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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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KLASIFIKASI DATA EEG UNTUK MENDETEKSI KEADAAN TIDUR DAN BANGUN MENGGUNAKAN AUTOREGRESSIVE MODEL DAN SUPPORT VECTOR MACHINE Yunan Helmi Mahendra; Handayani Tjandrasa; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 15, No. 1, Januari 2017
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v15i1.a633

Abstract

Tidur merupakan kebutuhan dasar manusia. Salah satu gangguan tidur yang cukup berbahaya adalah narkolepsi, yaitu gangguan tidur kronis yang ditandai dengan rasa kantuk yang luar biasa di siang hari dan serangan tidur yang terjadi secara tiba-tiba. Salah satu metode dokter untuk mendiagnosis penyakit narkolepsi adalah dengan melihat aktivitas gelombang otak (melalui sinyal EEG) pasien. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat mengklasifikasikan keadaan tidur dan bangun melalui sinyal EEG secara otomatis. Dataset EEG yang digunakan tersedia di Physionet. Pertama-tama data EEG yang menjadi masukan dilakukan normalisasi dan filtering. Proses filtering dilakukan untuk membagi data menjadi 3 subband yaitu theta, alpha, dan beta. Setelah itu pada masing-masing subband dilakukan tahap ekstraksi fitur menggunakan Autoregressive Model. Hasil estimasi koefisien AR model digunakan sebagai fitur. Metode yang digunakan untuk mengestimasi koefisien AR model yaitu metode Yule-Walker dan metode Burg. Dataset dibagi menjadi data latih dan data uji menggunakan 10-fold cross validation. Data training digunakan untuk membuat SVM Model. SVM Model digunakan untuk mengklasifikasikan data testing sehingga menghasilkan keluaran label 1 untuk tidur dan label 0 untuk bangun. Untuk menentukan kelas final dilakukan majority vote dari hasil klasifikasi masing-masing subband. Performa sistem diperoleh dengan menghitung akurasi, presisi, dan sensitivitas pada setiap skenario uji coba. Skenario uji coba yang dilakukan antara lain dengan memvariasikan order AR, fungsi kernel, dan parameter C pada SVM. Dari hasil uji coba yang dilakukan, metode Yule-Walker menghasilkan rata-rata akurasi 80.60%, presisi 78.19%, dan sensitivitas 77.56%. Metode Burg menghasilkan akurasi 94.01%, presisi 95.70%, dan sensitivitas 93.39%. Hasil tersebut menunjukkan metode Burg memiliki performa lebih baik dibandingan dengan metode Yule-Walker.
PERHITUNGAN DAN PEMISAHAN SEL DARAH PUTIH BERDASARKAN CENTROID DENGAN MENGGUNAKAN METODE MULTI PASS VOTING DAN K-MEANS PADA CITRA SEL ACUTE LEUKEMIA Nursanti Novi Arisa; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 16, No. 2, Juli 2018
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v16i2.a661

Abstract

Leukemia is one of the dangerous diseases that can cause death. One of the types of leukemia is acute leukemia that includes ALL (Acute Lymphoblastic Leukemia) and AML (Acute Myeloid Leukemia). The fastest identification against this disease can be done by computing and analysing white blood cell types. However, the manual counting and identification of the white blood cell types are still limited by time. Therefore, automatic counting process is necessary to be conducted in order to get the results more quickly and accurately. Previous studies showed that automatic counting process in the image of Acute Leukemia cells faced some obstacles, the existence of touching cell and the implementation of  geometry feature that cannot produce an accurate counting. It is because the shapes of the cell are various. This study proposed a method for the counting of white blood cells and the separation of touching cells on Acute Leukemia cells image by using Multi Pass Voting method (MPV) based on seed detection (centroid) and K-Means method. Initial segmentation used for separating foreground and background area is canny edge detection. The next stage is seed detection (centroid) using Multi Pass Voting method. The counting of white blood cells is based on the results of the centroid produced. The existence of the touching cells are  separated using K-Means method, the determination of the initial centroid  is based on the results of the Multi Pass Voting method. Based on the evaluation results of 40 images of Acute Leukemia dataset, the proposed method is capable to properly compute based on the centroid. It is also able to separate the touching cell into a single cell. The accuracy of the white blood cell counting result is about 98,6%.
Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake Martini Dwi Endah Susanti; Handayani Tjandrasa; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 16, No. 2, Juli 2018
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v16i2.a762

