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Ema Utami

Universitas Amikom Yogyakarta

Author-ID : 259410

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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An Ontology-Driven Adaptive User Interface Framework for Indonesian Student Wellbeing Bhanu Sri Nugraha; Muhammad Suyanto; Kusrini Kusrini; Ema Utami
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.26404

Background: The personalization of digital learning environments to support student wellbeing presents a significant challenge due to the need for integrating multi-dimensional contextual factors. Existing adaptive user interface (AUI) approaches often rely on ad-hoc rule-based systems or simpler adaptive mechanisms, lacking the formal semantic grounding required to handle the intricate interdependencies between diverse wellbeing dimensions and corresponding UI adaptations. Objective: This research aimed to design, develop, and formally validate a novel semantic framework for a context-aware AUI specifically tailored to provide a formal foundation for supporting multidimensional student wellbeing factors. Methods: The methodology involved creating a detailed "Context Mapping" table to establish relational scenarios between student contexts, wellbeing factors, and UI adaptations. Subsequently, a Semantic Taxonomy was engineered and implemented as a Web Ontology Language (OWL) model in Protégé. Its relational data retrieval capabilities were demonstrated via the SPARQL Protocol. The principal outcome of this investigation is the formal validation of the ontology using the HermiT reasoner. This study utilized a comprehensive literature review to formulate its context mapping, followed by a formal modeling and validation methodology. Results: Using the HermiT reasoner conclusively determined that the model is both coherent and consistent. The research successfully translated qualitative Indonesian student wellbeing requirements into a formal, computationally tractable semantic framework. Conclusion: The developed semantic framework provides a validated foundation for the deployment of intelligent, context-aware applications designed to enhance Indonesian student wellness in higher education. This research successfully multi-dimensional requirements for a context-aware student wellbeing application into a robust, and scalable framework.

Utilization of the Convolutional Neural Network Method for Detecting Banana Leaf Disease Nita Helmawati; Ema Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6140

Banana leaf diseases such as Sigatoka, Cordana, and Pestalotiopsis pose a significant threat to banana productivity, with implications for food security and the global economy. Early detection of this disease is an important step to reduce its spread and maintain crop yield stability. This research utilizes the Convolutional Neural Network (CNN) method to detect banana leaf diseases based on image analysis of infected and healthy leaves. The dataset used includes 937 images consisting of four main categories, namely healthy leaves, Sigatoka, Cordana, and Pestalotiopsis. The dataset is processed through augmentation to increase data diversity and quality. The CNN model was applied for classification, with evaluation results reaching 92.85% accuracy, 95.73% recall, 93.52% precision, and 94.60% F1-score. This research contributes to the development of Artificial Intelligence-based technology for applications in the agricultural sector, especially in supporting farmers to detect banana leaf diseases quickly, accurately and efficiently. The research results also provide recommendations for exploring additional data augmentation and increasing dataset variety to improve model detection performance in the future. This shows CNN's potential in supporting sustainable agriculture in the modern era.

Boosting YOLO11: Global Attention & Hyperparameter Tuning for High-Fidelity Military Aircraft Detection Satyo Widijanuarto; Ema Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7102

Military aircraft detection from aerial and satellite imagery is crucial for strategic surveillance and intelligence. This study evaluated the impact of the Global Attention Mechanism (GAM) and hyperparameter optimization on the performance of the YOLO11 model for military aircraft detection. Utilizing a traditional YOLO model as a baseline, we compared precision, recall, and mean Average Precision (mAP) metrics across various configurations. These configurations included the implementation of GAM and variations in n, s variant of YOLO11, optimizers (Adam, NAdam, RAdam, Adamax, AdamW, SGD) and learning rates (0.01, 0.001, 0.0001, 0.00417). Experimental results demonstrate that the integration of GAM significantly enhances the model's detection capabilities within 300 iterations, particularly when combined with the Adamax optimizer and a learning rate of 0.001. This specific configuration achieved the highest mAP performance of 98.5%, outperforming other setups. Further confusion matrix analysis confirmed high accuracy in classifying various aircraft types, while also highlighting some challenges in distinguishing certain classes. The primary contribution of this study is the empirical demonstration of improved military aircraft detection performance by YOLO11 through the utilization of four global attention mechanism modules and effective hyperparameter tuning. These findings offer valuable insights for developing more accurate and robust object detection systems for defense and security applications.

