Article Per Year (5 Year)
p-Index From 2021 - 2026
8.717
P-Index
This Author published in this journals
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

Published : 150 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 150 Documents
Search

 9   10   11   12   13 
Query expansion using novel use case scenario relationship for finding feature location Achmad Arwan; Siti Rochimah; Chastine Fatichah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5501-5516

Abstract

Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice Diana Purwitasari; Chastine Fatichah; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1928.298 KB)

Abstract

Recommendation of active or productive experts is indispensable in supporting collaborations. Activities of publication and citation indicate expert productivity. An expert can be inferred to have an interest in a subject through productivity in that particular topic. Since an expert can change interests over time, the contribution of this paper is a Discrete Choice Model (DCM) based on topic productivities to predict the primary interests of the experts. DCM uses features extracted from bibliographic data of citation relation and title-abstract texts. Before extracting productivity features and dynamicity features to represent interest changes, title clustering with KMeans++ is used to identify research topics. There are six productivity features and five dynamicity values for each productivity feature to demonstrate the expert behavior. Therefore, a clustered topic as a research interest is represented as an expert choice with 30 extracted features in the proposed method. The experiments used multinomial logistic regression for DCM and a log-likelihood indicator for the fitted models of the features. The resulted DCM models showed that productive behavior of the experts by doing many publications and receiving many citations effected to the precision of topic prediction by 80%. Some features were better for predicting primary interests of the expert. It was demonstrated with a lower precision value of 60% by using features that represent the expert behavior of only doing publication or only getting citation.
Perbaikan Prediksi Kesalahan Perangkat Lunak Menggunakan Seleksi Fitur dan Cluster-Based Classification Fachrul Pralienka Bani Muhamad; Daniel Oranova Siahaan; Chastine Fatichah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 3: Agustus 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1356.057 KB)

Abstract

High balance value of software fault prediction can help in conducting test effort, saving test costs, saving test resources, and improving software quality. Balance values in software fault prediction need to be considered, as in most cases, the class distribution of true and false in the software fault data set tends to be unbalanced. The balance value is obtained from trade-off between probability detection (pd) and probability false alarm (pf). Previous researchers had proposed Cluster-Based Classification (CBC) method which was integrated with Entropy-Based Discretization (EBD). However, predictive models with irrelevant and redundant features in data sets can decrease balance value. This study proposes improvement of software fault prediction outcomes on CBC by integrating feature selection methods. Some feature selection methods are integrated with CBC, i.e. Information Gain (IG), Gain Ration (GR), One-R (OR), Relief-F (RFF), and Symmetric Uncertainty (SU). The result shows that combination of CBC with IG gives best average balance value, compared to other feature selection methods used in this research. Using five NASA public MDP data sets, the combination of IG and CBC generates 63.91% average of balance, while CBC method without feature selection produce 54.79% average of balance. It shows that IG can increase CBC balance average by 9.12%.
Spatial Fuzzy C-means dan Rapid Region Merging untuk Pemisahan Sel Kanker Payudara Desmin Tuwohingide; Chastine Fatichah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 1: Februari 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (982.892 KB)

Abstract

Segmentation and overlapped cells separation are important phases in microscopic image processing of breast cancer, because the accuracy of overlapped cells separation result determines the accuracy of breast cancer cell calculation. The amount of breast cancer cells is considered by doctor in determining the action towards patients. Two of the most common topics discussed in previous studies are the problem of increasing the accuracy of overlapped cancer cell separation result by calculating the number of cancer cell and over-segmentation problem. Compared to watershed method, clustering method produces higher accuracy in separating overlapped cancer cells. In this paper, a combination of Spatial Fuzzy C-Means (SFCM) and Rapid Region Merging (RRM) method is proposed to separate the overlapped cells and handling the over-segmentation problem. The input image of overlapped cells separation phase is the result of breast cancer cell identification by Gram-Schmidt (GS) method, while the clustered cancer cells are overlapped cancer cells which are detected based on the area of geometric feature. 40 microscopic breast cancer cells image of benign and malignant type is used as the datasets. The average value of Mean Square Error (MSE) for cell identification is 0.07 and the average accuracy of overlapped cells separation using SFCM and RRM is 78.41%.
Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS Nursuci Putri Husain; Chastine Fatichah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 1: Februari 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1386.815 KB)

Abstract

Component Normalized Generalized Gradient Vector Flow Snake (CNGGVFS) method is the development of Gradient Vector Flow Snake (GVFS) method as an external force algorithm for active contour (snake) that can be used to get the contour of nucleus and cytoplasm of cervical smear image. However, CNGGVFS using a conventional calculation of edge map such as Sobel can not detect the nucleus area correctly in single cell cervical smear image segmentation. In this study, an external force algorithm in snake that uses Radiating Edge Map (REM) calculation to search the edge map in CNGGVFS, called as Radiating Component Normalized Generalized Gradient Vector Flow Snake (RCNGGVFS), is proposed. RCNGGVFS is used to get the contour of nucleus and cytoplasm of single cervical smear image. There are three main stages in this study, which are: pre-processing, initial segmentation, and contour segmentation. Experiments are conducted on Herlev data-set. The proposed method is compared with other methods in previous research in single cell cervical smear image segmentation. The experiment results show that the proposed method can detect the nucleus area correctly better than Radiating GVFS & Fuzzy C-Means (FCM) and Radiating GVFS & K-means. The average value of accuracy and Zijdenbos similarity index (ZSI) for nucleus segmentation is 95.34% and 88.06%. Then, the average value of accuracy and ZSI for cytoplasm segmentation is 83.48% and 87.16%. The evaluations show the proposed method can be used as a segmentation process of cervical smear image on automatic identification of cervical cancer.
Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering Dwi Taufik Hidayat; Chastine Fatichah; R.V. Hari Ginardi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 3: Agustus 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (758.98 KB)

Abstract

One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO.
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.
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'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