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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

Author-ID : 350177

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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Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Ranggianto, Narandha Arya; Purwitasari, Diana; Fatichah, Chastine; Sholikah, Rizka Wakhidatus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5170

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.

Graph-Structured Network Traffic Modelling for Anomaly-Based Intrusion Detection Pratomo, Baskoro Adi; Haykal, Muhammad Farhan; Studiawan, Hudan; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.94959

The increasing complexity of cyber threats demands more advanced network intrusion detection systems (NIDS) capable of identifying both known and emerging attack patterns. In this study, we propose a graph-based anomaly detection approach for network intrusion detection, where network traffic is modeled as graph structures capturing both attribute and topological information. Five graph anomaly detection models—DOMINANT, OCGNN, AnomalyDAE, GAE, and CONAD—are implemented and evaluated on the UNSW-NB15 dataset. The constructed graphs use info_message attributes as nodes, with edges representing sequential traffic relationships. Experimental results show that the Graph Autoencoder (GAE) and Dual Autoencoder (AnomalyDAE) models outperform other methods, achieving F1-scores of 0.8728 and 0.7939, respectively. These findings demonstrate that reconstruction-based approaches effectively capture complex network behaviors, highlighting the potential of graph-based methods to enhance the robustness and accuracy of modern NIDS. Future work will explore dynamic graph modeling, attention mechanisms, and optimization techniques to further improve detection capabilities.

Transfer Learning Menggunakan LoRA+ pada Llama 3.2 untuk Percakapan Bahasa Indonesia Kautsar, Faiz; Wicaksono, Farhan; Hafidz, Abdan; Purwitasari, Diana; Suciati, Nanik; Adni Navastara, Dini; Gurat Adillion, Ilham
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12508

Penelitian ini mengeksplorasi penerapan dari metode Parameter-Efficient Finetuning (PEFT) Low-Rank Adaptation+ (LoRA+) pada transfer learning model Llama 3.2 1B, sebuah model bahasa besar. Seiring bertambahnya ukuran model bahasa, finetuning yang dilakukan secara konvensional dalam transfer learning semakin tidak fisibel untuk dilakukan tanpa menggunakan komputasi skala besar. Untuk menangani hal tersebut, dapat dilakukan finetuning pada beberapa komponen saja, menggunakan komputasi yang relatif minimal berbanding dengan finetuning konvensional, metode-metode yang menerapkan prinsip ini disebut juga sebagai PEFT. Penelitian menguji efektifitas metode PEFT, yakni LoRA+, pada transfer learning model bahasa besar terhadap domain baru, yakni bahasa Indonesia, menggunakan metrik BLEU, ROUGE, serta Weighted F1. Hasil penelitian menunjukkan bahwa penerapan LoRA+ menghasilkan performa kompetitif dan unggul terhadap baseline dalam kemampuan berbahasa Indonesia, dengan peningkatan 112% pada skor BLEU dan 21.7% pada skor ROUGE-L, dengan standar deviasi yang relatif rendah sebesar 3.72 dan 0.00075. Meskipun terjadi penurunan pada skor Weighted F1 sebesar 13% yang disebabkan oleh domain shift, model menunjukkan kemampuan transfer lintas-bahasa yang baik. Kata kunci: Finetuning, Model Bahasa Besar, Parameter-Efficient Finetuning, Low-Rank Adaptation, Transfer Learning

Document Matching for Contradiction Detection in Low-Resource Legislative Texts With Self-Training and Augmentation Using Transformer Model Navastara, Dini Adni; Abdillah, Surya; Benito, Davian; Adillion, Ilham Gurat; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.95954

Detecting contradictions within low-resource legislative texts presents significant challenges due to limited labeled data, complex legal language, and the vast number of verses contained within legal documents. These contradictions can lead to legal ambiguities and disputes if not addressed effectively. To tackle this problem, this study proposes a comprehensive system that combines document matching with contradiction detection. Legal documents are first clustered based on contextual similarity, enabling a more targeted analysis of potentially contradictory verses. Among several clustering approaches tested, keyword similarity-based clustering using KeyBERT produced the highest MatchingScore of 0.6111. To overcome the scarcity of labeled data, we employed a multi-step strategy involving manual annotation, generative AI-based data augmentation, and self-training techniques. The contradiction detection model was developed using the XLM-RoBERTa architecture, trained on TPU V2 with a batch size of 64. The model achieved strong performance, with 0.978 recall, 0.9356 precision, 0.982 accuracy, and a 0.9566 F1-score, completing each epoch in 82 seconds. This integrated approach significantly reduces the complexity of contradiction detection in legislative documents while ensuring high accuracy and robustness.

