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All Journal Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Sistemasi: Jurnal Sistem Informasi JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Kridatama Sains dan Teknologi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jifosi Nusantara Science and Technology Proceedings Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmiah Wahana Pendidikan Computer Science (CO-SCIENCE) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sistem Informasi dan Bisnis Cerdas Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Innovative: Journal Of Social Science Research Jurnal Pengabdian West Science ILTEK : Jurnal Teknologi Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Ilmiah Sistem Informasi Journal of Computer Science and Information Technology Router : Jurnal Teknik Informatika dan Terapan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Router : Jurnal Teknik Informatika dan Terapan Journal of Golden Generation Multidisciplinary Jurnal Nasional Teknologi Informasi dan Aplikasinya Jurnal Publikasi Ilmu Komputer dan Multimedia
Wahyuni, Eka Dyar
Information System Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
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Pengamanan Data Akademik Berbasis Web dengan Enkripsi AES-256 (Studi Kasus pada Pendataan Digital SMA XYZ) Zahrah Hayat Arka Putri; Yessy Arye Yustraini; Ramdhan Ariansyah; Najma Choirun Nisa; Eka Dyar Wahyuni; Agung Brastama Putra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i03.p26

Abstract

The advancement of information technology has significantly facilitated data management, particularly in the field of education. However, this convenience also presents new challenges in ensuring data security, especially for academic information that is sensitive and vulnerable to unauthorized access. This study aims to develop a web-based academic data security system by implementing the AES-256 encryption algorithm in Cipher Block Chaining (CBC) mode. The system is built using the PHP programming language and MySQL database to manage student data such as name, NISN, class, address, gender, religion, and mother’s name. The core functionality lies in encrypting and decrypting sensitive information using the openssl_encrypt and openssl_decrypt functions, integrated with a 256-bit encryption key and a randomly generated Initialization Vector (IV) to ensure confidentiality. The encrypted data is stored in base64 format to maintain compatibility with relational databases and storage systems. Testing was conducted to evaluate the accuracy of the encryption-decryption process and to assess the impact on system performance. Results show that the system effectively secures sensitive data; the encrypted entries in the database are unreadable to unauthorized users and can only be restored using the correct encryption key and IV. With a simple yet functional user interface and automated encryption handling, the system proves to enhance academic data security without compromising operational efficiency. These findings demonstrate that the implementation of AES-256-CBC encryption can be effectively applied in educational information systems, offering a practical and reliable solution for safeguarding academic data in web-based environments.
Application of NLP and Rasa for Intent Classification in Durga Historical Texts Dea Puspita; Eka Dyar Wahyuni; Tri Lathif Mardi Suryanto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2887

Abstract

This research aims to develop a chatbot based on Natural Language Processing (NLP) using the Rasa framework and the DIETClassifier model to classify user intent from historical data about Dewi Durga within the Indonesian cultural context. Dewi Durga’s stories play a significant role in preserving Indonesian heritage, especially as younger generations increasingly disengage from traditional knowledge. This study highlights the importance of digital preservation as a strategy to keep these narratives accessible and relevant. The chatbot is designed to provide interactive, educational conversations about Durga mythology, helping users understand Hindu cultural values through an intuitive and accessible digital platform. The research methodology involves several key stages: data cleaning and preparation, intent labeling, splitting data into training and testing sets, and evaluating model performance using a Confusion Matrix. Metrics such as accuracy, precision, recall, and F1-score are used to assess classification performance. The DIETClassifier model achieved strong results, with an accuracy of 0.94, precision of 0.90, recall of 0.93, and an F1-score of 0.91, indicating high effectiveness in intent classification. Following model training and evaluation, the chatbot was deployed using Flask, allowing real-time user interaction through a responsive web interface. This project contributes to the use of NLP-based chatbot technology in cultural preservation, specifically focusing on Dewi Durga’s mythology in Javanese and Balinese traditions. By digitizing these stories, the study aims to prevent cultural erosion and promote broader engagement with heritage. The approach may also be applied to other domains that require deep cultural understanding.
Sistem Rekomendasi Paket Menu Menggunakan Algoritma FP Growth di Teré Café and Bar Seminyak Hukama’ Nur Romadlon; Eka Dyar Wahyuni; Nur Cahyo Wibowo
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4404

