Article Per Year (5 Year)
p-Index From 2021 - 2026
11.862
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Penelitian Pendidikan Matematika dan Sains Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INFORMATIKA PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Abdimas: Papua Journal Of Community Service Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Informatics and Computer Science (JINACS) Ranah Research : Journal of Multidisciplinary Research and Development Journal Serambi Engineering (JSE) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Emerging Information Systems and Business Intelligence (JEISBI) SELAYAR: Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration
Yuni Yamasari
Department Of Informatics, Faculty Of Engineering, Universitas Negeri Surabaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 101 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 101 Documents
Search

 6   7   8   9   10 
Systematic Literature Review on Optical Character Recognition Methods for Text Extraction Nurcahyo, Krisna Bayu Aditya; Ricky Eka Putra; Yuni Yamasari
Jurnal Serambi Engineering Vol. 11 No. 2 (2026): April 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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

Abstract

The development of technology has driven a significant increase in the need for document digitization and automation of text-based data processing. A systematic review is needed to identify progress related to the development of OCR in text extraction. Therefore, this study presents a systematic literature review on the development and use of OCR in text extraction using the PRISMA method. The study began with an initial search of 38 studies, which were then selected based on established criteria. Seven relevant articles were successfully identified through a focused search using the keywords "Optical Character Recognition/OCR." The results of the literature review analysis show that the Convolutional Neural Network (CNN) method is the most widely used approach in the development of OCR for text extraction. In addition, the analysis results also reveal that OCR has been applied in various fields, including healthcare, public administration, government, transportation, and commercial services. This study also highlights the various benefits as well as several challenges that are still faced in the future development of OCR. These challenges include improving character recognition accuracy and handling font variations as well as image quality. Thus, the insights generated by this research contribute to the development of OCR as a more reliable and effective tool in supporting document digitization processes.
Tinjauan Literatur Tentang Event Log dan Process Mining: Implementasi, Tools, dan Algoritma Nafisah, Nurun; Yamasari, Yuni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Analisis event log melalui process mining telah digunakan secara luas untuk mengeksplorasi pola perilaku dalam berbagai domain, terutama pendidikan dan game. Pendekatan ini memanfaatkan alat bantu seperti Disco, ProM, dan metode berbasis machine learning untuk menggali pola perilaku, memahami proses, dan mengevaluasi hasil. Penelitian ini menggunakan metode Systematic Literature Review (SLR) dengan teknik Critical Appraisal Skills Programme (CASP) untuk memastikan kualitas dan validitas studi yang dianalisis. Dari total 48 artikel yang ditemukan, sebanyak 30 artikel lolos tahap seleksi, dan 18 artikel dinyatakan layak untuk dianalisis lebih lanjut. Dalam domain pendidikan, analisis menunjukkan perbedaan signifikan dalam pola belajar siswa, seperti perilaku kelompok dengan nilai tinggi yang cenderung konsisten dibandingkan kelompok dengan nilai rendah. Pola akses ke Learning Management System (LMS) dihubungkan dengan capaian akademik, memberikan wawasan tentang hubungan antara aktivitas belajar dan hasil pembelajaran. Di sisi lain, dalam domain game, algoritma process mining digunakan untuk mengidentifikasi kesalahan pemain, mengevaluasi jalur normatif, dan menganalisis keterkaitan antara model proses dan performa pemain. Hasilnya menunjukkan bahwa algoritma seperti Inductive Miner, Heuristic Miner, dan Fuzzy Miner memberikan fitness model tinggi (0,9–1,0) dan mampu mengekstraksi pola perilaku yang signifikan. Penggunaan tools seperti ProM dan Disco memungkinkan visualisasi model proses yang intuitif, sementara algoritma berbasis machine learning seperti XGBoost memberikan akurasi tinggi dalam memprediksi hasil berdasarkan data event log. Secara keseluruhan, hasil studi menegaskan bahwa process mining berperan penting dalam mengungkap pola perilaku kompleks serta mendukung peningkatan efektivitas pembelajaran dan evaluasi performa pengguna di berbagai domain.   Abstract Event log analysis through process mining has been widely used to explore behavioural patterns in various domains, particularly education and gaming. This approach utilizes Disco, ProM, and machine learning to examine behavioural patterns, understand processes, and evaluate results. This study employed a Systematic Literature Review (SLR) with the Critical Appraisal Skills Programme (CASP) technique to ensure the quality and validity of the analyzed studies. Of the 48 articles found, 30 passed the selection stage, and 18 were deemed suitable for further analysis. In the education domain, the analysis revealed significant differences in student learning patterns, such as the behaviour of high-scoring groups tending to be more consistent than that of low-scoring groups. Access patterns to Learning Management Systems (LMS) were linked to academic achievement, providing insight into the relationship between learning activities and learning outcomes. Process mining algorithms were used in the gaming domain to identify player errors, evaluate normative pathways, and analyze the relationship between process models and player performance. The results show that algorithms such as Inductive Miner, Heuristic Miner, and Fuzzy Miner provide high model fitness (0.9–1.0) and can extract significant behavioural patterns. Tools such as ProM and Disco allow for intuitive visualization of process models. In contrast, machine learning-based algorithms such as XGBoost provide high accuracy in predicting outcomes based on event log data. The study results confirm that process mining is crucial in uncovering complex behavioural patterns and supporting improved learning effectiveness and user performance evaluation across various domains.  
Rule-Based Adaptive Chatbot on WhatsApp for Visual, Auditory, and Kinesthetic Learning Style Detection Muhammad Rahulil; Yuni Yamasari; Ricky Eka Putra; I made Suartana; Anita Qoiriah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1215

