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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 Perfecting a Video Game with Game Metrics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor SCAN 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 Informatika Mulawarman 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 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
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 Social Sciences Other

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A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19 Diana Purwitasari; Adi Surya Suwardi Ansyah; Arya Putra Kurniawan; Asiyah Nur Kholifah
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.v7i2.4816

Abstract

As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.
Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19 Salsabila Salsabila; Salsabila Mazya Permataning Tyas; Yasinta Romadhona; Diana Purwitasari
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.84-94

Abstract

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately.   Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.
Sistem Pendaftaran Online untuk PPDB SMA/SMK Negeri Provinsi Jawa Timur Diana Purwitasari; Alqis Rausanfita; Hadziq Fabroyir
Sewagati Vol 4 No 2 (2020)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (427.514 KB)

Abstract

Penerimaan Peserta Didik Baru (PPDB) merupakan langkah awal dalam bidang pendidikan yang menjadi agenda rutin tiap tahunnya dengan dua mekanisme yaitu luar jaringan (offline) dan dalam jaringan (online). Namun pada tahun 2020, Indonesia bahkan dunia sedang ditimpa pandemi Covid-19, yang menyebabkan pemerintah provinsi Jawa Timur tidak bisa melaksanakan PPDB dengan mekanisme luar jaringan (offline). Oleh karena itu, kegiatan pengabdian masyarakat ini membangun sebuah sistem pendaftaran PPDB berbasis web yang dapat memfasilitasi tiga jenis tahapan pendaftaran PPDB jenjang SMA / SMK Negeri Jawa Timur 2020. Sebelum melakukan pendaftaran pada salah satu jalur calon peserta didik harus melaksanakan tahap pengambilan pin. Pengabdian ini mengadopsi konsep objek oriented programming (oop) dengan menggunakan framework code igniter. Sistem pendaftaran online untuk PPDB jenjang SMA/SMK Negeri Jawa Timur telah diuji dengan menggunakan teknik blackbox sehingga dapat dipastikan sistem telah berjalan dengan baik. Sebelum pendaftaran PPDB berlangsung pada tanggal 8 juni 2020 sampai dengan 27 juni 2020, tim informatika ITS telah melakukan sosialisasi sistem, namun tetap saja ketika kegiatan ini berlangsung terdapat beberapa kendala yang dialami calon peserta didik dalam menggunakan sistem. Untuk itu, tim informatika ITS melakukan pendampingan untuk mengatasi kendala-kendala yang terjadi selama berlangsungnya pendaftaran PPDB.
Pemanfaatan Platform Google Classroom untuk Pembelajaran Daring di Pondok Pesantren Miftahul Ulum Al-Islamy, Bangkalan, Madura Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Diana Purwitasari; Handayani Tjandrasa; Agus Zainal Arifin; Akwila Feliciano; Yulia Niza; Rangga Kusuma Dinata; Safhira Maharani; Ahmad Syauqi; Sherly Rosa Anggraeni; Fandy Kuncoro Adianto; Zakiya Azizah Cahyaningtyas; Salim Bin Usman; Kevin Christian Hadinata
Sewagati Vol 4 No 3 (2020)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.198 KB)