Abstract

Sebuah sistem penyaringan otomatis dan sistem diagnosa yang akurat sangat berguna untuk proses analisis hasil pemeriksaan pap smear. Langkah yang paling utama dari sistem tersebut adalah proses segmentasi sel nukleus dan sitoplasma pada citra hasil pemeriksaan pap smear, karena dapat memengaruhi keakuratan sistem. Normally Biased Generalized Gradient Vector Flow Snake (NBGGVFS) merupakan sebuah algoritma gaya eksternal untuk active contour (snake) yang menggabungkan metode Generalized Gradient Vector Flow Snake (GGVFS) dan Normally Biased Gradient Vector Flow Snake (NBGVFS). Dalam memodelkan snake, terdapat fungsi edge map. Edge map biasanya dihitung dengan menggunakan operator deteksi tepi seperti sobel. Namun, metode ini tidak dapat mendeteksi daerah nukleus dari citra smear serviks dengan benar. Penelitian ini bertujuan untuk segmentasi citra sel tunggal smear serviks dengan memanfaatkan penggunaan Radiating Edge Map untuk menghitung edge map dari citra dengan metode NBGGVFS. Metode yang diusulkan terdiri atas tiga tahapan utama, yaitu tahap praproses, segmentasi awal dan segmentasi kontur. Uji coba dilakukan dengan menggunakan data set Herlev. Pengujian dilakukan dengan membandingkan hasil segmentasi metode yang diusulkan dengan metode pada penelitian sebelumnya dalam melakukan segmentasi citra sel tunggal smear serviks. Hasil pengujian menunjukkan bahwa metode yang diusulkan mampu mendeteksi area nukleus lebih optimal metode penelitian sebelumnya. Nilai rata-rata akurasi dan Zijdenbos Similarity Index (ZSI) untuk segmentasi nukleus adalah 96,96% dan 90,68%. Kemudian, nilai rata-rata akurasi dan ZSI untuk segmentasi sitoplasma adalah 86,78% and 89,35%. Dari hasil evaluasi tersebut, disimpulkan metode yang diusulkan dapat digunakan sebagai proses segmentasi citra smear serviks pada identifikasi kanker serviks secara otomatis.
KLASTERISASI DOKUMEN MENGGUNAKAN WEIGHTED K-MEANS BERDASARKAN RELEVANSI TOPIK Muhammad Riduwan; Chastine Fatichah; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 17, No. 2, Juli 2019
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v17i2.a892

Abstract

Jumlah penelitian di dunia mengalami perkembangan yang pesat, setiap tahun berbagai peneliti dari penjuru dunia menghasilkan karya ilmiah seperti makalah, jurnal, buku dsb. Metode klasterisasi dapat digunakan untuk mengelompokkan dokumen karya ilmiah ke dalam suatu kelompok tertentu berdasarkan relevansi antar topik. Klasterisasi pada dokumen memiliki karakteristik yang berbeda karena tingkat kemiripan antar dokumen dipengaruhi oleh kata-kata pembentuknya. Beberapa metode klasterisasi kurang memperhatikan nilai semantik dari kata. Sehingga klaster yang terbentuk kurang merepresentasikan isi topik dokumen. Klasterisasi dokumen teks masih memiliki kemungkinan adanya outlier karena pemilihan fitur teks yang tidak optimal. Oleh karena itu dibutuhkan pemrosesan data yang tepat serta metode yang mengoptimalkan hasil klaster. Penelitian ini mengusulkan metode klasterisasi dokumen menggunakan Weighted K-Means yang dipadukan dengan Maximum Common Subgraph. Weighted k-means digunakan untuk klasterisasi awal dokumen berdasarkan kata-kata yang diekstraksi. Pembentukan Weighted K-Means berdasarkan perhitungan Word2Vec dan TextRank dari kata-kata dalam dokumen. Maximum common subgraph merupakan tahap pembentukan graf yang digunakan dalam penggabungan klaster untuk menghasilkan klaster baru yang lebih optimal. pembentukan graf dilakukan dengan perhitungan nilai Word2vec dan Co-occurrence dari klaster. Representasi topik dokumen tiap klaster dapat dihasilkan dari pemodelan topik Latent Dirichlet Allocation (LDA). Pengujian dilakukan dengan menggunakan dataset artikel ilmiah dari Scopus. Hasil dari analisis Koherensi topik menunjukkan nilai koherensi usulan metode adalah 0.532 pada dataset 1 yang bersifat homogen dan 0.472 pada dataset 2 yang bersifat heterogen.
FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS Afrizal Laksita Akbar; Chastine Fatichah; Ahmad Saikhu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1000