Comparative Analysis of Machine Learning-Based Software Defect Prediction in Object-Oriented and Structured Paradigms Using Apache Camel and Redis Datasets Nasiri, Asro; Setyanto, Arief; Utami, Ema; Kusrini, Kusrini
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5315

Software Defect Prediction (SDP) is a crucial component of software engineering aimed at improving quality and testing efficiency. However, the majority of SDP research often overlooks the fundamental influence of the programming paradigm on the nature and causes of defects. This study presents a comparative analysis to identify the most influential software metrics for predicting defects across two distinct paradigms: Object-Oriented (OOP) and Structured. To ensure modern relevance and reproducibility, we constructed two new datasets from large-scale, open-source projects: Apache Camel (Java) for OOP and Redis (C) for Structured which exhibited realistic defect rates of 14.4% and 21.8%, respectively. The dataset creation process involved mining Git repositories for defect labeling and automated metric extraction using the CK and Lizard tools. Correlation analysis and baseline modeling using Random Forest revealed significant differences between the paradigms. In the OOP system, dominant defect predictors were related to the complexity of the class interface and features (e.g., uniqueWordsQty, totalMethodsQty, WMC, CBO). Conversely, defects in the structured system were strongly correlated with size and algorithmic complexity (e.g., file_tokens, file_loc, file_ccn_sum). Although the baseline models performed well (ROC–AUC = 0.82–0.87), the significant class imbalance resulted in low recall (44–50%). This motivates the need for more context aware approaches. These findings underscore that effective SDP strategies must be tailored to the underlying programming paradigm.

Benchmarking Relational and Array-Based Models for Genealogical Data Storage in PostgreSQL Raharjo, Suwanto; Utami, Ema
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5629

Genealogical information systems manage inherently hierarchical data structures that represent family relationships across multiple generations. Traditional implementations predominantly rely on normalized relational database designs using junction tables to model parent–child relationships. While this approach ensures strong referential integrity, it often incurs substantial performance overhead due to complex join operations during deep hierarchical traversal. Recent versions of PostgreSQL provide native support for array data types This study compares two genealogical database models implemented in PostgreSQL: a normalized relational model using a junction table and a denormalized model that stores child identifiers directly as UUID arrays. To evaluate their performance, we conducted controlled benchmarking experiments using synthetically generated genealogical datasets with varying generational depth and branching patterns. The comparison focuses on storage efficiency, recursive traversal performance, and write operation costs under realistic hierarchical workloads. Results obtained from a large-scale dataset containing more than 7 million individual records show that the UUID array–based model reduces disk space usage by 31%. During deep recursive traversal involving over 12 million nodes at the tenth generation, the array-based model demonstrates improved data locality, leading to a 5.2% reduction in execution latency and 7% fewer shared buffer accesses compared to the relational model. Interestingly, contrary to common expectations in normalized database design, the array-based model achieves 22% faster single-insert performance because it avoids foreign key validation and multiple index updates. This improvement comes with slightly higher write amplification, reflected in a 6.6% increase in buffer usage due to PostgreSQL’s multi-version concurrency control mechanism. These findings contribute to the field of Informatics by providing empirical evidence on how database internal mechanisms influence performance trade-offs in hierarchical data management, offering guidance for designing scalable and read-efficient information systems beyond genealogical applications.

Temporal Gradient Oscillation with Accuracy Recovery Mechanism for Efficient BERT-Based Text Classification Indra Listiawan; Ema Utami; Kusrini Kusrini; Arief Setyanto
JET (Journal of Electrical Technology) Vol 11, No 2 (2026): : Edisi June
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i2.13472

Large Language Models (LLMs) such as BERT have demonstrated impressive performance across various NLP tasks, yet their high computational cost poses challenges for deployment in resource-constrained environments. This paper proposes a dynamic temporal token pruning approach based on gradient oscillation monitoring, where token importance is estimated from the temporal variability of gradient signals during training. Tokens exhibiting low gradient oscillation are selectively pruned to reduce effective input length. To mitigate potential performance degradation caused by aggressive pruning, an accuracy recovery mechanism based on lightweight re-finetuning is introduced. Experimental results on benchmark sentiment classification datasets, including IMDB and SST-2, demonstrate that the proposed method substantially reduces the number of input tokens while maintaining or recovering predictive performance. These results indicate that gradient oscillation provides a viable signal for token-level efficiency, achieving a favorable trade-off between input sparsity and model accuracy without modifying the underlying Transformer architecture.