Abstract

Adapting learning methods to individual learning styles remains a major challenge in digital education due to the static nature of traditional questionnaires and the absence of adaptive feedback mechanisms. This study aimed to develop a rule-based adaptive WhatsApp chatbot capable of automatically identifying users’ learning styles, visual, auditory, and kinesthetic, through a weighted questionnaire enhanced with probabilistic refinement. The proposed system introduces an adaptive decision framework that dynamically manages conversation flow using score dominance evaluation, early termination, and selective question expansion. Bayesian posterior probability estimation is employed to strengthen decision confidence in borderline cases, ensuring consistent and interpretable results even when user responses are ambiguous. The chatbot was implemented using WhatsApp-web.js and MongoDB, supported by session validation and activity log monitoring to ensure operational reliability and data integrity. System validation involved white-box testing using Cyclomatic Complexity to verify logical accuracy and 20-fold cross-validation using a Support Vector Machine (SVM) to evaluate classification performance. The adaptive model achieved an accuracy of 80.2% and an AUC of 0.902, supported by a balanced precision (0.738), recall (0.662), and F1-score (0.698). These results demonstrate stable discriminative capability and confirm that the adaptive scoring mechanism effectively reduces redundant questioning, lowers cognitive load, and improves interaction efficiency without compromising reliability. In conclusion, the study successfully achieved its objective of developing an adaptive, efficient, and mathematically transparent learning style detection system. The findings confirm that adaptive rule-based logic reinforced by probabilistic reasoning can significantly enhance the efficiency and reliability of digital learning assessments. Future research will extend this framework by incorporating multimodal behavioral indicators and personalized learning content to further strengthen adaptive learning support
Efficient hierarchical summarization of long legal documents using a lightweight transformer and divide and conquer strategy Muhammad Zhafran Ammar; Ricky Eka Putra; Yuni Yamasari
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.5

Abstract

This research addresses the challenges of summarizing long and complex legal documents, which often exceed the input length limitations of transformer-based models and contain intricate legal reasoning structures. The purpose of this study is to develop an efficient and scalable summarization framework that preserves semantic fidelity and structural coherence in judicial summaries. To achieve this objective, a hybrid summarization pipeline is proposed by integrating a Bidirectional Encoder Representations from Transformers (BERT)-based extractive model with a hierarchical abstractive model based on Distilled Bidirectional and Auto-Regressive Transformers (DistilBART), combined with a Divide-and-Conquer strategy. The proposed method partitions long legal documents into smaller segments, processes each segment independently, and reconstructs them into a coherent final summary. Experiments were conducted on the Indian Legal Case Summarization dataset and evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), BERTScore, and Cosine Similarity to assess both lexical overlap and semantic similarity. The results show that the hierarchical DistilBART model outperforms the extractive baseline, achieving a ROUGE-1 score of 0.3802 and a Cosine Similarity of 0.6917. These findings demonstrate that the proposed framework provides an effective solution for long-document summarization in the legal domain.
Pengembangan Edugame Berbasis Virtual Reality Untuk Menumbuhkan Kesadaran Lingkungan Pada Siswa Sekolah Dasar Sulaiman Amrozi Jailani; Yuni Yamasari
JURNAL INFORMATIKA Vol 15, No 1 (2026): Jurnal Informatika
Publisher : Informatics Engineering Department, Dayanu Ikhsanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55340/jiu.v15i1.2707