Abstract

Proses pembelajaran daring menjadi hambatan tersendiri dalam bidang pendidikan, terlebih untuk pendidikan wajib yang harus dilakukan secara bertatap muka langsung antara pengajar dan pelajar. Di luar faktor permasalahan eksternal, permasalahan internal perlu diselesaikan terlebih dahulu, yaitu media pembelajaran. Salah satu platform digital yang tersedia sebagai media pembelajaran untuk menunjang pembelajaran secara daring adalah Google Classroom. Aplikasi Google Classroom berbasis web yang berbentuk pembelajaran asynchronous atau dapat dikatakan pemberian materi ajar dilakukan secara tidak langsung. Walaupun sebuah media daring sudah tersedia, masih ada yang belum mengenal atau memahami penggunaan aplikasi Google Classroom sebagai media ajar mereka. Oleh karena itu, kami mengadakan pengabdian masyarakat berupa pelatihan tentang penggunaan aplikasi Google Classroom bagi guru-guru di Pondok Pesantren Miftahul Ulum Al-Islamy, yang berada di Bangkalan, Madura. Selain itu, tim pengabdi juga melakukan pendampingan bagi guru-guru dalam mempraktikkan penggunaan Google Classroom sesuai dengan mata pelajaran yang diajar. Berdasarkan hasil survei, sebanyak 91% dari total peserta pelatihan menyebutkan bahwa pelatihan ini dapat meningkatkan pengetahuan dan kemampuan secara softskill dan hardskill para guru.
Pendampingan Modul Pengumpulan dan Pelaporan Data pada Aplikasi Penelusuran COVID-19 untuk Dinas Kesehatan Jawa Timur Agus Budi Raharjo; Erlinda Argyanti Nugraha; Fransiscus Xaverius Arunanto; Dwi Sunaryono; Fajar Baskoro; Diana Purwitasari; Misbakhul Munir Irfan Subakti
Sewagati Vol 5 No 1 (2021)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1149.283 KB)

Abstract

Provinsi Jawa Timur (Jatim) adalah wilayah terpadat kedua di Indonesia dengan sekitar 39 juta penduduk tersebar di 38 kota. Selain Jakarta sebagai daerah terbanyak dengan pasien COVID-19, Jatim merupakan salah satu provinsi dengan pasien terkonfirmasi terbanyak. Dengan kondisi tersebut, penelusuran dan prediksi pasien menjadi hal vital yang dapat membantu pemerintah provinsi dalam mempelajari pola penyebaran sehingga mampu memberikan landasan dalam mengambil keputusan. Saat ini ITS khususnya Fakultas Teknologi Elektro dan Informatika Cerdas (FTEIC) sudah berpartisipasi aktif dalam mendampingi pembangunan sistem pengelolaan data lengkap pasien terkonfirmasi. Sistem yang dibangun tersebut diaplikasikan di wilayah Jatim, di mana ITS menjadi mitra Dinas Komunikasi dan Informasi (Diskominfo) dan Dinas Kesehatan (Dinkes) Jatim. Meskipun saat ini sistem pengelolaan data sudah dibangun, namun fitur penelusuran dan prediksi masih belum bisa dioptimalkan karena terkendala tenaga ahli. Oleh karena itu, departemen Informatika ITS khususnya laboratorium Algoritma dan Pemrograman (AP) menawarkan untuk melanjutkan pendampingan dengan Diskominfo dan Dinkes Jatim dalam pengumpulan dan pelaporan data guna menunjang fitur penelusuran dan prediksi tersebut. Dengan adanya pengabdian ini, diharapkan dapat mengoptimalkan sistem yang sudah dibangun sebelumnya dan dapat diadaptasi agar bisa mengelola data pandemi di masa mendatang sebagai bentuk kontribusi mendukung usaha pemerintah dalam menangani COVID-19.
PlasmaHub: Aplikasi Donor Plasma Konvalesen Berbasis Web Pengolah Informasi guna Memudahkan Pemetaan Pendonoran di Jawa Timur Agus Budi Raharjo; Diana Purwitasari; Elshe Erviana Angely; Herdayanto Sulistyo Putro; Edy Sukotjo; Imam Santosa; Ivonne Soejitno; Juli Purwanto
Sewagati Vol 7 No 2 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1863.508 KB) | DOI: 10.12962/j26139960.v7i2.456

