SDN Sutorejo I/240 Surabaya Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Handayani Tjandrasa
Sewagati Vol 7 No 6 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v7i6.553

Abstract

Model pembelajaran yang efektif diperlukan oleh setiap Lembaga Pendidikan. Di era digital ini, teknologi dapat dimanfaatkan untuk meningkatkan pembelajaran tersebut. Dengan perkembangan teknologi yang semakin pesat dan canggih tentunya akan membuat pengelola pendidikan, khususnya guru/pengajar akan semakin berupaya untuk meningkatkan kompetensinya mempelajari teknologi dalam rangka meningkatkan kualitas pembelajaran di sekolah. Sekolah Dasar Negeri Sutorejo I/240 Surabaya merupakan salah satu sekolah dasar negeri yang turut serta dalam pengembangan materi pembelajaran pada program Kementerian Pendidikan dan Kebudayaan (Kemdikbud) yaitu Rumah Belajar. Agar bahan materi pembelajaran menarik, terstruktur dan interaktif, maka guru menyusun materi pembelajaran dengan berbasis multimedia. Oleh karena itu, dalam rangka meningkatkan kualitas pembelajaran, dilakukan kegiatan pelatihan pemanfaatan teknologi informasi, seperti Microsoft PowerPoint untuk menyusun materi pembelajaran berbasis multimedia interaktif. Kegiatan terbagi menjadi empat tahap yaitu persiapan, pelatihan, pendampingan, dan evaluasi. Pelaksanaan pelatihan dan pendampingan dilakukan secara hybrid, yaitu daring dan luring di Laboratorium Pemrograman I Teknik Informatika ITS. Dan pelaksanaan evaluasi dilakukan secara luring di SDN Sutorejo I/240, Surabaya. Berdasarkan hasil evaluasi, peserta pelatihan yaitu guru dapat mengimplementasikan materi pelatihan dengan baik, sehingga peserta didik lebih tertarik dengan pembelajaran menggunakan Microsoft PowerPoint.
Klasifikasi Ulasan Berdasarkan Divisi Pada Google Play Menggunakan Metode Hierarchical Dirichlet Process dan Metode Ensemble Irham Maulani; Chastine Fatichah; Arya Yudhi Wijaya
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 6 No 1 (2024): Volume 6, Nomor 1, April 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i1.596

Abstract

Ulasan yang diberikan oleh pengguna pada aplikasi, dewasa ini menjadi umpan balik yang menjadi jembatan penghubung antara pengembang dan pengguna. Pengalaman secara langsung dalam menggunakan aplikasi dapat menjadi masukan yang dapat membuat aplikasi menjadi lebih baik. Ulasan yang dapat menjadi masukan adalah ulasan yang berkualitas baik dan berhubungan secara langsung terhadap pengalaman pengguna. Data ulasan yang banyak dan kalimat ulasan memiliki arti bias menyulitkan untuk memahami dan memilah ulasan secara manual, sehingga diharapkan klasifikasi secara otomatis membantu dalam pelimpahan masukan secara tepat pada divisi yang bertanggung jawab. Penelitian ini mengusulkan pendekatan klasifikasi menggunakan metode Ensemble pada dua kelas utama, yaitu divisi pengembangan dan divisi operasi. Setiap ulasan di ekstraksi fitur menggunakan metode Hierarchical Dirichlet Process (HDP) karena dapat membantu dalam mengelompokkan ulasan yang memiliki karakteristik arti yang secara sentimen ambigu dan emosional ke dalam topik-topik yang relevan. Ulasan diambil dari Google Play dan dilakukan pelabelan secara manual oleh pakar. Hasil penelitian menunjukkan bahwa dengan menggunakan metode Gradient Boosting menghasilkan performa yang lebih baik dibandingkan metode klasifikasi Ensemble lainnya yang diuji dengan menggunakan ekstraksi fitur HDP mendapatkan akurasi 0.63, precision 0.62, recall 0.55 dan F1 Score 0.52. Ekstraksi fitur menggunakan HDP memberikan performa yang lebih baik dibandingkan dengan metode pembanding Latent Dirichlet Allocating (LDA).
Deep Learning Approaches for Automatic Drum Transcription Cahyaningtyas, Zakiya Azizah; Purwitasari, Diana; Fatichah, Chastine
EMITTER International Journal of Engineering Technology Vol 11 No 1 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i1.764