Abstract

Pendidikan pengelolaan sampah di sekolah dasar memerlukan media yang tidak hanya menyampaikan konsep, tetapi juga melatih tindakan pemilahan sampah secara langsung. Penelitian ini bertujuan mengembangkan dan mengevaluasi ECO-SCHOOL VR, edugame berbasis virtual reality untuk mendukung pengetahuan, kebiasaan, dan perilaku pengelolaan sampah siswa sekolah dasar. Penelitian menggunakan metode Research and Development dengan model ADDIE. Aplikasi dikembangkan menggunakan Unreal Engine 5 dan diimplementasikan pada Oculus Meta Quest 2. Evaluasi melibatkan 75 siswa kelas IV–VI SDN Mulyoagung, Bojonegoro, melalui pengujian black-box, validasi ahli media, pre-test, post-test, kuesioner kebiasaan, observasi lingkungan sekolah, wawancara, uji reliabilitas, analisis N-Gain, Wilcoxon Signed-Rank Test, effect size, dan Technology Acceptance Model. Hasil black-box menunjukkan seluruh fitur inti berjalan sesuai spesifikasi. Validasi ahli media memperoleh rata-rata 89,5 dengan kategori sangat layak. Seluruh instrumen reliabel dengan Cronbach’s Alpha 0,82–0,91. Pengetahuan sampah meningkat dari 44% menjadi 84%, kebiasaan pengelolaan sampah dari 69,3% menjadi 87%, dan kebiasaan membuang sampah dari 9,3% menjadi 95%, dengan N-Gain 0,71; 0,58; dan 0,94. Uji Wilcoxon menunjukkan p-value 0,000 dengan effect size 0,48–0,61. PEOU sebesar 91% dan PU sebesar 95% menunjukkan penerimaan pengguna sangat baik. Namun, karena desain penelitian menggunakan one-group pretest-posttest tanpa kelompok kontrol, hasil ini ditafsirkan sebagai bukti awal peningkatan, bukan bukti kausal mutlak.
Bitcoin price prediction using Autoencoder-based GRU and LSTM models Nurun Nafisah; Yuni Yamasari; Ervin Yohannes
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.027

Abstract

The high volatility of Bitcoin prices presents a major challenge in forecasting, as predictive models must capture both short-term fluctuations and long-term trends. Despite extensive studies on deep learning for cryptocurrency prediction, there remains a lack of systematic comparative analysis of autoencoder-based recurrent architectures, particularly AE-LSTM and AE-GRU, across both univariate and multivariate input settings, with statistical validation. This study analyzes Bitcoin price data from 2018 to 2025 to evaluate and compare the performance of two hybrid deep learning models, Autoencoder-based Gated Recurrent Unit (AE-GRU) and Autoencoder-based Long Short-Term Memory (AE-LSTM), in Bitcoin price prediction. The experiments explore the effects of dropout, learning rate, and epochs, using both univariate and multivariate inputs (Open, High, Low, Close). Results show that AE-GRU consistently outperforms AE-LSTM across all configurations, achieving up to 16.5% higher MAPE-based accuracy. The best performance was achieved by the multivariate AE-GRU, dropout = 0.1, learning rate = 0.001, epoch = 100, with RMSE 1667.125, MAE 1145.718, MAPE 2.33%, and R² 0.995017. Moreover, AE-GRU demonstrates faster training efficiency, requiring 185–195 ms/step, while AE-LSTM takes 208–215 ms/step under the same conditions. AE-GRU's superior accuracy and efficiency are attributed to its simplified gating structure and the feature compression capability of the autoencoder, which enhances learning stability and generalization. Overall, the AE-GRU model offers robust predictive performance and computational efficiency. It is a reliable framework for real-time cryptocurrency forecasting and a promising foundation for advanced deep learning architectures in financial time-series analysis.
Pelatihan Aplikasi AI untuk Peningkatan Literasi dan Profesionalisme Guru dalam Pembelajaran Sekolah Yuni Yamasari; Anita Qoiriah; Aditya Prapanca
SELAYAR: Jurnal Pengabdian Masyarakat Vol. 2 No. 1: SELAYAR: Jurnal Pengabdian Masyarakat, Februari 2026
Publisher : Gema Cendekia Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/selayar.v2i1.351