Abstract

Pandemi COVID-19 telah memicu krisis kesehatan global. Sampai dengan saat ini, belum ada pilihan yang terbukti untuk pemulihan bagi masyarakat yang terkonfirmasi positif. Salah satu strategi jangka pendek untuk membantu pemulihan pasien terkonfirmasi adalah melalui terapi plasma konvalesen. Plasma konvalesen yang didapat dari individu penyintas COVID-19 mampu digunakan untuk pemeliharaan kesehatan pasca terpapar atau mempercepat penyembuhan penyakit menular. Saat ini, jumlah penyintas yang banyak di Indonesia tidak sebanding dengan masyarakat yang telah melakukan donor plasma konvalesen. Hal tersebut mengakibatkan kebutuhan plasma yang tinggi dengan ketersediaan pendonor yang rendah. Pasien harus menyebarkan data pribadi mereka melalui media sosial untuk mendapatkan golongan darah yang cocok. Tim Satuan Tugas COVID-19 ITS bekerja sama dengan ikatan alumni penyintas COVID-19 RS Indrapura berpartisipasi aktif dalam membangun aplikasi berbasis web PlasmaHub, untuk membantu menjembatani antara pemohon dan pendonor plasma konvalesen secara anonim, sehingga meminimalisir tersebarnya data pribadi. Sosialisasi penggunaan aplikasi untuk meningkatkan peminat donor plasma konvalesen dilakukan dengan peresmian oleh rektor ITS, konferensi pers, dan webinar. Saat ini, aktivitas sosialisasi sudah dipublikasi ke lebih dari dua puluh media massa nasional. Aplikasi yang diusulkan diharapkan dapat membantu PMI dalam meningkatkan stok plasma konvalesen, mempermudah masyarakat untuk mendapat donor plasma, dan turut serta melindungi keamanan data pribadi pasien.
A Clustering Approach for Mapping Dengue Contingency Plan Husna, Farida Amila; Purwitasari, Diana; Sidharta, Bayu Adjie; Sihombing, Drigo Alexander; Fahmi, Amiq; Purnomo, Mauridhi Hery
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.36885

Abstract

Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.Methods: We used clustering with silhouette scoring to select features, the best cluster formation, the best clustering method, and cluster severity. Cluster severity is carried out by levelling the attributes of the average value to low, medium, high, and extreme, which are related to the plans each region sets for village type and season type.Result: In five years of data (2016-2020) ±15K cases from Semarang City, Indonesia, feature selection results show that environmental and demography group features have the biggest silhouette score. With these features, it is found that K-Means has a high silhouette score compared to DBSCAN and agglomerative with three optimum numbers of clusters. K-Means also successfully mapped the cluster severity and assigned the cluster to a suitable contingency policy.Novelty: Most of the research on DHF cases is about predicting DHF cases and measuring the risk of DHF occurrence. There are not many studies that discuss the policy recommendations for dengue control.
Aspect Based Sentiment Analysis of Product Review Using Memory Network Ismet, Hilya Tsaniya; Mustaqim, Tanzilal; Purwitasari, Diana
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.34094