Abstract

The rapid growth of the food and beverage industry encourages business actors to have innovative sales strategies to increase their sales. This thesis focuses on TERÉ café and Bar Seminyak, which has not utilized its sales transaction data optimally. The main purpose of the preparation is to identify customer purchasing patterns and formulate recommendations for food and beverage menu packages that can increase sales. This thesis uses data mining techniques with Association Rules and the FP-Growth algorithm to analyze sales transaction data at TERÉ café and Bar Seminyak based on customer preferences in five different time sessions. The data used is sales data from July 1, 2023 to June 30, 2024 and the framework used is CRISP-DM. The results of the analysis show that there is a strong combination between “Octopus” and “Burger” in the opening session, a strong combination between “Baked Egg” and “Avocado Toast” or “Tere Toast” in the lunch session, and in the next three sessions there is a strong combination between “Bintang (PACKAGE)” and “B2G3 BINTANG”. These results were obtained from the min support parameters of 0.01, confidence of 0.1 and lift of 2.
Analisis Sentimen Multi-Aspek pada Ulasan Aplikasi MySiloam Menggunakan Pipeline BERTopic dengan Perbandingan Algoritma Clustering Jihan Hasna Iftinan; Eka Dyar Wahyuni; Reisa Permatasari
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 18 No 1 (2026): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v18i1.556

Abstract

Tingginya volume ulasan pengguna aplikasi kesehatan digital belum dimanfaatkan secara optimal untuk memahami aspek spesifik yang memengaruhi pengalaman pengguna. Penelitian ini bertujuan menganalisis sentimen berbasis multi-aspek pada ulasan aplikasi MySiloam menggunakan metode BERTopic untuk ekstraksi aspek dan SVM One-vs-One untuk klasifikasi sentimen. Sebanyak 2.657 ulasan dikumpulkan dari Google Play Store dan App Store rentang 2019–2025, disaring menjadi 1.699 ulasan setelah preprocessing. BERTopic dijalankan dengan perbandingan tiga algoritma clustering (HDBSCAN, BIRCH, K-Means) dan klasifikasi sentimen dibandingkan dalam dua skenario yaitu pendekatan dua tahap dan klasifikasi gabungan. K-Means dengan stemming menghasilkan tiga aspek layanan utama dengan kualitas topik terbaik, sementara pendekatan dua tahap menghasilkan F1-score tertinggi 89,53%, membuktikan bahwa kombinasi BERTopic dan SVM OvO efektif sebagai solusi otomatis analisis sentimen berbasis aspek pada ulasan aplikasi kesehatan digital berbahasa Indonesia.
Klasifikasi Multi-Label Dan Ekstraksi Entitas Pada Ulasan Aplikasi Blu by BCA Digital Menggunakan IndoBERT Bhagas Satrya Dewa; Eka Dyar Wahyuni; Nur Cahyo Wibowo
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.402

Abstract

The growth of digital banking services in Indonesia has heightened the need to understand factors influencing users' application continuance intention. However, prior studies remain limited to single-label classification and general sentiment analysis, lacking the ability to capture the complexity of information in Indonesian-language user reviews in a structured manner. This study aims to perform multi-label classification based on four Expectation-Confirmation Model (ECM) factors—Confirmation, Perceived Usefulness, E-satisfaction, and Perceived Security—and to extract six Named Entity Recognition (NER) entities from Blu by BCA Digital application reviews using IndoBERT. The dataset was collected from Google Play Store and Apple App Store covering January to December 2025, yielding 3,389 Indonesian-language reviews after filtering. The study employs a single-task approach, applying oversampling and Focal Loss for multi-label classification, and token augmentation with Conditional Random Field (CRF) for NER. Annotation validation using Krippendorff's Alpha yielded average values of 0.856 for intent labels and 0.919 for NER entities. Results show that the best classification model achieved an F1-Score of 0.798 with a Hamming Loss of 0.131, while the best NER model achieved an F1-Score of 0.812. This study demonstrates that IndoBERT is effective for analyzing digital banking application reviews in identifying ECM factors and extracting domain-specific entities, thereby offering potential support for developers in automatically understanding user needs.
Analisis Komparatif Embedding Semantik Berbasis Large Language Model Pada Sistem Rekomendasi Buku Serendipitous di Perpustakaan Kampus Rahayu Kartika Sari; Eka Dyar Wahyuni; Amalia Anjani Arifiyanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.443