Abstract

Perkembangan teknologi kecerdasan buatan (AI) memberikan peluang besar dalam mendukung peningkatan kualitas pembelajaran di sekolah. Namun, pemanfaatan AI oleh guru masih menghadapi kendala, terutama pada aspek pemahaman dan keterampilan penggunaan secara praktis. Oleh karena itu, kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan literasi dan kompetensi guru dalam memanfaatkan aplikasi ChatGPT sebagai alat bantu pembelajaran. Metode pelaksanaan kegiatan meliputi tahap pra-pelaksanaan melalui penyebaran angket kebutuhan, pelaksanaan pelatihan berupa pemaparan materi dan praktik langsung penggunaan ChatGPT, serta tahap pasca-pelaksanaan melalui evaluasi menggunakan angket. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada pemahaman, keterampilan, dan kepercayaan diri peserta dalam menggunakan ChatGPT. Mayoritas peserta menyatakan pelatihan sangat bermanfaat, mudah dipahami, dan relevan dengan kebutuhan pembelajaran. Selain itu, peserta menunjukkan kesiapan untuk mengimplementasikan hasil pelatihan dalam penyusunan materi ajar, pembuatan soal, dan pengembangan inovasi pembelajaran di sekolah. Dengan demikian, kegiatan PKM ini berhasil meningkatkan kapasitas mitra dan berpotensi memberikan dampak berkelanjutan terhadap kualitas pembelajaran berbasis teknologi di lingkungan sekolah.
Klasifikasi Level Retinopati Diabetik Menggunakan Metode Hybrid Vision Transformer dan EfficientNet Meakhel Gunawan; Yuni Yamasari
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak—Retinopati diabetik merupakan komplikasi diabetes melitus yang menjadi salah satu penyebab utama kebutaan di dunia, termasuk di Indonesia. Oleh sebab itu, deteksi dan klasifikasi tingkat keparahan secara dini berbasis citra fundus retina menjadi krusial untuk mendukung penanganan klinis yang tepat. Vision Transformer yang memiliki keunggulan dalam menangkap konteks global citra, dan EfficientNet yang unggul dalam mengekstraksi fitur lokal, memiliki peluang untuk dikombinasikan secara optimal dalam klasifikasi level retinopati diabetik. Menggunakan Dataset APTOS 2019 Blindness Detection dengan tahapan preprocessing berupa cropping, resize, CLAHE, dan normalisasi, serta augmentasi data untuk mengatasi ketidakseimbangan kelas, model MobileViT-XS yang dikombinasikandengan EfficientNet-B0 hingga EfficientNet-B4 diuji dan dievaluasi menggunakan metrik efektivitas serta metrik efisiensi komputasi. Hasil penelitian menunjukkan bahwa hybrid MobileViT-XS dan EfficientNet-B1 dengan resize 512×512 piksel memberikan kinerja paling optimal, dengan akurasi validasi sebesar 91,80% dan akurasi pengujian sebesar 93,24%, serta efisiensi komputasi yang seimbang dengan waktu pelatihan 101 menit 59.44 detik, FLOPs sekitar 6,80G, dan ukuran model 37,95 MB. Penelitian ini menyimpulkan bahwa pendekatan metode hybrid mampu menghasilkan model yang efektif dan efisien untuk mendukung sistem deteksi dini retinopati diabetik berbasis kecerdasan buatan. Kata Kunci—retinopati diabetik, vision transformer, EfficientNet, hybrid model, klasifikasi citra, deep learning
Analisis Kinerja Retrieval Augmented Generation (RAG) Klasik Dalam Chatbot Akademik berbasis Multimodal Kartika Wulandari; Yuni Yamasari