Abstract

Abstract. Purpose: Consumer opinion is one of the essential keys that affect the success of a product. Sentiment analysis of consumer opinion is needed to find out information about customer satisfaction for companies in the decision-making process. The traditional sentiment analysis process extracts a complete sentiment from a single sentence. However, it does not consist of only one sentiment in one sentence. The total number depends on the number of aspects that make up the sentence. Therefore, a sentiment analysis process is needed to pay attention to aspects.Methods: This research focuses on product reviews from Indonesian e-commerce on several aspects of sentiment. Uses fastText word embedding to avoid Out of Vocabulary in datasets and Gated Recurrent Units for aspect spread detection. Sentiment classification on aspects using the Memory Network method.Result: The experiment results showed that aspect-based sentiment classification predictions had an accuracy of 83% compared to 78% overall classification predictions for review texts, indicating that aspect-based sentiment analysis can improve model performance on product review classification predictions.Novelty: Most product reviews analysis use document-level classification to extract and predict sentiment reviews, aspect-based analysis can be applied to product reviews for better sentiment understanding, using Memory Network to store important information explicitly on aspects and polarity.
Deep Learning Approaches for Automatic Drum Transcription Zakiya Azizah Cahyaningtyas; Diana Purwitasari; Chastine Fatichah
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 Katon Suwida; Muhammad Yusuf Kardawi; Diana Purwitasari; Fahril Mabahist
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%.
Co-Authors Abid Famasya Abdillah Abid Famasya Abdillah Achmad Affandi Addien Haniefardy Ade Afrian Adhi Nurilham Adi Surya Suwardi Ansyah 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 Alqis Rausanfita Aminul Wahib Aminul Wahib Aminul Wahib Anggraini, Ratih Nur Esti 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 Bambang Setiawan Baskoro, Fajar Budi Pangestu Budi Rahardjo Buliali, Joko Lianto Chastine Fatichah Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Cornelius Bagus Purnama Putra 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 Fahril Mabahist Fahrur Rozi Fajar Baskoro Fandy Kuncoro Adianto Fandy Kuncoro Adianto Faried Effendy Febri Fernanda Febriliyan Samopa Fransiscus Xaverius Arunanto Galih Hendra Wibowo Ghozali, Imam Ginardi, Raden Venantius Hari Glory Intani Pusposari Gus Nanang Syaifuddiin Hadziq Fabroyir Handayani Tjandrasa Hani’ah, Mamluatul Hani’ah, Mamluatul Hanif Affandi Hartanto Hasanah, Novrindah Alvi Herdayanto Sulistyo Putro Husna, Farida Amila Ilmi, Akhmad Bakhrul Imam Santosa Indra Lukmana Ismet, Hilya Tsaniya Ivonne Soejitno Juanita, Safitri Juli Purwanto Katon Suwida Kevin Christian Hadinata Kevin Christian Hadinata Khadijah F. Hayati Kurnia Aji Tritamtama Lailatul Hidayah Luthfi Atikah M. Abdillah M. Abdul Wakhid Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mirza Hamdhani Misbakhul Munir Irfan Subakti Mohammad Zaenuddin Hamidi Muhamad Nasir Muhammad Machmud Muhammad Mirza Muttaqi Muhammad Yusuf Kardawi Mustaqim, Tanzilal Nabila Puspita Firdi Nada Fitrieyatul Hikmah Nanik Suciati Narandha Arya Ranggianto Narandha Arya Ranggianto Nova Rijati Novemi Uki A Novrindah Alvi Hasanah Novrindah Alvi Hasanah Nugraha, Raditya Hari Nur Hayatin Nurilham, Adhi Oktaviandra Pradita Putri Oktaviandra Pradita Putri, Oktaviandra Pradita Paramastri Ardiningrum Putri Damayanti Putu Praba Santika Putu Utami Andarini S. Putu Yuwono Kusmawan Rangga Kusuma Dinata Rangga Kusuma Dinata Rendra Dwi Lingga P. Resti Ludviani Rio Indralaksono Rizal Setya Perdana Rizka Sholikah Rizka Wakhidatus Sholikah Rizqa Afthoni Rozi, Fahrur Rully Soelaiman Rully Sulaiman Ryfial Azhar, Ryfial Safhira Maharani Safhira Maharani Safitri Juanita Safitri, Julia Salim Bin Usman Salim Bin Usman Salsabila Mazya Permataning Tyas Salsabila Salsabila Satrio Hadi Wijoyo Satrio Verdianto Satrio Verdianto Septiyan Andika Isanta Septiyan Andika Isanta Septiyawan Rosetya Wardhana Septiyawan Rosetya Wardhana Sherly Rosa Anggraeni Sherly Rosa Anggraeni Sherly Rosa Anggraeni Sherly Rosa Anggraeni Sidharta, Bayu Adjie Sihombing, Drigo Alexander Siti Rochimah Surya Sumpeno Syadza Anggraini 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 Rosetya Wijayanti Nurul Khotimah Wijoyo, Satrio Hadi Windy Deftia Mertiana wulansari wulansari Yanuardhi Arief Budiyono Yasinta Romadhona Yoga Yustiawan Yos Nugroho Yudhi Purwananto Yufis Azhar Yuhana, Umi Laili Yulia Niza Yulia Niza Zahrul Zizki Dinanto Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas Zuraida, Vit