Abstract

The phenomenon of information overload in academic libraries often makes it difficult for users to discover relevant books, which may reduce reading interest. Conventional recommender systems are also prone to filter bubbles and tend to perform poorly under cold-start conditions. This study proposes a sequential recommendation system based on the Self-Attention Based Sequential Recommendation (SASRec) model integrated with five semantic embedding models, namely Word2Vec, BERT Multilingual, OpenAI text-embedding-3-small, Gemini-embedding-001, and Qwen3-Embedding-0.6B, to generate accurate and serendipitous recommendations. In addition, the Serendipity-Oriented Greedy (SOG) re-ranking algorithm is implemented to balance recommendation relevance and serendipity. The data set consists of 14,502 book records and 5,445 user interaction histories after the data cleaning process. Evaluation was conducted under three testing scenarios, namely the all-test set, warm test set, and cold test set, by comparing all model variants before and after the re-ranking process. The results show that the integration of Large Language Model (LLM)-based embeddings consistently improves performance compared to the standard SASRec model and traditional embeddings. Qwen3-Embedding-0.6B achieved the best performance, improving HitRate@10 by up to 282.9% and NDCG@10 by up to 387.8%, while maintaining semantic robustness in cold-start scenarios with an UnSerendipity@K score of 0.613. The implementation of SOG re-ranking reveals a direct trade-off between recommendation accuracy and diversity. Lightweight weighting provides the optimal balance, whereas overly aggressive weighting significantly reduces relevance. The main contribution of this study lies in integrating modern LLM embeddings into a sequential recommendation architecture to improve accuracy and cold-start robustness, while also evaluating the impact of serendipity-oriented re-ranking strategies on balancing recommendation relevance and diversity. Overall, this study demonstrates that modern LLM integration can produce a smarter, more adaptive, and more balanced library recommendation system in terms of both accuracy and serendipity.
Multilabel Aspect-Based Emotion Analysis Pada Ulasan Aplikasi IKD: Pengaruh Focal Loss dan Threshold Tuning Menggunakan Indobert Viviana Purba; Eka Dyar Wahyuni; Tri Luhur Indayanti Sugata
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.448

Abstract

User reviews of the Identitas Kependudukan Digital (IKD) application contain various emotions toward different service aspects. These reviews not only reflect the level of service satisfaction but also encompass user experiences, complaints, expectations, and public perceptions regarding the quality of the system. This study aims to develop a multi-label Aspect-Based Emotion Analysis (ABEA) model using an end-to-end IndoBERT architecture to identify user emotions across each service aspect of the IKD application. Additionally, it analyzes the impact of implementing Focal Loss and threshold tuning on classification performance under highly imbalanced label distributions. Data were collected from 13,197 user reviews on the Google Play Store spanning from June 2024 to November 2025 using web scraping methods, which were subsequently cleaned and filtered to yield 6,891 data entries. Service aspects were empirically identified using BERTopics. Labeling was conducted by three human annotators and two AI annotators, with the final labels determined through majority voting. The model was developed across 6 experimental scenarios varying in preprocessing, Focal Loss, threshold tuning, and data split ratios. Evaluation was performed using F1 Score Macro, F1 Score Micro, Precision, Recall, and Hamming Loss metrics. BERTopic achieved a Coherence Score of 0.6196 and a Topic Diversity of 0.92 with 5 representative aspects. The most optimal model was obtained using a configuration of Focal Loss, a threshold of 0.4, and a 60:20:20 split ratio, achieving an F1 Score Macro of 0.3916, a 24.1% increase from the baseline, alongside an F1 Score Micro of 0.9134 and a Recall of 0.9423. The selected model was successfully integrated into a web-based system using the Flask framework to visualize the classification results. Anger dominated the reviews concerning the Login & Akses Akun and Scan Barcode ke Dukcapil aspects, whereas the Dokumen & Layanan Digital aspect recorded the highest joy emotion. The combination of Focal Loss and threshold tuning proved effective in handling imbalanced label distributions in Indonesian multi-label ABEA classification.
Implementasi Sistem Informasi Eksekutif Untuk Evaluasi Kinerja Penjualan Tiket Kapal Menggunakan Metode Visualisasi Data Dan Drill-Down (Studi Kasus: Seapass) Eka Wahyudinarti; Putri Andini Rachmatika; Agung Brastama Putra; Siti Mukaromah; Eka Dyar Wahyuni
Journal of Golden Generation Multidisciplinary Vol. 1 No. 1 (2025): Februari : Journal of Golden Generation Multidisciplinary
Publisher : PT. Lembaga Penerbit Penelitian Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65244/jggm.v1i1.218