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak— Akses informasi akademik di Universitas Negeri Surabaya (UNESA) melalui website panduan saat ini dinilai belum optimal karena kurangnya interaktivitas, sehingga mahasiswa seringkali harus menghubungi pihak administrasi secara langsung. Penelitian ini bertujuan untuk melakukan evaluasi kinerja Retrieval-Augmented Generation (RAG), yaitu RAG Klasik guna menguji kinerja sistem dalam menghasilkan respon yang relevan, akurat, dan kontekstual pada chatbot akademik. Lingkup penelitian ini difokuskan pada pengujian evaluasi kinerja dan efisiensi metode tersebut dalam mengolah data multimodal yang bersumber dari portal SSO UNESA, mencakup format teks, gambar, dan dokumen PDF. Metodologi yang digunakan meliputi tahap pengumpulan data, ekstraksi teks menggunakan Optical Character Recognition (OCR), preprocessing, serta penerapan dua strategi pelabelan, yakni manual dan otomatis. Penelitian ini menganalisis kinerja RAG Klasik yang menggunakan algoritma BM25 dalam mengolah data multimodal. RAG Klasik diimplementasikan menggunakan algoritma BM25 berbasis kata kunci. Evaluasi dilakukan secara sistematis menggunakan metrik efektivitas (Precision, Recall, F1-Score, Exact Match, dan Cosine Similarity) serta metrik efisiensi (waktu respons). Hasil penelitian menunjukkan bahwa RAG Klasik memiliki efisiensi luar biasa dengan waktu respon rata-rata dibawah 0,01 detik. Namun, dari sisi kinerja, metode ini sangat bergantung pada kualitas pelabelan, di mana skenario label otomatis mencapai F1-Score 0.52 dan Csoine Similarity 0.51, mengungguli scenario label manual. Penelitian menyimpulkan bahwa RAG Klasik sangat ideal untuk informasi procedural yang membutuhkan respon instan meskipun memiliki keterbatsan dalam pemahaman semantic yang mendalam.   Kata Kunci— Retrieval Augmented Generation (RAG), RAG Klasik, BM25, Multimodal, UNESA.
Analisis Persepsi Pengguna Berdasarkan Nielsen's Attributes of Usability dan Karakteristik Perhatian Visual Menggunakan Eye Tracking pada Website SSO UNESA Ikko Cahya Awinata; Yuni Yamasari
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak— Website Single Sign-On (SSO) Universitas Negeri Surabaya (UNESA) merupakan gerbang utama akses berbagai layanan akademik sehingga kualitas usability dan efisiensi navigasi visual menjadi faktor penting dalam mendukung pengalaman pengguna. Penelitian ini bertujuan untuk menganalisis persepsi pengguna berdasarkan Nielsen's Attributes of Usability (NAU) serta mendeskripsikan karakteristik perhatian visual pengguna melalui visualisasi heatmap dan gazeplot. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan metode task-based testing yang melibatkan 23 mahasiswa aktif Jurusan Teknik Informatika UNESA. Pengumpulan data dilakukan melalui kuesioner NAU yang mencakup aspek learnability, efficiency, memorability, errors, dan satisfaction, serta pengujian eye tracking berbasis platform RealEye.io. Hasil penelitian menunjukkan bahwa aspek memorability memperoleh skor tertinggi dengan grand mean 4,10 dalam kategori Setuju, mengindikasikan struktur navigasi dan letak menu relatif mudah diingat. Aspek learnability dan errors masing-masing memperoleh skor 3,83 dalam kategori Setuju, sedangkan aspek satisfaction memperoleh skor 3,54 dalam kategori Setuju. Aspek efficiency memperoleh skor terendah sebesar 3,41 