Abstract

Drum transcription is the task of transcribing audio or music into drum notation. Drum notation is helpful to help drummers as instruction in playing drums and could also be useful for students to learn about drum music theories. Unfortunately, transcribing music is not an easy task. A good transcription can usually be obtained only by an experienced musician. On the other side, musical notation is beneficial not only for professionals but also for amateurs. This study develops an Automatic Drum Transcription (ADT) application using the segment and classify method with Deep Learning as the classification method. The segment and classify method is divided into two steps. First, the segmentation step achieved a score of 76.14% in macro F1 after doing a grid search to tune the parameters. Second, the spectrogram feature is extracted on the detected onsets as the input for the classification models. The models are evaluated using the multi-objective optimization (MOO) of macro F1 score and time consumption for prediction. The result shows that the LSTM model outperformed the other models with MOO scores of 77.42%, 86.97%, and 82.87% on MDB Drums, IDMT-SMT Drums, and combined datasets, respectively. The model is then used in the ADT application. The application is built using the FastAPI framework, which delivers the transcription result as a drum tab.
Aspect-based sentiment analysis: natural language understanding for implicit review Suhariyanto, Suhariyanto; Sarno, Riyanarto; Fatichah, Chastine; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6711-6722

Abstract

The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.
Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics Fajar, Aziz; Sarno, Riyanarto; Fatichah, Chastine
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1590-1600

Abstract

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.
Fog and rain augmentation for license plate recognition in tropical country environment Wahyu Saputra, Vriza; Suciati, Nanik; Fatichah, Chastine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3951-3961

Abstract

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.
Segmentation and Classification of Breast Cancer Histopathological Image Utilizing U-Net and Transfer Learning ResNet50 Sudianjaya, Nella Rosa; Fatichah, Chastine
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4186

Abstract

Breast cancer is the most common type of cancer among various types of cancer. Approximately 1 in 8 women in the United States die from breast cancer. Early screening and accurate diagnosis are essential for prevention and accelerated treatment intervention. Several artificial intelligence methods have emerged to develop effective segmentation, detection, and classification to determine cancer types. Although there has been progress in automated algorithms for breast cancer histopathology image analysis, many of these approaches still face several challenges. This study aims to address the challenges in breast cancer image analysis. This research method uses the development of the U-Net architecture combined with Transfer Learning using ResNet50. The encoder path aims to improve the model’s sensitivity in the segmentation and classification of cancer areas by utilizing deep hierarchical features extracted by ResNet50. In addition, data augmentation techniques are used to create a diverse and comprehensive training dataset, which improves the model’s ability to distinguish between different tissue types and cancer areas. The results of this study are U-Net and ResNet50, which show an average IoU of 0.482 and a Dice coefficient of 0.916. This study concludes that integrating UNet with Transfer Learning ResNet50 improves the segmentation and classification accuracy in breast cancer histopathology images and overcomes the problem of high computational requirements. This approach shows significant potential for improvement in early breast cancer detection and diagnosis.
Arrhythmia Classification with ECG Signal using Extreme Gradient Boosting (XGBoost) Algorithm Asmawati, Diah; Arif Sanjani, Lukman; Dimas Renggana, Christiant; Fatichah, Chastine; Mustaqim, Tanzilal
Journal of Technology and Informatics (JoTI) Vol. 6 No. 1 (2024): Vol. 6 No.1 (2024)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v6i1.792

Abstract

Heart disease is one of the most dangerous illnesses because it has the potential to take people's lives. One of the causes of heart disease is arrhythmia, an abnormal condition of the heartbeat. To diagnose arrhythmia, analysis of electrocardiographic (ECG) signals can be performed. However, this analysis is very difficult to do conventionally and has the potential for errors, so there is a need for automatic ECG classification to detect arrhythmia. This study aims to fill the research gap by creating an ECG classification model to detect arrhythmia using the XGBoost algorithm. The results are quite good for each class, with accuracies for class N at 98.87%, class SVEB at 99.37%, class VEB at 99.4%, class F at 99.75%, and class Q at 99.99%. However, compared to existing methods in previous research, these results are still considered not better than those models.
Smart Home for Supporting Elderly Based On Ultrawideband Positioning System Muhtadin; Nazarrudin, Ahmad Ricky; Purnama, I Ketut Eddy; Fatichah, Chastine; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84186

Abstract

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.
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