Abstract

Pertumbuhan pesat industri transportasi maritim menyebabkan peningkatan volume data transaksi yang signifikan, sehingga menimbulkan tantangan bagi perusahaan dalam mengelola dan memanfaatkan informasi untuk pengambilan keputusan eksekutif. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Informasi Eksekutif (EIS) pada SeaPass guna mengevaluasi kinerja penjualan tiket kapal. Sistem ini memanfaatkan teknik visualisasi data yang dikombinasikan dengan mekanisme penelusuran dua tingkat, sehingga memungkinkan analisis data secara hierarkis dari ringkasan eksekutif hingga detail operasional. Pengembangan sistem dilakukan melalui siklus hidup terstruktur, diawali dengan analisis kebutuhan eksekutif dan dilanjutkan dengan perancangan prototipe UI/UX menggunakan Figma. Implementasi sistem menggunakan HTML, CSS, dan JavaScript pada sisi front-end, serta MySQL sebagai basis data. Pengujian fungsional dilakukan menggunakan metode Black Box Testing untuk memastikan keandalan sistem. Hasil penelitian menunjukkan bahwa EIS mampu mengintegrasikan data jadwal, informasi kapal, dan data penumpang ke dalam dasbor interaktif. Fitur penelusuran dua tingkat membantu eksekutif mengidentifikasi tren penjualan, anomali operasional, dan perubahan pasar secara real-time, sehingga mendukung pengambilan keputusan yang lebih akurat dan strategis.      
PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DAN INDOBERT PADA ANALISIS SENTIMEN WONDR BY BNI Melisya Sesy Amelia; Shafa Sabrina Almas; Siti Mukaromah; Eka Dyar Wahyuni
Journal of Computer Science and Information Technology Vol. 3 No. 3 (2026): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan layanan perbankan digital mendorong institusi perbankan untuk terus meningkatkan kualitas aplikasi perbankan mobile banking untuk memenuhi kebutuhan dan kepuasan nasabah. Penelitian ini dilakukan untuk menganalisis serta membandingkan tingkat efektivitas algoritma klasifikasi sentimen yang meliputi SVM, Logistic Regression, dan IndoBERT untuk menganalisis opini pengguna yang disampaikan melalui ulasan aplikasi WONDR by BNI. Data penelitian diperoleh melalui proses ekstraksi umpan balik pengguna yang tersedia pada Google Play Store, yang memiliki jumlah ulasan 10.001. Setelah melalui tahap data cleaning, dataset berkurang menjadi 9.999 data karena penghapusan data duplikat dan data yang tidak valid, kemudian sebanyak 9.998 data digunakan pada tahap analisis setelah menghapus satu data yang tidak dapat diproses akibat karakter non-teks. Proses penelitian meliputi pemrosesan awal teks berbahasa Indonesia, pelabelan sentimen menggunakan Groq API berbasis Large Language Model (LLM), pembagian dataset menjadi data latih dan data uji dengan rasio 80:20, ekstraksi fitur TF-IDF untuk model SVM dan Logistic Regression, serta proses fine-tuning IndoBERT sebagai model berbasis transformer. Hasil dari pengujian menunjukkan bahwa IndoBERT yang telah disempurnakan memberikan hasil terbaik dengan accuracy sebesar 88,9%, presicion 89,3%, recall 88,9%, dan F1-score 89,1%. Sementara itu, SVM memperoleh accuracy 85,5%, precision 83,6%, recall 85,5%, dan F1-score 84,0%, sedangkan Logistic Regression memperoleh accuracy 85,5%, precision 83,5%, recall 85,5%, serta nilai F1-score sebesar 83,0%. Berdasarkan hasil penelitian, diketahui bahwa algoritma berbasis transformer menunjukkan kemampuan yang lebih unggul dalam konteks bahasa Indonesia jika dibandingkan dengan model machine learning berbasis TF-IDF. Hasil penelitian ini diharapkan dapat memberikan rujukan bagi peneliti maupun praktisi guna menentukan metode klasifikasi sentimen yang paling sesuai untuk mengevaluasi ulasan pengguna aplikasi perbankan digital berdasarkan data ulasan yang tersedia.
IMPLEMENTASI BLACK BOX TESTING MENGGUNAKAN TOOLS KATALON STUDIO PADA FUNGSIONALITAS WEBSITE REKRUTMEN FAST PRINT INDONESIA Fadiyah Dhara Al Arsya; Nur Cahyo Wibowo; Eka Dyar Wahyuni
ILTEK : Jurnal Teknologi Vol. 20 No. 02 (2025): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v20i02.235