dalam kategori Netral, menunjukkan bahwa pengguna belum sepenuhnya merasakan efisiensi navigasi secara konsisten. Analisis heatmap mengungkap dua kecenderungan utama, yaitu fokus pada area konten utama (blok menu) dan fokus pada area navigasi atas (header). Analisis gazeplot menunjukkan variasi alur pandangan dari pola terstruktur hingga tidak teratur yang mengindikasikan kebingungan dalam pencarian informasi. Penelitian ini menyimpulkan bahwa website SSO UNESA memiliki tingkat kemudahan diingat yang baik, namun masih memerlukan perbaikan pada efisiensi navigasi visual dan konsistensi desain antarmuka.   Kata Kunci— Usability, Nielsen's Attributes of Usability, Eye Tracking, Heatmap, Gazeplot, SSO UNESA
Co-Authors Aditya Prapanca Aditya Prapanca Agnes, Rifa Zaini Agus Prihanto Agus Prihanto Agustin Tjahyaningtijas, Hapsari Peni Alhakiim, Thomi Aditya Alifian Erga Iswardhana Alpiana, Intan Ammar, Muhammad Zhafran Amrina Rosyada Andi Iwan Nurhidayat Anggraini, Lusiana Anindha Arsya Dhaniswara Anita Qoiriah ANITA QOIRIAH Anita Qoiriah Anita Qoiriah Anita Qoiriah Anjani, Ayu Annisa Nur Hidayati ARI KURNIAWAN Arya Tandy Hermawan Asma Johan Asmunin Asmunin Atik Wintarti Atik Wintarti Audzad Dzulhijzah Aviana, Anisah Nurul Azalia, Virna Hari Nur Chindy Ayudia Sri Fastaf Derri Nuril Wardana Dwi F. Suyatno Eka Putra, Ricky Ervin Yohannes Ervin Yohannes Ervin Yohannes Ervin Yohannes Esa Maidatussohiba Esther Irawati Setiawan Esti Yogiyanti Esti, Esti Fani Fadillah Hermawan Farid Baskoro Fatimah Nur Alifiah Febry San Pratama Fellia Nurlailatul Rochmah Firdaus Bagus Wicaksono Firdaus, Mohamad Adi Putra Hani Nafisah Amaliya Hapsari Peni Agustin Tjahyaningtijas Harahap, Satria Baladewa Herlangga Fitdia Saputra I made Suartana I Made Suartana Ika Putri Arisanti Ikko Cahya Awinata Inez Hafizhah Febrina Iqbaal Januar Eka Firmansyah Ismail Johanes, Sugiharto Kartika Wulandari Khahar, Abdul Khoiru Miftakhul Falah Khusna, Asmaul M Dzikri Hisyam Ilyasa M. Aziz Rizaldi Mas Arya Bhisma Rangga Douval Saputra Mauridhi H. Purnomo Mauridhi Hery Purnomo Meakhel Gunawan Mediana, Prissely Pravasstifany Miranda Dwi Diana Moh. Novil Ma'arij Mohammad Hasan Machfoed Muhamad Alfin Ardiansyah Muhamad Azis Thohari Muhamad Khafidhun Alim Muslim Muhammad Habil Habibi Muhammad Rahulil Muhammad Rifki Agustian Muhammad Zakia Avlach Muhammad Zhafran Ammar Mutiara Nur Zarima Zarima Muttaqin, Aziz Fiqri Nafisah, Nurun Naim Rahmawati Naim Rochmawati Naim Rochmawati Nanda Ade Handaya Nesya Amelinda Nugroho, Supeno M. S. Nurcahyo, Krisna Bayu Aditya Nurun Nafisah Nurun Nafisah Oktafia Widya Santoso Pradangga, Aryo Praptama, Ervan Putri Alvina Putri, Rezky Arisanti Raden Mohamad Herdian Bhakti Rafli Aditya Pramana Raharko, Natasha Isnaeni Rahayu, Siskawati Rahmawati, Naim Rahulil, Muhammad Ramadhan, Dani Rebecca Jasmine Surya Ricky Eka Putra Ricky Eka Putra Ricky Eka Putra Rina Harimurti Rochmawati, Naim S., Rahma Aziz Sadewa, Bagas Ahmad Salahuddin, Muhammad Rico Saputra, Ivan Rangga Shahputri, Vira Arum Solihin, Aziz Suartana, I Made Sukrisna Sulaiman Amrozi Jailani Supeno M. S. Nugroho Surya, Arum Ayu Suyatno, Dwi F. Syarif Hidayatulloh Tazki Yatun Niyah Tohari Ahmad Wardana, Prima Lukito Widi Aribowo Wiyli Yustanti Wiyli Yustanti Wiyli Yustanti Yoyok Prastyo, Yoyok