Multi-Label Classification of Bilingual Doctor Responses in Online Medical Consultations Using Deep Learning Juanita, Safitri; Purwitasari, Diana; Purnama, I Ketut Eddy; Raihan, Muhammad; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.96980

Online health consultations (OHCs) have become an integral component of modern healthcare delivery. However, significant challenges remain in multilingual and low-resource contexts such as Indonesia, where language barriers and digital disparities hinder effective doctor–patient communication. Ensuring the quality of such interactions requires the identification of six key communicative functions: building relationships, gathering and providing information, decision-making, promoting disease- and treatment-related behaviour, and responding to emotions. While existing research has largely focused on English-language OHCs, studies analysing these communicative functions in Indonesian remain limited due to the lack of annotated datasets and linguistic complexity. To address this gap, we propose a deep learning framework for multi-label classification of communicative functions in bilingual (Indonesian/English) doctor response texts. The dataset used in this study was annotated by medical professionals with six predefined communicative function labels. We conducted a comprehensive comparative evaluation of three deep learning architectures namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Networks (CNN) equipped with cross-language word embedding to improve multilingual generalization. Model performance is evaluated through four complementary perspectives: example-based, label-based, ranking-based, and multifaceted metrics, ensuring a holistic assessment. Result show that the fine-tuned LSTM model achieved the highest precision (0.972) on Indonesian texts, while Bi-LSTM obtained the best results on English texts with 0.890 accuracy and 0.980 precision. The LSTM model also reduced false positives in Indonesian classifications, whereas Bi-LSTM improved diagnostic reliability in English, confirming the models’ cross-lingual adaptability. These findings highlight the potential of deep learning to improve communication effectiveness in bilingual and resource-constrained OHC settings.

Multi-label Aspect Dangerous Speech Classification Using Keyword-Driven Ensemble Classifier on Imbalanced Data Findawati, Yulian; Budi Raharjo, Agus; Adni Navastara, Dini; Yonathan, Vincent; Yatestha, Anak Agung; Purwitasari, Diana
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3129

This study aims to detect various aspects of dangerous speech on social media, particularly Twitter, which has the potential to incite violence and increase prejudice against specific communities. The research dataset includes tweets containing dangerous speech related to the Indonesian government from 2019 to 2022. Researchers manually labeled the data based on seven aspects of hazardous speech, including social and historical context, dehumanization, accusations in the mirror, threats against women/children, questioning in-group loyalty, and threats against groups. The study employs a multi-label classification method to handle these aspects, which appear simultaneously in a single text. The main challenges include data imbalance, ambiguity, and the informal language frequently appearing in tweets. This study introduces a Keyword-Driven Ensemble Classifier (KDEC), a new ensemble model that leverages the strengths of SVC, Logistic Regression, IndoBERTweet, and specific keyword lists for each label. Researchers designed KDEC based on the best results from machine learning and deep learning methods tested in this study. The research team tested the model on small and large datasets, conducting trials involving seven and four-label classifications. The results show that KDEC, with label reduction and keyword support, effectively addresses data imbalance, resolves label overlap, and achieves 92% accuracy for seven-label classification and 88% for four-label classification. The findings of this research are highly relevant for hate speech analysis across various platforms and languages, particularly in understanding context and conveyed messages. Additionally, this study provides valuable insights into managing harmful content in online government-related discussions. This method identifies dangerous speech on a larger scale and supports data-driven social media content regulation decision-making.

Analisis Data Penggunaan Block Storage Untuk Rekomendasi Penyeimbangan Beban Kerja Aplikasi Telekomunikasi Menggunakan Klasterisasi Sembiring, Fred Erick; Purwitasari, Diana
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.767