Abstract

Personal identification can be done by using face, fingerprint, palm prints, eye’s retina, or voice recognition which commonly called as biometric methods. Face recognition is the most popular and widely used among those biometric methods. However, there are some issues in the implementation of this method: lighting factor, facial expression, and attributes (chin, mustache, or wearing some accessories). In this study, we propose a combination method of Discrete Wavelet Transform and Stationary Wavelet Transform that able to improve the image quality, especially in the small-sized image. Moreover, we also use Histogram Equalization in order to correct noises such as over or under exposure, Discrete Cosine Transform in order to transform the image into frequency domain, and Deep Neural Networks in order to perform the feature extraction and classify the image. A 10-fold cross-validation method was used in this study. As the result, the proposed method showed the highest accuracy up to 92.73% compared to Histogram Equalization up to 80.73%, Discrete Wavelet Transform up to 85.85%, Stationary Wavelet Transform up to 64.27%, Discrete Cosine Transform up to 89.50%, the combination of Histogram Equalization, Discrete Wavelet Transform, and Stationary Wavelet Transform up to 69.77%, and the combination of Stationary Wavelet Transform, Discrete Wavelet Transform, and Histogram Equalization up to 77.39%.
FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS Avin Maulana; Chastine Fatichah; Nanik Suciati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1004

Abstract

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result.
ENHANCEMENT OF DECISION TREE METHOD BASED ON HIERARCHICAL CLUSTERING AND DISPERSION RATIO Dimas Ari Setyawan; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1005

Abstract

The classification process using a decision tree is a classification method that has a feature selection process. Decision tree classifications using information gain have a disadvantage when the dataset has unique attributes for each imbalanced class record and distribution. The data used for decision tree classification has 2 types, numerical and nominal. The numerical data type is carried out a discretization process so that it gets data intervals. Weaknesses in the information gain method can be reduced by using a dispersion ratio method that does not depend on the class distribution, but on the frequency distribution. Numeric type data will be dis-criticized using the hierarchical clustering method to obtain a balanced data cluster. The data used in this study were taken from the UCI machine learning repository, which has two types of numeric and nominal data. There are two stages in this research namely, first the numeric type data will be discretized using hierarchical clustering with 3 methods, namely single link, complete link, and average link. Second, the results of discretization will be merged again then the formation of trees with splitting attributes using dispersion ratio and evaluated with cross-validation k-fold 7. The results obtained show that the discretization of data with hierarchical clustering can increase predictions by 14.6% compared with data without discretization. The attribute splitting process with the dispersion ratio of the data resulting from the discretization of hierarchical clustering can increase the prediction by 6.51%.
PREDICTION OF MULTIVARIATE TIME SERIES DATA USING ECHO STATE NETWORK AND HARMONY SEARCH Muhammad Muharrom Al Haromainy; Chastine Fatichah; Ahmad Saikhu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

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

Abstract

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

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

Abstract

Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory’s in determining further medical actions. Convolutional Neural Networks (CNN) is a commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN requires large number of labeled training data. A classification of RBCs abnormalities in limited data is a challenge. In this research we explore a semi-supervised learning using Graph Convolutional Networks (GCN) to classify RBCs abnormalities with limited number of labeled sample images. The proposed method consists of 3 stages, i.e., extraction of Region of Interest (ROI) of RBCs from blood images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. The experiment was conducted on a publicly accessible blood sample image dataset to compare classification performance of pretrained CNN models (Resnet-101 and VGG-16) and GCN models (Resnet-101 + GCN and VGG-16 + GCN). The experiment showed that the GCN model build on VGG-16 features (VGG-16  + GCN) produced the best accuracy of 95%.
MULTI-DOCUMENT SUMMARIZATION USING A COMBINATION OF FEATURES BASED ON CENTROID AND KEYWORD Narandha Arya Ranggianto; Diana Purwitasari; Chastine Fatichah; Rizka Wakhidatus Sholikah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 2, July 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i2.a1195

Abstract

Summarizing text in multi-documents requires choosing important sentences which are more complex than in one document because there is different information which results in contradictions and redundancy of information. The process of selecting important sentences can be done by scoring sentences that consider the main information. The combination of features is carried out for the process of scoring sentences so that sentences with high scores become candidates for summary. The centroid approach provides an advantage in obtaining key information. However, the centroid approach is still limited to information close to the center point. The addition of positional features provides increased information on the importance of a sentence, but positional features only focus on the main position. Therefore, researchers use the keyword feature as a research contribution that can provide additional information on important words in the form of N-grams in a document. In this study, the centroid, position, and keyword features were combined for a scoring process which can provide increased performance for multi-document news data and reviews. The test results show that the addition of keyword features produces the highest value for news data DUC2004 ROUGE-1 of 35.44, ROUGE-2 of 7.64, ROUGE-L of 37.02, and BERTScore of 84.22. While the Amazon review data was obtained with ROUGE-1 of 32.24, ROUGE-2 of 6.14, ROUGE-L of 34.77, and BERTScore of 85.75. The ROUGE and BERScore values outperform the other unsupervised models.
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