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All Journal Jurnal Informatika Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Jurnal Teknik ITS IPTEK The Journal for Technology and Science Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Ilmiah Pangabdhi Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Jurnal Inspiration Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTEGER: Journal of Information Technology IKRA-ITH Informatika : Jurnal Komputer dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) SISFO Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information System,Graphics, Hospitality and Technology ILKOMNIKA: Journal of Computer Science and Applied Informatics Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Nusantara Journal of Computers and its Applications
Diana Purwitasari
Alumni Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Strategi Pengenalan Pemrograman Web di SMP Al-Hikmah Surabaya: Pendekatan Inovatif untuk Pendidikan Digital Raharjo, Agus Budi; Maheswari, Clarissa Luna; Purwitasari, Diana; Sunaryono, Dwi; Baskoro, Fajar
Sewagati Vol 8 No 4 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i4.1057

Abstract

Di tengah perkembangan teknologi yang semakin canggih, keterampilan pemrograman web menjadi aset yang berharga, terutama bagi generasi muda yang sedang menyiapkan diri untuk era digital. Pengabdian masyarakat ini berisi kegiatan pelatihan pemrograman web di SMP Al-Hikmah Surabaya, dengan tujuan untuk menanamkan dasar-dasar pemrograman kepada siswa dan mengintegrasikan keahlian ini dalam kurikulum sekolah menengah. Mengadaptasi silabus Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya dan beragam sumber literatur, program pelatihan ini dirancang untuk memberikan pengenalan kepada HTML, CSS, JavaScript, dan kerangka kerja Bootstrap. Pelatihan ini melibatkan mahasiswa dan dosen dari Departemen Teknik Informatika yang berkolaborasi dengan ekstrakurikuler pemrograman di SMP Al-Hikmah. Dengan pendekatan interaktif, praktis, dan kolaboratif, kegiatan ini telah meningkatkan pemahaman teknologi informasi di kalangan siswa, mendorong kreativitas, serta memperkuat persiapan para siswa untuk pendidikan lanjutan dan tantangan masa depan. Inisiatif ini juga menargetkan keluaran dalam bentuk publikasi ilmiah dan materi pelatihan yang dapat diakses oleh publik, menandai kontribusi berkelanjutan terhadap pengembangan pendidikan digital di Indonesia.
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.
A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates Suwida, Katon; Kardawi, Muhammad Yusuf; Purwitasari, Diana; Mabahist, Fahril
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.v11i1.768

Abstract

When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%.
Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan Hamidi, Mohammad Zaenuddin; Purwitasari, Diana; Anggraini, Ratih Nur Esti
Techno.Com Vol. 22 No. 1 (2023): Februari 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i1.7268

Abstract

Penelitian kesehatan harus melalui proses telaah etik yang bertujuan untuk mengantisipasi dugaan atas risiko fisik, sosial, ekonomi dan psikologis. Secara etik penelitian kesehatan dapat diterima apabila pada penelitian tersebut mampu dibuktikan dengan metode ilmiah yang valid serta lulus uji etik sebelum penelitian dilakukan. Untuk memastikan pada ringkasan penelitian kesehatan terdapat aspek etik, dibutuhkan kata kunci yang dapat dijadikan representasi dari isi ringkasan tersebut. Salah satu pendekatan yang sering dilakukan adalah dengan menghitung frekuensi kemunculan kata dalam dokumen. Pendekatan lain yaitu pendekatan YAKE dan keyBERT yang tidak hanya menghitung frekuensi kata namun juga menghitung konteks kata. Selain melakukan ekstraksi dilakukan juga proses ekspansi kueri sebagai upaya memperluas istilah yang dapat mewakili masing-masing aspek etik. Salah satu pendekatan yang digunakan untuk ekspansi kueri adalah model word2Vec. Penelitian ini mengusulkan metode pengembangan ekspansi kueri dengan dan metode ekstraksi kata kunci seperti TFIDF,YAKE dan keyBERT dan mengombinasikannya dengan fuzzy. Hasil eksperimen menunjukkan bahwa metode paling unggul secara presisi yaitu YAKE dan gabungan antara TFIDF + YAKE + keyBERT dengan nilai tertinggi 46% kemudian dari untuk recall model YAKE mendapat nilai tertinggi dengan angka 72% dan untuk nilai F1-Score yang paling unggul adalah metode YAKE dengan nilai tertinggi 54%.
Content and Network Feature in Attention-based Neural Network for Stance Detection on COVID-19 Vaccination Tweets Bimantara, I Made Satria; Irdayanti, Marina; Nisa, Chilyatun; Purwitasari, Diana
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2671