Abstract

Website rekrutmen merupakan platform digital yang dirancang untuk mempermudah proses seleksi dan penerimaan karyawan secara digital. Untuk memastikan fungsionalitas sistem berjalan dengan baik, dibutuhkan proses pengujian perangkat lunak guna menjamin bahwa seluruh fitur berfungsi sesuai kebutuhan pengguna. Penelitian ini menerapkan metode Black Box Testing dengan menggunakan automation testing tools yaitu Katalon Studio untuk meningkatkan efektivitas dan efisiensi dalam proses pengujian. Pengujian dilakukan berdasarkan tahapan Software Testing Life Cycle (STLC) yang mencakup Requirement Analysis, Test Planning, Test Case Development, Test Environment Setup, Test Case Execution dan Test Cycle Closure. Selama pengujian, skenario uji disusun berdasarkan fungsi-fungsi utama yang terdapat dalam website rekrutmen. Proses eksekusi test case dilakukan secara otomatis untuk meminimalisir kesalahan manusia serta mempercepat waktu pengujian. Hasil pengujian menunjukkan bahwa seluruh fitur pada website rekrutmen Fast Print Indonesia telah berjalan sesuai dengan skenario uji yang telah disusun, tanpa ditemukan adanya bug atau kesalahan sistem.
Co-Authors Abdul Rezha Efrat Najaf Adam Rachman, Muhammad Adelia Putri, Ledina Adha, Didan Rizky Agung Brastama Putra Agussalim, Agussalim Ahmad Galih Nur Jati Aji, Dwi rachmat Akira Permata Ramadhani alathoillah, abdul hanif Allendra Donny Irawan Amalia Anjani Arifiyanti Anastasya Nurhaliza, Zabina Anatasya, A Edet Fauri Andhika Rizky Aulia Anisa Rahma Salsabila Anjani, Amalia Apriandi, Dwatra Arfianto, Ricky Arief Yahya Prasetio Ariyana, Denny Arsya Amalia Ristias AryaRafa, Daud Asif Faroqi Asriana, Rina Aulia, Ervina Rosa Bella Trinanda Sanni Bhagas Satrya Dewa Cahyo Wibowo, Nur Candra, Devilia Dwi Dayu Renita Dea Puspita Deswita Rini, Ni Made Berliana Devilia Dwi Candra Dharmawan, Ega Dhian Satria Yudha Kartika Diajeng Putri Widiastuti Dian Rahmawati Dian Rahmawati Eka Wahyudinarti Eka Wahyudinarti Eklesia Simaremare Ervina Rosa Aulia Fadiyah Dhara Al Arsya Fariz Febriany, Asri Kinanti Haidar Triari Respati harby, muhamad faiz Hayaza, QONITA Hilman Habib Habibi, Muhammad Hukama’ Nur Romadlon Icha Sinaga Imam Hanafi Irawan, Allendra Donny Izzuddin, Muhammad Jihan Hasna Iftinan Kusumantara, Prisa Marga Kusumantara, Prisa Marga Kusumantara, Prisa Marga Laksono, Cindy Fitri Lina Wardani Lumintang, Qolbi Adi Marga Kusumantara, Prisa Mas'udah, Erica Mashita Kustyani Maulana Arrasyid, Nizar Maulana, Ribas Satria Melisya Sesy Amelia Mochammad Nabil Nugraha Ramadhan Mohamad Irwan Afandi Muh. Ahlun Nazar Muhammad Farhan Najaf, Abdul Rezha Efrant Najma Choirun Nisa Nendra Wono, Lasmargo Ni Made Berliana Deswita Rini Nur Fadlilah, Imamah Nur Jati, Ahmad Galih Oktaviarini, kamilia nabila Peratasari, Reisa Permatasari, Reisa Prabowo, Dimas Agung Prabowo Prasetyo, Bagus Rizky PUSPITASARI, DIANITA Putri Andini Rachmatika Putri Andini Rachmatika Qolbi Adi Lumintang Rachman Esa Masthury Budoyo Rahayu Kartika Sari Ramdhan Ariansyah Reisa Permatasari Respati, Haidar Triari Ridwandono, Doddy Ristias, Arsya Amalia Rizka Hadiwiyanti Rulyawan, Muhammad Rizky Abiwafa Rumonang, Datu Sadli, Adi Safitri Pradhistya Suwandi Salma, Marylda Sanni, Bella Trinanda Sari, Reisa Permata Satria Yuda Kartika, Dhian Seftin Fitri Ana Wati Sembilu, Nambi Shafa Sabrina Almas Siti Mukaromah Sugiarto Tri Luhur Indayanti Sugata Tri Luhur Indayanti Sugata Viviana Purba Wajendra Dewi, Marylda Salma Wardani, Lina Wati, Seftin Fitri Ana Wibowo, Nur Cahyo Wicaksono, Yeni Windy Fadhilah Susanti Wulansari, Anita Yasmine Shalsabilla, Syafierra Yessy Arye Yustraini Yuniar, Sella Zahrah Hayat Arka Putri