Systematic Review of the Use of the MIT-BIH Polysomnography Database for the Detection and Classification of Sleep Disorders Akbar Akbar; Ema Utami
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5819

The MIT-BIH Polysomnography Database (SLPDB) is a widely adopted benchmark for the development of automated methods for sleep disorder detection and sleep stage classification. This study presents a Systematic Literature Review of 35 articles that utilize the SLPDB, examining research focus areas, types of physiological signals employed, and the computational approaches applied. Five major methodological categories were identified: Sleep Apnea Detection, Sleep Staging, Signal Processing Enhancement, Multichannel Fusion Methods, and Interpretable Artificial Intelligence, with the first two categories being the most dominant. Four groups of physiological signals—EEG, ECG, respiratory signals, and multichannel data—form the basis for model development, where EEG is predominantly used for sleep staging and ECG for sleep apnea detection. Deep learning approaches, particularly CNNs, LSTMs, and hybrid models, are the most frequently employed techniques. Reported model accuracies range from 78% to over 99%, depending on the signal modality and modeling strategy. Future research should prioritize the development of more interpretable hybrid models and broader clinical validation to enhance reproducibility and implementation readiness.

Deteksi Penyakit Gigi dan Mulut Menggunakan Algoritma Inception-V3 Detection of Dental and Oral Diseases Using Inception-V3 Dloifur Rohman Al Ghifari; Ema Utami; Dhani Ariatmanto
Jurnal Pendidikan Indonesia Vol. 6 No. 4 (2025): Jurnal Pendidikan Indonesia (Japendi)
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i4.7649

Kesehatan gigi dan mulut sangat penting bagi kesejahteraan umum, namun banyak orang mengabaikan pengobatan karena kurangnya kesadaran atau tantangan diagnostik. Metode diagnostik tradisional sering kali kurang akurat dan efisien. Penelitian ini bertujuan mengembangkan sistem otomatis untuk mengklasifikasikan penyakit gigi dan mulut menggunakan algoritma deep learning Inception-V3 guna meningkatkan akurasi diagnostik. Penelitian menggunakan dataset 8.776 citra oral yang diseimbangkan dengan SMOTE dan diproses dengan teknik augmentasi. Inception-V3 dilatih dan dibandingkan dengan CNN, VGG-16, ResNet50, serta model machine learning tradisional. Model Inception-V3 mencapai akurasi 94%, mengungguli model lain (CNN: 81%, VGG-16: 88.7%, ResNet50: 76.25%) dan menunjukkan stabilitas tanpa overfitting. Studi ini menegaskan potensi Inception-V3 dalam analisis gambar medis, menawarkan alat diagnostik yang andal untuk deteksi dini penyakit gigi dan mulut, sehingga dapat meningkatkan hasil layanan kesehatan.

Design of Automatic Feeder with Adjustable Temperature, PH, and Weather for Catfish Pebri Antara; Ema Utami; Kusnawi Kusnawi
Cerdika: Jurnal Ilmiah Indonesia Vol. 5 No. 4 (2025): Cerdika: Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v5i4.2473

Catfish is a fish that is consumed by many Indonesians and is one of the business opportunities. This can be a good prospect for the future with catfish farming. Even many restaurants and restaurants use this fish as one of the menus. Cultivating catfish is not easy, there are many parameters that need to be considered so that fish life can develop properly. Starting from sufficient water ph or according to the standard of living catfish, up to the air temperature. These factors will affect if catfish farms are ignored. Timely feeding is also a major factor that can have sufficient nutrition. With an automatic fish feed system and water quality monitoring, farmers' problems related to water quality can be monitored using sensors. Feeding can be done via a telegram bot with certain settings as needed.