Abstract

Stance detection in COVID-19 vaccination utilizing tweets is crucial for several reasons, such as public health communication, monitoring vaccine sentiment, and identifying misinformation. This research aims to explore the use of attention-based neural networks for stance detection in Indonesian COVID-19 vaccination tweets. The research focuses on enhancing accuracy by integrating content and network features. The content features represent the tweet's text, while network features define the user account's following or unfollowing. The primary contribution of this research is the development of an Attention Long Short-Term Memory (AttLSTM) model for stance detection in Indonesian tweets related to the COVID-19 vaccination. This model combines content and network features to improve accuracy in classifying user attitudes. We also highlight the performance differences between Word2Vec and FastText for numerical text representation in the AttLSTM model. The research used the Indonesian COVID-19 vaccination-related tweet dataset from prior research. The dataset is extracted using user metadata to obtain content and network features necessary to represent users' interest in tweets. Our research method involves data preparation, preprocessing, extraction of content and network features, and the development of an AttLSTM model. By integrating content and network features into the AttLSTM model with Word2Vec text representation, the study demonstrates superior performance compared to the LSTM baseline model and FastText. Adding attention mechanisms to the baseline LSTM model can capture crucial information, such as the minority class inside a tweet's text. Future research will involve exploring advanced data processing methods and ensemble learning techniques to further improve the model's performance.
EKSTRAKSI TRENDING ISSUE DENGAN PENDEKATAN DISTRIBUSI KATA PADA PEMBOBOTAN TERM UNTUK PERINGKASAN MULTI-DOKUMEN BERITA Aditya, Christian Sri Kusuma; Fatichah, Chastine; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 14, No. 2, Juli 2016
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v14i2.a570

Abstract

Penggunaan trending issue dari media sosial Twitter sebagai kalimat penting efektif dalam proses peringkasan dokumen dikarenakan trending issue memiliki kedekatan kata kunci terhadap sebuah kejadian berita yang sedang berlangsung. Pembobotan term dengan TFIDF yang hanya berbasis pada dokumen itu tidak cukup untuk menentukan in-deks dari suatu dokumen. Penentuan indeks yang akurat juga bergantung pada nilai informatif suatu term terhadap kelas atau cluster. Term yang sering muncul di banyak kelas atau cluster seharusnya tidak menjadi term yang penting meskipun nilai TFIDF-nya tinggi. Penelitian ini bertujuan untuk melakukan peringkasan multi dokumen berita menggunakan ekstraksi trending issue dengan pendekatan term distribution on centroid based (TDCB) pada pembobotan fitur dan mengintegrasikannya dengan query expansion sebagai kata kunci dalam peringkasan dokumen. Metode TDCB dilakukan dengan mempertimbangkan adanya kemunculan sub topic dari cluster hasil pengelompokan tweets yang dapat dijadikan nilai informatif tambahan dalam penentuan pembobotan kalimat penting penyusunan ringkasan. Tahapan yang dilakukan untuk menghasilkan ringkasan multi dokumen berita antara lain ekstraksi trending issue, query expansion, auto labelling, seleksi berita, ekstraksi fitur berita, pembobotan kalimat penting dan penyusunan ringkasan. Hasil percobaan menunjukan metode peringkasan dokumen dengan menambahkan nilai informatif sub topic trending issue NeFTIS-TDCB menunjukan nilai rata-rata max-ROUGE-1 terbesar 0.8615 untuk n=30 dari seluruh varian topik berita.
REDUKSI DIMENSI FITUR MENGGUNAKAN ALGORITMA ALOFT UNTUK PENGELOMPOKAN DOKUMEN Hani’ah, Mamluatul; Fatichah, Chastine; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 14, No. 2, Juli 2016
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v14i2.a573

