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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

Author-ID : 264382

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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Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph Adhi Nurilham; Diana Purwitasari; Chastine Fatichah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 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 (1771.664 KB)

Document clustering based on topic similarities helps users in searching from a collection of scientific articles. Topic labels are necessesary for describing subjects of the document clusters. Clusters with related subjects or contextual similarities can be merged to produce more descriptive labels. Relations between those words in one context can be modelled as a graph. Instead of single word, this paper proposed cluster labeling of phrases from scientific articles withcluster merging based on graph. The proposed method begins with K-Means++ for clustering the scientific articles. Then, the candidates of word phrases from document clusters are extracted using Frequent Phrase Mining which inspired by Apriori algorithm. Each cluster result has a representation graph from those extracted word phrases. An indicator value from each graph shows any similarities of graph structures which is calculated with Maximum Common Subgraph (MCS). Those clusters are merged if there are any structure similarities between them. Topic labels of clusters are keyword phrases extracted from a representation graph of previous merged clusters using TopicRank algorithm. The merging process which becomes the contribution of this paper is considering topic distribution within clusters for phrase extraction. The proposed method evaluationis performed based on topic coherence of the merged clusterslabel. The results show that proposed method can improve topic coherence on the merged clusters with MCS graph size percentage as the key factor.Further observation shows that merged cluster labels consistent to MCS graph.

METODE FUZZY ID3 UNTUK KLASIFIKASI BENTUK WAJAH MANUSIA MENGGUNAKAN DENTAL PANORAMIC Nur Nafi’iyah; Chastine Fatichah
SPIRIT Vol 10, No 1 (2018): SPIRIT
Publisher : STMIK YADIKA BANGIL

Penelitian ini ingin menerapkan algoritma Fuzzy dan ID3 dalam mengklasifikasi bentuk wajah manusia. Tujuannya, yaitu untuk melihat keakurasian dan ketepatan algoritma ID3 dalam mengklasifikasi bentuk wajah manusia. Klasifikasi bentuk wajah dalam penelitian ini terdapat 3 bentuk, yaitu: oval, lancip, dan kotak. Manfaat yang diperoleh dalam penelitian ini, membantu tim forensik dalam melakukan identifikasi korban atau manusia dari segi bentuk wajah. Metode Fuzzy digunakan untuk menormalisasi nilai fitur dari gigi seri panoramic dan mengubah ke bentuk kategori. Sedangkan ID3 digunakan untuk mengklasifikasi bentuk wajah manusia menjadi 3 bentuk. Ekstraksi fitur yang digunakan dalam penelitian ini, yaitu: area, perimeter, lebar, panjang, rasio lebar/panjang, rasio area/perimeter, pusat_x dan pusat_y. Tahapan penelitian ini, meliputi: digitalisasi dental panoramic menjadikan file, kemudian melakukan segmentasi gigi seri rahang atas, mengekstraksi fitur gigi seri, selanjutnya memasukkan nilai fitur ke dalam database dan dilakukan pelatihan ID3. Pelatihan fitur gigi seri panoramic menggunakan metode ID3 menghasilkan tree, dan rule. Rule dari ID3 digunakan klasifikasi bentuk wajah manusia menunjukkan nilai akurasi sebesar 65% dari total data 26 gigi seri dental panoramic. Keywords—Fuzzy ID3, Bentuk Wajah Manusia, Dental Panoramic.

Hypergraph-Partitioning pada Co-Authorship Graph untuk Pengelompokan Penulis Berdasarkan Topik Penelitian Daniel Swanjaya; Chastine Fatichah
Melek IT : Information Technology Journal Vol. 1 No. 1 (2015): Melek IT : Information Technology Journal
Publisher : Informatics Engineering Department-UWKS

Research topics can be seen from Abstraction research documents, for example, reports Scientific Writing (KTI) in the form of Final Project, Thesis and Dissertation. Research Topics of KTI is a collection of important words that indicate the area / field of study of the KTI. A guided KTI some supervisor, and a lecturer normally would guide some particular topic. Some lecturers have the same field of research formed a research group within the Department, but some courses are lecturers who exhibit similarities field of research. At this thesis proposed a method for classifying Writer (Lecturer) based on common research topics in Co-Authorship Graph using the Hypergraph Partitioning, making it possible to create a research group within the scope of inter Programs or college level. The method is divided into three stages: extraction of research topics, pembentuksn Co-Authorship Graph, and grouping author. Extraction of research topics, get the topic of EI by Title and Abstract using Latent Dirichlet Allocation (LDA). Formation of Co-Authorship Graph, where the nodes are the author, edge is the collaborative relationship and similarity / resemblance of research topics, and the weighting edge is Jaccard and cosine values similary research topics between author. Grouping Writers on Co-Authorship Graph using the Hypergraph Partitioning. Test method uses data from the Research Institute of Research and Community Service (LPPM) ITS. Grouping the results are validated using the Silhouette and Entropy. The final results showed that the grouping has been formed group Authors whose members come from the Department or a different field, with high similarity topic.

EFFECTIVENESS OF DEEP LEARNING APPROACH FOR TEXT CLASSIFICATION IN ADAPTIVE LEARNING Umi Laili Yuhana; Imamah Imamah; Chastine Fatichah; Bagus Jati Santoso
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.285

Klasifikasi text sangat bermanfaat dan dibutuhkan diberbagai bidang. Salah satu bidang yang membutuhkan klasifikasi text adalah E-learning yang bersifat adaptive atau disebut sebagai adaptive learning sistem. Adaptive learning sytem adalah sistem pembelajaran online yang dapat memberikan rekomendasi pembelajaran berdasarkan kebutuhan pengguna. Adaptive learning memiliki dua bagian, yaitu modul learning dan modul testing. Modul learning adalah bagian dari sistem yang bertugas untuk memberikan rekomendasi pembelajaran bagi pengguna, sedangkan modul testing bertugas untuk menguji dan memberikan penilaian terhadap hasil pembelajaran yang diperoleh dari modul learning. Materi pembelajaran pada modul learning memerlukan klasifikasi text berdasarkan tingkat kesulitannya untuk memastikan bahwa pengguna dengan level kemampuan rendah juga mendapatkan materi pembelajaran yang mudah, dan rekomendasi ini akan dinamis mengikuti perkembangan kemampuan pengguna. Pada penelitian ini, akan dibahas bagian kecil dari sistem pembelajaran adaptive pada modul learning yaitu tahap klasifikasi text. Dataset yang digunakan dalam penelitian ini adalah mata pelajaran IPA untuk tingkat SMP yang didapatkan dari Ruang Guru dan merupakan salah satu platform E-learning di Indonesia. Metode yang digunakan dalam penelitian ini adalah CNN, RNN dan HAN dengan menggunakan word embedding Word2Vec

Exposure Fusion Framework in Deep Learning-Based Radiology Report Generator Hilya Tsaniya; Chastine Fatichah; Nanik Suciati
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13572

Writing a radiology report is time-consuming and requires experienced radiologists. Hence a technology that could generate an automatic report would be beneficial. The key problem in developing an automated report-generating system is providing a coherent predictive text. To accomplish this, it is important to ensure the image has good quality so that the model can learn the parts of the image in interpreting, especially in medical images that tend to be noise-prone in the acquisition process. This research uses the Exposure Fusion Framework method to enhance the quality of medical images to increase the model performance in producing coherent predictive text. The model used is an encoder-decoder with visual feature extraction using a pre- trained ChexNet, Bidirectional Encoder Representation from Transformer (BERT) embedding for text feature, and Long-short Term Memory (LSTM) as a decoder. The model’s performance with EFF enhancement obtained a 7% better result than without enhancement processing using an evaluation value of Bilingual Evaluation Understudy (BLEU) with n-gram 4. It can be concluded that using the enhancement method effectively increases the model’s performance.

Feature Selection Using Hybrid Binary Grey Wolf Optimizer for Arabic Text Classification Muhammad Bahrul Subkhi; Chastine Fatichah; Agus Zainal Arifin
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13769

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature selection requires solution quality, stability, conver- gence speed, and the ability to find the global optimal. This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic text classification. The HBGWO method combines the local search capabilities or exploratory of the BGWO and the search capabilities around the best solutions or exploits of the PSO. HBGWO method also combines SCA’s capabilities in finding global solutions. The data set used Arabic text from islambook.com, which consists of five Hadith books. The books selected five classes: Tauhid, Prayer, Zakat, Fasting, and Hajj. The results showed that the BGWO-PSO-SCA feature selection method with the fitness function search and classification method using SVM could per- form better on Arabic text classification problems. BGWO-PSO with fitness function and the classification method using SVM (C=1.0) gives a high accuracy value of 76.37% compared to without feature selection. The BGWO-PSO-SCA feature selec- tion method provides an accuracy value of 88.08%. This accuracy value is higher than the BGWO-PSO feature selection and other feature selection methods.

Modification of IDTCS Method for Touching Leukemia Cell Grouping Nenden Siti Fatonah; Chastine Fatichah; Handayani Tjandrasa
Journal Research of Social Science, Economics, and Management Vol. 1 No. 8 (2022): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Morphological analysis and calculation of the number of white blood cells on microscopic images are stages in diagnosing leukemia. Constraints in developing a system for diagnosing leukemia are white blood cell segmentation and counting of the number single cells in touching cell. We propose to modify the Iterative Distance Transform For Convex Sets (IDTCS) method to separate the touching leukemia cells. The IDTCS method is used to determine markers for each cell in touching cells. The marker results from the IDTCS method are used as cell centroids and the next process is pixels clustering based on the nearest cell centroid using the euclidean distance function. The data used are microscopic images of Acute Lymphoblastic Leukemia (ALL). The experimental results show that using modified IDTCS method for clustering produces better accuracy compared to the K-Means clustering and Watershed methods.

Tajweed-YOLO: Object Detection Method for Tajweed by Applying HSV Color Model Augmentation on Mushaf Images Anisa Nur Azizah; Chastine Fatichah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4739

Tajweed is a basic knowledge of learning to read the Al-Qur’an correctly. Tajweed has many laws grouped into several parts so that only some people can memorize and implement Tajweed properly. Therefore, it is necessary to have an automatic detection system to facilitate the recognition of Tajweed, which can be used daily. This study presents Tajweed-YOLO, which applies the HSV color augmentation model to detect Tajweed objects in Mushaf images using YOLO. The contribution to this study was to compare the three versions of You Only Look Once (YOLO), i.e., YOLOv5, YOLOv6, and YOLOv7, and usage of the HSV color model augmentation to improve Tajweed detection performance. Comparing the three YOLO versions aims to solve problems in detecting small objects and recognizing various forms of Mushaf writing fonts in Tajweed detection. Meanwhile, the HSV color model aims to recognize Tajweed objects in various Mushaf and handle minority class problems. In this study, we collected four different Al-Qur’an mushaf with 10 Tajweed classes. The augmentation process can increase the detection performance by up to 85% compared to without augmentation 6th Class (Mad Jaiz Munfashil) using YOLOv6. The comparison of three YOLO versions concluded that YOLOv7 was better than YOLOv5 and YOLOv6, seen in data with augmentation and without augmentation. The evaluation results of mAP0.5 on 17 test data on the YOLOv7, YOLOv6, and YOLOv5 models are 80%, 69%, and 71%, respectively. These results prove that this research model’s results are suitable for the real-time detection of Tajweed.

Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification Dina Zatusiva Haq; Chastine Fatichah
IPTEK The Journal for Technology and Science Vol 34, No 1 (2023)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v34i1.14968

Breast cancer is the leading cause of death in women worldwide; prevention of possible death from breast cancer can be decreased by early identification ultrasound image analysis by classifying ultrasound images into three classes (Normal, Benign, and Malignant), where the dataset used has imbalanced data. Imbalanced data cause the classification system only to recognize the majority class, so it is necessary to handle imbalanced data. In this study, imbalanced data can be handled by implementing the Deep Convolution Generative Adversarial Network (DCGAN) method as the addition of synthetic images to the training data. The DCGAN method generates synthetic images with feature learning on a Convolutional Neural Network (CNN), making DCGAN more stable than the basic generative adversarial network method. Synthetic and original images were further classified using the CNN GoogleNet method, which performs well in image classification and with reasonable computation cost. Synthetic ultrasound images were generated using a tuning hyperparameter in the DCGAN method to adjust the input size on GoogleNet for imbalanced data handling. From the experiment result, the implementation of DCGAN-GoogleNet has a higher accuracy in handling imbalanced data than conventional augmentation and other previous research, with an accuracy value reaching 91.61%, which is 1% to 4% higher than the accuracy value in the previous method.

Pemanfaatan E-commerce dan Media Sosial Guna Meningkatkan Ekonomi dan Proses Bisnis UMKM Koppontren NURILA Bangkalan Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Handayani Tjandrasa; Agus Zainal Arifin; Zakiya Azizah Cahyaningtyas; Yulia Niza; Evelyn Sierra; Daniel Sugianto; Kevin Christian Hadinata; Salim Bin Usman; Muhammad Fikri Sunandar; Fiqey Indriati Eka Sari
Sewagati Vol 6 No 4 (2022)
Publisher : Pusat Publikasi ITS

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran yang besar dalam bidang industri dan ekonomi suatu negara. Di era digital ini, pemanfaatan teknologi untuk meningkatkan produktifitas UMKM sudah marak dilakukan. Sayangnya pemanfaatan tekonologi ini belum diterapkan pada UMKM dari Koperasi Pondok Pesantren Addimyathy Nurul Iman Labang (Koppontren NURILA). Tim pengabdi berinisiatif melaksanakan pelatihan untuk meningkatkan produktifitas UMKM Koppontren NURILA. Kegiatan terbagi menjadi empat tahap yaitu persiapan, pelatihan, pendampingan, dan evaluasi. Kegiatan ini mengangkat topik tentang pemanfaatan e-commerce dan media sosial untuk peningkatan ekonomi dan proses bisnis UMKM. Pelaksanaan pelatihan dan pendampingan dilakukan secara hybrid, yaitu daring dan luring di lokasi UMKM Koppontren NURILA. Berdasarkan hasil evaluasi, peserta kegiatan merasa puas terhadap kualitas materi dengan nilai 4.35 dari skala 5.

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 Nafiâ€™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

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