A Systematic Literature Review of Retrieval-Augmented Generation: Methods, Applications, and Future Research Directions Muhammad Rizky Hajar; Ema Utami; Alva Hendi Muhammad
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i2.1170

Retrieval-Augmented Generation (RAG) represents a growing research direction in the advancement of large language models (LLMs) by incorporating external information sources into the response generation process. As LLM-based systems are increasingly deployed in information-sensitive domains such as healthcare, education, and law, the demand for responses that are not only fluent but also verifiable and context-aware has become more pronounced. This study conducts a systematic literature review (SLR) of 100 recent publications to examine methodological approaches, application domains, technical challenges, and research contributions related to RAG. The review draws on studies indexed in major academic databases, including IEEE, ACM, and Springer, and applies structured inclusion and exclusion criteria to ensure analytical rigor. The findings reveal a strong emphasis on architectural optimization, particularly in the interaction between retrieval and generation components, alongside widespread adoption in domain-specific contexts. Persistent challenges identified across the literature include limitations in retriever effectiveness, system integration complexity, and the absence of standardized evaluation benchmarks. Overall, this review provides a structured synthesis of current RAG research and highlights directions for future investigation and practical deployment..

Co-Authors , Anggit Dwi Hartanto A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdul Malik Zuhdi Abdullah Ardi Abdulrahmat E Ahmad Abyan Fauzi Widihasani Achmad Yusron Arif Ade Pujianto Adi Surya Adiatma, Biva Candra Lutfi Afif, Muhammad Sholih Afifah Nur Aini Afis Julianto Aflahah Apriliyani Afu Ichsan Pradana Agun Nurul Widiyanto Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetyo Agung Dwi Cahyanto Agung Nurhidayatullah, Rizqy Agung Susanto Agus Fathurahman Agus Fatkhurohman AGUS PURWANTO Agustin, Tinuk Agustina Srirahayu Ahmad Fauzi Ahmad Febri Diansyah Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Hajar Ahsan, Muhammad Rafiqudin Ahsan, Muhammad Rafiqudin Ainul Yaqin Ainul Yaqin Ainul Yaqin Aji Said Wahyudi Hidayat Aji Triwerdaya Akbar Akbar Akhmad Arif Faisal Siregar Akhmad Dahlan Al Fathir As, Rahmat Saudi Aldy A Kulakat Alfansani, Abdul Rauf Alfin Mahadi Alfrida Sabar Alimuddin Yasin Almi Yulistia Alwanda Alqowiy, Mohd Qorib Alsyaibani, Omar Muhammad Altoumi Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Alvhinia Meinda Amitaba Alvian Trias Kurniawan Alvian Trias Kurniawan Alvina Felicia Watratan Amali Amali Amir Fatah Sofyan Amir, Fail Amrullah, Ahmad Afief Amrullah, Ahmad Afief Amrullah, Yusuf Amri Andang Wijanarko Andhika Wisnu Widyatama Andhika Wisnu Widyatama Andi Sunyoto Andrie Prajanueri Kristianto Anggi Muhammad Rifai Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggit Hartanto Anggit Rianansyah Anggriandi, Dendi Anip Moniva Anisa Rahmanti Anisya Nursyah Gusman Anjar Setiawan Annisa Rahayu P Anwar Sadad Ardi, Abdullah Arfian Hendro Priyono Arham Rahim Ari Rudiyan Arief Setyanto Arief, M.Rudyanto Arif Nur Rohman Arif Rahman Arif Santoso Arif Sutikno Arif, Achmad Yusron Aris Setiyadi aristin chusnul khotimah Arli Aditya Parikesit Armadiyah Amborowaty Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Arridho, Muhammad Noor Arvi Pramudyantoro Arya Luthfi Mahadika Asrawi, Hannan Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asrul Abdullah Astica, Yustikamasy Atin Hasanah Aziza Devita Indraswari Baiq Sri Mardiani Bangun Watono Banu Dwi Putranto Basri, Nur Faizal Bayu Setiaji Bety Wulan Sari Bhanu Sri Nugraha Bima Widianto Bisono, Hadi Hikmadyo Biva Candra Lutfi Adiatma Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Brahmantha, Gede Putra Aditya Budi, Agung Prasetio Buyut Khoirul Umri Cahya Pangestu, Galang Candra Adipradana Candra Aditya Pinuyut Carolina, Vinnesa Patricia Catur Iswahyudi Catur Iswahyudi Catur Riyono Heri Wibowo Cecep Yedi Permana Chan Uswatun Khasanah Chavid Syukri Fatoni Christina Andriyani Constantin Menteng D. Diffran Nur Cahyo Dalillah Razan S Danar Putra Pamungkas, Danar Putra Dandi Sunardi Dany Fajar Kristanto Saputro Wibowo David Agustriawan Dede Sandi Dedy Ikhsan Dedy Sugiarto Dengen, Angel Fourtuna Deny Nugroho Triwibowo Desy Bawan, Sarah Bunda Deta Oktariani Dewa Saksana, Jidan Dewi Yustika Lakoro Dhana Aulia Ayu Kurniawan Dhanar Intan Surya Saputra Dhani Ariatmanto DHANI ARIATMANTO Dhani Ratna Sari Dhani Ratna Sari, Dhani Ratna Dibyo Sudarsono Dimaz Arno Prasetio Dina Juni Marianti Dloifur Rohman Al Ghifari Donni Prabowo Donny Yulianto Dwi Ahmad Dzulhijjah Dwi Hartanto, Anggit Dwi Hartono, Anggit Dwi Rahayu Dwi Septiyani Arwin Dwi Yuli Prasetyo Dzulhijjah, Dwi Ahmad Eddy Nurraharjo Eddy Nurraharjo Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edi, Mohammad Eko Boedijanto, Eko Eko Darmanto Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Purwanto Elim, Marthinus Ikun Elvis Pawan Elvis Pawan Emha Taufik Lutfi Emha Taufiq Luthfi Emilya Ully Artha Emilya Ully Artha Enie Yuliani Enni Lindrawati Eric Ariyanto Ermawan, Bagas Restya Esha Alma'arif Fachruddin Edi Nugroho Saputro Fadhillah, Akmal Rafi Fahmi Ilmawan Fahry, Fahry Fail Amir Faisal Fadhila Fajar Ardanu Fajar Rohman Hariri Fajar Surya Putro Farid Fitriadi Fariz Zakaria Fathoni Dwiatmoko Fatoni, Chavid Syukri Felisberto Pereira Fendi Sumanto Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fersellia, Fersellia Fidya Farasalsabila Firdaus, M. Haikal Firdiyan Syah Firdiyan Syah Firstyani Imannisa Rahma Firstyani Imannisa Rahma Firza Septian Fitrah Eka Susilawati Fitriana, Frizka Fitriani Fitriani Fitrony, Fachri Ayudi Gabriel Bintang Timur Gardyas Bidari Adninda Gardyas Bidari Adninda Gusti F Rahman Gusti Fathur Rakhman Habib, Muhammad Hafidh Rezha Maulana Hafidz Sanjaya Hafidz Sanjaya, Hafidz Hafiz Ridha Pramudita Hafiz Ridha Pramudita, Hafiz Ridha Halim Bayuaji Sumarna Hamdani, Nahrowi Hamdikatama, Bimantyoso Hanafi Hanafi Hanafi Hanafi Hanafi Hani Setiani Hanif Al Fatta Hardita, Veny Cahya Hartanto, Anggit Dwi Hartatik Hary Susanto Hasna Nirfya Rahmandhani Hedy Leoni Henderi . HENDRA SETIAWAN Hendrawan, Ivan Rifky Hendrik Setiawan, Hendrik Herda Dicky Ramandita Herlandro Tribiakto Hidayat, Jati Arif Hikmianto, Riki Hirmayanti Hudha, Yans Safarid I Dewa Bagas Suryajaya, I Dewa Bagas I Made Adi Purwantara I Wayan Rangga Pinastawa Idris Idris Ikrahmi Ikrahmi Imam Ainuddin P Ina Sholihah Widiati, Ina Sholihah Indarto Indarto Indra Listiawan Irawan, Ridwan Dwi Irawan, Rio Irfan Kurniawan Irma Yanti Irsyad Khalid Ilyas Irwan Siswanto Iskandar, Ahmad Fikri Isra Andika Bakhri Ivan Rifky Hendrawan Ivan Rifky Hendrawan Ivan Rifky Hendrawan Jangkung Tri Nugroho Januario Freitas Araujo Bernardo Jihadul Akbar Juni Marianti, Dina Kartikasari Kusuma Agustiningsih Kasim, Rafli Junaidi Khifni Beyk Ahmad Khoirunnita, Aulia Khusnawi Khusnawi Krisnawati Krisnawati Kriswantoro, Andi Kumara Ari Yuana Kumara Ari Yuawan Kuncoro Hadi Kurniawan, Muhammad Bayu Kusnawi Kusnawi Kusrini Kuswantoro, RB. Hendri Langgeng Hadi Prasetijo Lewu, Retzi Lindrawati, Enni Lisa Dinda Yunita M Imam Budi Laksamana M. Imam Budi Laksamana M. Imam Budi Laksamana M. Nuraminudin M. Rudyanto Arief M. Rudyanto Arief M. RUDYANTO ARIEF M. Rudyanto Arief M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syafri Lamato M. Ulil Albab M. Zainal Arifin M. Ziaurrahman Ma'ruf Aziz Muzani Mahdi Ridho Mahmud Zunus Amirudin Mardi Utomo Marianti, Dina Juni Maringka, Raissa Marselina Endah Hiswati Martina Endah Pratiwi Maulana Brama Shandy Megantara, Nugraha Asthra MEI PARWANTO KURNIAWAN Miftah Alfian Firdausy Mochammad Yusa Mochammad Yusa Mochammad Yusa Mochammad Yusa, Mochammad Moh Muhtarom Mohammad Edi Monalisa Fatmawati Sarifah Moniva, Anip Mudawil Qulub Muh Adha Muh Adha Muh Wal Ikram Muh Wal Ikram Muhamad Arldi Megantara Muhamad Ekhsan Muhamad Fatahillah Z Muhamad Fatchan Muhamad Paliya Sadana Muhamad Ridwan Muhammad Agung Nugroho Muhammad Akbar Maulana Muhammad Altoumi Alsyaibani Muhammad Anwar Fauzi Muhammad Arfina Afwani Muhammad Bayu Kurniawan Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Ilyas Prakanada Muhammad Iqbal Muhammad Lathifuddin Arif Muhammad Paliya Sadana Muhammad Resa Arif Yudianto Muhammad Ricky Perdana Putra Muhammad Ricky Perdana Putra Muhammad Rizky Hajar Muhammad Rosikhu Muhammad Rusdi Rahman Muhammad Surahmanto Muhammad Suyanto Muhammad Syaiful Anam Muhammad Syukri Mustafa Muhammad Syukri Mustafa, Muhammad Syukri Mukhadimah Mukhlishah, Aiman Mursyid Ardiansyah Mutiara Dwi Anggraini NABILA OPER NAHROWI HAMDANI Nahrun Hartono Nahrun Hartono, Nahrun Nalda Kresimo Negoro Napianto, Riduwan Nasiri, Asro Ngaeni, Nurus Sarifatul Ngajiyanto, Ngajiyanto Ni Nyoman Utami Januhari Nita Helmawati Nova Agustina Nova Noor Kamala Sari Nugroho Setio Wibowo Nugroho, Jangkung Tri Nuk Ghurroh Setyoningrum Nur Hamid Sutanto Nur Hamid Sutanto Nur?aini, Nur?aini Nura Nugraha, Icha Nurcahyo, Azriel Christian Nurfaizah Nurfaizah Nurfajri Asfa Nurhasan Nugroho Nuri Cahyono Nurmasani, Atik Nurul Ilma Hasana Kunio Nurul Muttaqien Nurul Pratiwi, Annisa Octhavia Alin Okfan Rizal Ferdiansyah Oktariani, Deta Olivia Maria Inacio Tavares Omar Muhamammad Altoumi Alsyaibani Omar Muhammad Altoumi Alsyaibani Pangera, Abas Ali Patmawati Hasan Pebri Antara Prabowo Budi Utomo Pranata, Caraka Aji Prasetio, Agung Budi Prasetyo, Ade Prasetyo, Yoga Adi Pratama, Rendy Bagus Pratama, Zudha Prayoga, Dimas Prayoga, Mahendra Bayu pujiharto, eka wahyu Pulungan, Linda Nurul Taqwa Purnawan Purnawan Purnawan Purwidiantoro, Moch. Hari Purwoko, Agus Putu Putrayasa Qolbun Salim As Shidiqi Qolbun Salim As Shidiqi Quratul Ain Raden Bagus Bambang Sumantri Raditya Maulana Anuraga Rahardyan Bisma Setya Putra Rahmad Ardhani Rahmat Rahmat Rahmat Taufik R.L Bau Rahmatullah, Sidik Rakhma Shafrida Kurnia Ramadoni, Ramadoni Rantung, Tessa Vatma Rasyida, Zulfa Raynaldi Fatih Amanullah Resty Wulanningrum Reyhan Dwi Putra Reyhan Dwi Putra Rhomita Sari Ria Andriani Ricki Firmansyah Ricky Ferdiansyah Rifki Fahmi Rifqi Anugrah Rifqi Mizan Aulawi Riska K Abdullah Riska Kurniyanto Abdullah Risma, Vita Melati Rismayani Rismayani Riyanto Riyanto Rizki Firdaus Mulya Rizky Arya Kurniawan Rizky Handayani Rizky Handayani Rizqa Luviana Musyarofah Rodney Maringka Rodrigo Martínez Béjar Ronaldus Morgan James Roshandri, Wien Fitrian Roshandri, Wien Fitrian S, Muhammad Sabri Safor Madianto Saiful Bahri Salibana, Chlyfen Richard Samsul Bahri Samuel Adhi Bagaskoro Santi Santi Sapta Hary Surya Wibowo Saputra, Artha Gilang Saputra, Artha Gilang Sari, Rita Novita Sari, Yunita Sartika Sarkawi - Sartje Mala Rangkoly Satyo Widijanuarto Selamet Riadi Selvi Marcellia Selvy Megira Setiawan Budiman Setiawan, Bambang Abdi Setiawan, Hendi Setya Putra, Rahardyan Bisma Sidiq Wahyu Surya Wijaya Sigit Sugiyanto Sigit Suryono Siswo Utomo, Mardi Slameto, Andika Agus Sofyan Pariyasto Sofyawati, Siti Sri Hartati Sri Hartati Sri Yanto Qodarbaskoro Subastian Wibowo Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Sukoco Sukoco Sukoco Sukoco Sukrisno Amikom Suliswaningsih Suliswaningsih Suparyati Suparyati Supriadi, Oki Akbar Surya Ade Saputera Surya, Satria Dwi Suryono, Sigit Suryono, Wachid Daga Sutanto, Nur Hamid Sutrisno Sutrisno Suwanto Raharjo Suwanto Suwanto Suwondo, Adi Suyadi - Suyatmi Suyatmi Swastikawati, Claudia Syah, Firdiyan Syah, Firdiyan Syahrudin, Erwin Syarham, Syarham Tamaulina Br Sembiring Tamrizal A. M. Tamsir, Kurniawati Tantoni, Ahmad Tantoni, Ahmad Teguh Ansyor Lorosae Tigus Juni Betri Tika Novita Sari Tikasni, Elisa Tinuk Agustin Tommy Dwi Putra TONNY HIDAYAT Toto Indriyatmoko Toto Rusianto Tri Amri Wijaya Tri Yusnanto Triana Triana Tuhpatussania, Siti Tutut Maitanti Ulinuha, Hinova Rezha Utama, Hastari Veny Cahya Hardita Verra Budhi Lestari Vian Ardiyansyah Saputro Wahyu Ciptaningrum Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyudi Hidayat, Aji Said wahyuni, wenti ayu Wicaksono, Sherif Aji Widjiyati, Nur Wijaksana, Candra Putra Wijaya, Tri Amri Yans Safarid Hudha Yanuargi, Bayu Yaqin, Aiinul Yefta Tolla Yetman Erwadi Yohanes Aryo Bismo Raharjo Yosef Murya Kusuma Ardhana Yulianto Mustaqim Yulita Fatma Andriani Yumarlin MZ Yusa, Mochammad Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zulfa Rasyida Zulpan Hadi

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