Abstract

Pengelompokan dokumen masih memiliki tantangan dimana semakin besar dokumen maka akan menghasilkan fitur yang semakin banyak. Sehingga berdampak pada tingginya dimensi dan dapat menyebabkan performa yang buruk terhadap algoritma clustering. Cara untuk mengatasi masalah ini adalah dengan reduksi dimensi. Metode reduksi dimensi seperti seleksi fitur dengan metode filter telah digunakan untuk pengelompokan dokumen. Akan tetapi metode filter sangat tergantung pada masukan pengguna untuk memilih sejumlah n fitur teratas dari keseluruhan dokumen. Algoritma ALOFT (At Least One FeaTure) dapat menghasilkan sejumlah set fitur secara otomatis tanpa adanya parameter masukan dari pengguna. Karena sebelumnya algoritma ALOFT digunakan pada klasifikasi dokumen, metode filter yang digunakan pada algoritma ALOFT membutuhkan adanya label pada kelas sehingga metode filter tersebut tidak dapat digunakan untuk pengelompokan dokumen. Pada penelitian ini diusulkan metode reduksi dimensi fitur dengan menggunakan variasi metode filter pada algoritma ALOFT untuk pengelompokan dokumen. Sebelum dilakukan proses reduksi dimensi langkah pertama yang harus dilakukan adalah tahap preprocessing kemudian dilakukan perhitungan bobot tfidf. Proses reduksi dimensi dilakukan dengan menggunakan metode filter seperti Document Frequency (DF), Term Contribution (TC), Term Variance Quality (TVQ), Term Variance (TV), Mean Absolute Difference (MAD), Mean Median (MM), dan Arithmetic Mean Geometric Mean (AMGM). Selanjutnya himpunan fitur akhir dipilih dengan algoritma ALOFT. Tahap terakhir adalah pengelompokan dokumen menggunakan dua metode clustering yang berbeda yaitu k-means dan Hierarchical Agglomerative Clustering (HAC). Dari hasil ujicoba didapatkan bahwa kualitas cluster yang dihasilkan oleh metode usulan dengan menggunakan algoritma k-means mampu memperbaiki hasil dari metode VR.
K-MEANS AND XGBOOST FOR CUSTOMER ELECTRICITY ACCOUNT PAYMENT BEHAVIOR ANALYSIS (CASE STUDY: PLN ULP PANAKKUKANG) Nugraha, Raditya Hari; Purwitasari, Diana; Raharjo, Agus Budi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1132

Abstract

Revenue Acceleration from electricity account receivables is one of the energy companies' efforts to maintain cash flow so that they can carry out operational activities and carry out investment activities to develop company assets. Factors that influence electricity bill payment behavior include the location of consumers, the amount of the bill, payment point facilities located around consumers' homes, the use of digital technology as a media of payment, as well as consumer awareness and understanding regarding the time limit for paying electricity bills. Therefore, it is necessary to conduct an analysis so that the company can determine a special strategy for customers who have the potential to be in arrears in electricity bills. To get the characteristic of electricity bill payments, several previous studies have used various classification methods of machine learning such as random forest, nave bayes, SVM, CART, etc. to get the best accuracy. In this research, to increase the accuracy of the model, author using the cluster method with the k-means technique and combining it with the eXtreme Gradient Boosting (XGBOOST) classification method based on data on the characteristics of consumer electricity bill payments. In this study also used hyperparameter adjustment with hillclimbing, random search, and bayesian techniques to increase the accuracy of the model. The model simulation carried out in this thesis gives the result that the combination of the k-means cluster with the XGBoost classification and by adjusting the bayesian technique hyperparameters has a much better model accuracy rate with a value of 89.27% and an Area Under Curve (AUC) value of 0.92 when compared to gradient boosting method with an accuracy rate of only 74.76% and an AUC value of 0.75. Based on the simulation results on ULP Panakkukang customer data, it was found that the subsidy category customer group and customers who often experience power outages have a tendency to be in arrears on electricity bills.
LOAD FORECASTING FOR DAILY LOAD OPERATIONAL PLAN USING LSTM (CASE STUDY: SOUTH SULAWESI SUB SYSTEM) Raharjo, Agus Budi; Wakhid, Muhammad Abdul; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1138