Perkembangan teknologi informasi yang pesat mendorong peningkatan kebutuhan akan sistem penyimpanan data yang andal dan efisien, khususnya pada sektor industri telekomunikasi. PT XYZ sebagai salah satu perusahaan telekomunikasi terbesar di Indonesia menghadapi tantangan terkait variasi beban kerja pada 32 server block storage yang mereka miliki. Perbedaan performa server akibat variasi metrik seperti IOPS, service time, dan bandwidth berpotensi menyebabkan ketidakseimbangan beban serta penurunan efisiensi infrastruktur TI. Penelitian ini bertujuan untuk menganalisis dan mengkategorikan beban kerja server block storage di PT XYZ menggunakan dua metode klasterisasi populer, yaitu K-Means dan DBSCAN. Metode penelitian yang digunakan meliputi pengumpulan data performa server, proses preprocessing data, penerapan algoritma clustering, serta evaluasi hasil klasterisasi menggunakan ground truth sebagai acuan validasi. Hasil penelitian menunjukkan bahwa kedua metode mampu mengelompokkan server ke dalam tingkatan beban kerja, namun DBSCAN terbukti lebih akurat dengan tingkat akurasi mencapai 87,72%, dibandingkan K-Means yang hanya mencapai 23,60%. Selain itu, DBSCAN juga efektif dalam mengidentifikasi server dengan pola beban kerja anomali sebagai noise, yang tidak terdeteksi oleh K-Means. Kesimpulan dari penelitian ini adalah bahwa metode DBSCAN lebih direkomendasikan untuk analisis beban kerja server block storage di PT XYZ guna mendukung strategi penyeimbangan beban kerja dan optimalisasi penggunaan sumber daya TI secara lebih efisien

Pelabelan Klaster Fitur Secara Otomatis pada Perbandingan Review Produk Rozi, Fahrur; Wijoyo, Satrio Hadi; Isanta, Septiyan Andika; Azhar, Yufis; Purwitasari, Diana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1 No 2: Oktober 2014
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Abstrak Penggunaan review produk sebagai suatu sumber untuk mendapatkan informasi dapat dimanfaatkan untuk mengoptimalkan pemasaran suatu produk. Situs belanja online merupakan salah satu sumber yang dapat digunakan untuk pengambilan review produk. Analisa terhadap produk dapat dilakukan dengan membandingkan antara dua buah produk berbeda berdasarkan fitur produk tersebut. Fitur dari suatu produk didapatkan melalui ekstraksi fitur dengan metode double propagation. Fitur yang terdapat dalam sebuah review sangat banyak serta terdapat beberapa kata yang memiliki arti yang sama yang mewakili suatu fitur tertentu, sehingga diperlukan suatu pengelompokan terhadap fitur tersebut. Pengelompokan suatu fitur produk dapat dilakukan secara otomatis tanpa memperhatikan kamus kata, yaitu dengan menggunakan teknik clustering. Hierarchical clustering merupakan salah satu metode yang dapat digunakan untuk pengelompokan terhadap fitur produk. Pengujian dengan metode hierarchical clustering untuk pengelompokan fitur menunjukkan bahwa metode average linkage memiliki nilai recall dan f-measure yang paling tinggi. Sementara untuk pengujian pelabelan menunjukkan bahwa semantic similarity antar fitur lebih berpengaruh dari pada kemunculan fitur di dokumen. Kata kunci: clustering, fitur produk, pelabelan Abstract Product review can be used as a source for acquire information and to optimize the marketing of product. Online shopping sites are one of source that can be used to get product reviews. Analysis of the product can be done by comparing two different products based on product’s features. Features of a product can be obtained through extraction of features with double propagation method. In the product review there are many feature that can be found, and there are some words that have the same meaning which represents a particular feature, so we need a grouping on the feature. Hierarchical clustering is one method that can be used for grouping the features of the product. Based on testing, hierarchical clustering method for grouping feature indicate that the average linkage method has the highest recall and f-measure. As for testing in labeling indicates that the semantic similarity between features is more influential than the appearance of features in the document. Keywords: clustering, features of the product, labeling

Pembobotan Kata Berbasis Preferensi Dan Hubungan Semantik Pada Dokumen Fiqih Berbahasa Arab Wardhana, Septiyawan R.; Yunianto, Dika R.; Arifin, Agus Zainal; Purwitasari, Diana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2 No 2: Oktober 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakDalam proses pencarian dokumen, pengguna sering menginginkan hasil pencarian yang sesuai dengan preferensi yang diinginkannya. Maka, untuk memperoleh hasil pencarian yang sesuai dengan preferensi tersebut dibutuhkan suatu metode pembobotan kata yang didasarkan pada preferensi tersebut. Metode pembobotan tersebut perlu mempertimbangkan hubungan semantik antar kata untuk meningkatkan relevansi hasil pencarian. Dalam penelitian ini diusulkan metode pembobotan kata berbasis preferensi berdasarkan hubungan semantik antar kata pada dokumen fiqih berbahasa Arab. Latent Semantic Indexing merupakan salah satu metode indexing dalam sistem temu kembali informasi yang mempertimbangkan hubungan semantik antar kata. Hasil pembobotan kata berdasarkan preferensi dijadikan sebuah matriks untuk perhitungan Latent Semantic Indexing yang menghasilkan sebuah vektor. Vektor tersebut dihitung similaritasnya antara vektor query dengan vektor-vektor dokumen yang ada. Metode pembobotan kata berbasis preferensi yang mempertimbangkan hubungan semantik antar kata dapat digunakan dalam perankingan dokumen fiqih bahasa Arab berbasis preferensi. Hal tersebut dapat dilihat dari nilai maksimal precision, recall dan f-measure yang meningkat menjadi 88.75 %, 89.72% dan 87.91%.Kata kunci: Bahasa Arab, Latent Semantic Indexing, Pembobotan Kata, PreferensiAbstractIn the document search process is not uncommon users want search results that correspond to the desired preferences. Thus, to obtain the search results according to user preferences needed a word weighting method based on user preference. The term weighting method needs to consider the semantic relationships between words to improve the relevance of search results. This paper propose a new method of term weighting based preference by considering the semantic relationships between term in documents fiqh Arabic. Latent Semantic Indexing is a method of indexing in information retrieval system that takes the semantic relationships between words. Term weighting results based on preferences made a matrix for calculation of Latent Semantic Indexing which generate a vector for the calculated similarity between the query vector of vectors documents. Term weighting based preference by considering the semantic relationships between term method can be used on the rank documents fiqh Arabic. It can be seen from the value of the precision, recall, and F-measure which increase to 88.75 %, 89.72 % and 87.91 %.Keywords: : Arabic, Latent Semantic Indexing, Term Weighting, Preference

Eliminasi Non-Topic Menggunakan Pemodelan Topik untuk Peringkasan Otomatis Data Tweet dengan Konteks Covid-19 Damayanti, Putri; Purwitasari, Diana; Suciati, Nanik
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814324

Akun twitter, seperti Suara Surabaya, dapat membantu menyebarkan informasi tentang COVID-19 meskipun ada bahasan lainnya seperti kecelakaan, kemacetan atau topik lain. Peringkasan teks dapat diimplementasikan pada kasus pembacaan data twitter karena banyaknya jumlah tweet yang tersedia, sehingga akan mempermudah dalam memperoleh informasi penting terkini terkait COVID-19. Jumlah variasi bahasan pada teks tweet mengakibatkan hasil ringkasan yang kurang baik. Oleh karena itu dibutuhkan adanya eliminasi tweet yang tidak berkaitan dengan konteks sebelum dilakukan peringkasan. Kontribusi penelitian ini adalah adanya metode pemodelan topik sebagai bagian tahapan dalam serangkaian proses eliminasi data. Metode pemodelan topik sebagai salah satu teknik eliminasi data dapat digunakan dalam berbagai kasus namun pada penelitian ini difokuskan pada COVID-19. Tujuannya adalah untuk mempermudah masyarakat memperoleh informasi terkini secara ringkas. Tahapan yang dilakukan adalah pra-pemrosesan, eliminasi data menggunakan pemodelan topik dan peringkasan otomatis. Penelitian ini menggunakan kombinasi beberapa metode word embedding, pemodelan topik dan peringkasan otomatis sebagai pembanding. Ringkasan diuji menggunakan metode ROUGE dari setiap kombinasi untuk ditemukan kombinasi terbaik dari penelitian ini. Hasil pengujian menunjukkan kombinasi metode Word2Vec, LSI dan TextRank memiliki nilai ROUGE terbaik yaitu 0.67. Sedangkan kombinasi metode TFIDF, LDA dan Okapi BM25 memiliki nilai ROUGE terendah yaitu 0.35. AbstractTwitter accounts, such as Suara Surabaya, can help spread information about COVID-19 even though there are other topics such as accidents, traffic jams or other topics. Text summarization can be implemented in the case of reading Twitter data because of the large number of tweets available, making it easier to obtain the latest important information related to COVID-19. The number of discussion variations in the tweet text results in poor summary results. Therefore, it is necessary to eliminate tweets that are not related to the context before summarization is carried out. The contribution to this research is the topic modeling method as part of a series of data elimination processes. The topic modeling method as a data elimination technique can be used in various cases, but this research focuses on COVID-19. The aim is to make it easier for the public to obtain current information in a concise manner. The steps taken in this study were pre-processing, data elimination using topic modeling and automatic summarization. This study uses a combination of several word embedding methods, topic modeling and automatic summarization as a comparison. The summary is tested using the ROUGE method of each combination to find the best combination of this study. The test results show that the combination of Word2Vec, LSI and TextRank methods has the best ROUGE value, 0.67. While the combination of TFIDF, LDA and Okapi BM25 methods has the lowest ROUGE value, 0.35.

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

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