Abstract

The electrical load required in an electricity sub-system changes every day. Electric power operators must be able to generate and distribute electricity according to consumer needs. In the Sulawesi sub-system, the power plants used are still dominated by fossil fuel generators, so that in their operations, fuel requirements need to be given serious attention. Planning a good daily electricity consumption is needed so that the fuel cost becomes optimal. In the current condition, the load forecasting for the Daily Load Operation Plan (ROH) is still based on Expert Judgment, which is different for each forecaster. With a fairly large error tolerance limit of 4%. We need a load forecasting instrument capable of better error tolerance. Forecasting methods such as ARIMA, SARIMA and ARIMAX have been used for many years. In recent years, several artificial intelligence techniques such as Neural Network and machine learning have been developed for time series analysis. And recently, more accurate forecasting results are shown by Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) compared to traditional forecasting methods. Long Short Term Memory (LSTM) is a model of RNN that uses past data (Long Term) to predict current data (Short Term). Electric load in Sulawesi subsystem used as data training after normalized using min-max normalization. The LSTM model is made with different data input. Forecasting  performance of each model is then evaluated based on the RMSE and MAPE values. Of the several data input models, forecasting models with daily data input show better performance than other scenarios. The MAPE and RMSE values obtained were 2.384% and 33.95, respectively.
MULTI-DOCUMENT SUMMARIZATION USING A COMBINATION OF FEATURES BASED ON CENTROID AND KEYWORD Ranggianto, Narandha Arya; Purwitasari, Diana; Fatichah, Chastine; Sholikah, Rizka Wakhidatus
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 Abdillah, Abid Famasya Abdillah, Surya Abid Famasya Abdillah Achmad Affandi Ade Afrian Adhi Nurilham Adi Surya Suwardi Ansyah Adillion, Ilham Gurat Adni Navastara, Dini Agus Budi Raharjo Agus Budi Raharjo Agus Zainal Arifin Agus Zainal Arifin Ahmad Syauqi Ahmad Syauqi Aida Muflichah Akwila Feliciano Akwila Feliciano Alif Akbar Fitrawan, Alif Akbar Alqis Rausanfita Aminul Wahib Aminul Wahib Aminul Wahib Apriantoni Apriantoni Apriantoni, Apriantoni Ardianto Ardianto Ariadi Retno Tri Hayati Arief Rahman Arif Fadllullah Arini Rosyadi Ario Bagus Nugroho Arrie Kurniawardhani Arya Putra Kurniawan Asiyah Nur Kholifah Atikah, Luthfi Bambang Setiawan Baskoro Adi Pratomo Baskoro, Fajar Benito, Davian Budi Pangestu Budi Rahardjo Budi Raharjo, Agus Budiyono, Yanuardhi Arief Buliali, Joko Lianto Cahyaningtyas, Zakiya Azizah Chastine Fatichah Chilyatun Nisa, Chilyatun Christian Sri kusuma Aditya, Christian Sri kusuma Cornelius Bagus Purnama Putra Damayanti, Putri Daniel Oranova Siahaan Daniel Swanjaya Dasrit Debora Kamudi Dhian Kartika Dian Saputra Dini Adni Navastara, Dini Adni Dwi Sunaryono Dwi Sunaryono Edy Sukotjo Eko Riduwan Elshe Erviana Angely Erlinda Argyanti Nugraha Erlinda Argyanti Nugraha Esti Yuniar F.X. Arunanto Fahmi Amiq Fahrur Rozi Fajar Baskoro Fajar Baskoro Falach Asy'ari, Misbachul Fandy Kuncoro Adianto Fandy Kuncoro Adianto Faried Effendy Febri Fernanda Febriliyan Samopa Fransiscus Xaverius Arunanto Galih Hendra Wibowo Ginardi, Raden Venantius Hari Glory Intani Pusposari Gurat Adillion, Ilham Gus Nanang Syaifuddiin Hadziq Fabroyir Hafidz, Abdan Hamidi, Mohammad Zaenuddin Handayani Tjandrasa Haniefardy, Addien Hanif Affandi Hartanto Haykal, Muhammad Farhan Herdayanto Sulistyo Putro Hilya Tsaniya Hudan Studiawan Husna, Farida Amila I Ketut Eddy Purnama I Made Satria Bimantara Ilmi, Akhmad Bakhrul Imam Santosa Indra Lukmana Irdayanti, Marina Ivonne Soejitno Juanita, Safitri Juanita, Safitri Juli Purwanto Kardawi, Muhammad Yusuf Kautsar, Faiz Kevin Christian Hadinata Kevin Christian Hadinata Khadijah F. Hayati Kurnia Aji Tritamtama Lailatul Hidayah M. Abdillah M. Abdul Wakhid Mabahist, Fahril Maheswari, Clarissa Luna Mamluatul Hani’ah Mauridhi Hery Purnomo Mirza Hamdhani Misbakhul Munir Irfan Subakti Muhamad Nasir Muhammad Machmud Muhammad Mirza Muttaqi Nabila Puspita Firdi Nada Fitrieyatul Hikmah Nanik Suciati Narandha Arya Ranggianto Nova Rijati Novemi Uki A Novrindah Alvi Hasanah Nugraha, Raditya Hari Nur Azizah, Anisa Nur Hayatin Nurilham, Adhi Oktaviandra Pradita Putri Oktaviandra Pradita Putri, Oktaviandra Pradita Paramastri Ardiningrum Putu Praba Santika Putu Utami Andarini S. Putu Yuwono Kusmawan Raihan, Muhammad Rangga Kusuma Dinata Rangga Kusuma Dinata Ratih Nur Esti Anggraini, Ratih Nur Esti Rendra Dwi Lingga P. Resti Ludviani Rio Indralaksono Rizal Setya Perdana Rizka Sholikah Rizka Wakhidatus Sholikah Rizka Wakhidatus Sholikah, Rizka Wakhidatus Rizqa Afthoni Rozi, Fahrur RR. Ella Evrita Hestiandari Rully Soelaiman Rully Sulaiman Ryfial Azhar, Ryfial Safhira Maharani Safhira Maharani Safitri, Julia Salim Bin Usman Salim Bin Usman Salsabila Mazya Permataning Tyas Salsabila Salsabila Satrio Hadi Wijoyo Satrio Verdianto Satrio Verdianto Sembiring, Fred Erick Septiyan Andika Isanta Septiyan Andika Isanta Septiyawan Rosetya Wardhana Septiyawan Rosetya Wardhana Sherly Rosa Anggraeni Sherly Rosa Anggraeni Sidharta, Bayu Adjie Sihombing, Drigo Alexander Siti Rochimah Surya Sumpeno Suwida, Katon Syadza Anggraini Tanzilal Mustaqim Tegar Rachman Muzzammil Tesa Eranti Putri Tri Arief Sardjono Tsabbit Aqdami Mukhtar, Tsabbit Aqdami Umy Rizqi Verdianto, Satrio Victor Hariadi Vit Zuraida Wakhid, Muhammad Abdul Wardhana, Septiyawan R. Wardhana, Septiyawan Rosetya Wicaksono, Farhan Wijayanti Nurul Khotimah Wijoyo, Satrio Hadi Windy Deftia Mertiana Wisma Dwi Prastya, Ifnu Wulansari Wulansari Yasinta Romadhona Yatestha, Anak Agung Yoga Yustiawan Yonathan, Vincent Yos Nugroho Yudhi Purwananto Yufis Azhar Yuhana, Umi Laili Yulia Niza Yulia Niza Yulian Findawati Yunianto, Dika R. Zahrul Zizki Dinanto Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas