Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

32.666

P-Index

This Author published in this journals

Ema Utami

Universitas Amikom Yogyakarta

Author-ID : 259410

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy 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

Published : 441 Documents Claim Missing Document

Claim Missing Document

Articles

41 42 43 44 45

Advancing Cross-Cultural Natural Language Processing with a Focus on Sundanese Language and Contextual Nuances Anggi Muhammad Rifai; Ema Utami; Amali Amali; Muhamad Fatchan; Muhamad Ekhsan
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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

The Sundanese language, as one of Indonesia’s regional tongues, holds deep cultural value but is still underrepresented in computational linguistics. The research addresses this gap by developing a translation model between Sundanese and Indonesian using a transformer-based sequence-to-sequence (Seq2Seq) architecture. With a parallel dataset of 3,616 sentence pairs, the model is fine-tuned to capture linguistic and contextual subtleties. The evaluation yields strong results: Bilingual Evaluation Understudy (BLEU) score of 44.12, Recall - Oriented Understudy for Gisting Evaluation (ROUGE)-1 F1-Score of 0.72, and ROUGE-L F1-Score of 0.71. Those demonstrate high translation quality despite limited data. Unlike earlier Sundanese translation studies that rely on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), or standard transformer models, this research uniquely leverages the multilingual pretrained M2M100 Transformer, enabling transfer learning from high-resource languages to improve low-resource performance. These outcomes highlight the model’s potential for real-world applications, such as translation tools for education and cultural exchange. The research emphasizes the importance of improving access to Sundanese texts and promoting its digital presence to aid in language preservation. Overall, the research not only advances Natural Language Processing (NLP) research for low-resource languages but also reinforces the importance of integrating regional languages like Sundanese into modern technology. Building upon prior studies on Indonesian–Sundanese translation, the research novelty lies in fine-tuning a multilingual Seq2Seq Transformer that captures both linguistic and contextual nuances, thereby setting a new benchmark for lowresource language processing.

Institutional and Individual Drivers of AI Adoption in Higher Education: An Integrative TAM–TOE Model Baiq Sri Mardiani; Ema Utami
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1470

The rapid diffusion of artificial intelligence (AI) in higher education necessitates a deeper understanding of both institutional and individual factors influencing its adoption, particularly in developing-country contexts. This study examines the drivers of AI adoption in Indonesian higher education institutions by integrating the Technology Acceptance Model (TAM) and the Technology, Organization, Environment (TOE) framework. Addressing a gap in prior research that often separates individual acceptance from institutional readiness, this study adopts a quantitative survey approach involving 366 academic stakeholders, including lecturers, students, and administrative staff. Data collected between October and December 2025 were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived ease of use strongly influences attitude toward AI, which in turn significantly affects behavioral intention. Perceived usefulness also has a positive, albeit weaker, effect on behavioral intention. At the institutional level, environmental context is found to significantly influence AI readiness, while other contextual factors exhibit limited explanatory power. Several hypothesized relationships, including the effects of AI readiness on perceived usefulness and the moderating roles of digital literacy and top management support, are not supported. These results suggest that AI adoption in higher education is primarily shaped by user-centered factors, while institutional readiness may depend on additional determinants not fully captured in the model. This study provides empirical insights into the role of AI readiness as an intermediate construct within an integrated TAM–TOE framework in higher education.

Effectiveness of the RIFE Algorithm in 3D Animation Motion Interpolation : A Case Study Approach Ramdhani, Arie Rahmat; Utami, Ema
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2569

This study evaluates the performance of the Real-Time Intermediate Flow Estimation (RIFE) algorithm in enhancing the temporal continuity and perceptual quality of 3D-rendered animation. Using 13 animation samples from the short film “I Draw It”, the algorithm was tested at 480p, 720p, and 1080p resolutions to interpolate motion from 24 fps to 48 fps, increasing temporal density while preserving visual coherence. The assessment integrates objective measures Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and processing time with expert-based evaluations using a Likert-scale instrument to capture perceptual fidelity and motion smoothness. Experimental results indicate that higher resolutions consistently provide superior interpolation stability and detail preservation, with 1080p yielding the highest perceptual performance despite increased computational requirements. Conversely, 720p demonstrates a practical trade-off between visual quality and processing efficiency, making it suitable for preview stages in production workflows. This research addresses a gap in the literature by conducting a resolution-specific evaluation of RIFE within fully 3D-rendered animation, a context that remains underexamined in existing studies. The findings establish RIFE as a viable and reliable interpolation method for modern animation pipelines and highlight its potential for integration into real-time frameworks such as Unreal Engine and Unity, thereby supporting improved production efficiency and visually consistent output.

Analysis of K-NN with the Integration of Bag of Words, TF-IDF, and N-Grams for Hate Speech Classification on Twitter Kuncoro Hadi; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23829

Social media has emerged as one of the primary communication channels in the modern world, but it has simultaneously become a platform where hate speech can spread easily. This study attempts to evaluate the performance of a hate speech classification model using the K-Nearest Neighbors (K-NN) algorithm along with various feature extraction techniques, specifically Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and N-Grams. The dataset used in this study consists of 13169 entries, which represent a diverse range of hate speech examples commonly encountered on social media platforms. In this experimental investigation, we assess the efficacy of the model using each feature extraction technique. The findings reveal that the K-NN model exhibits optimal performance when the k parameter is set to 3 (k=3). Under this configuration, the model achieves an accuracy of 86.88%, with a precision of 88.27%, a recall of 86.88%, and an F1-Score of 86.50%. These results show that the integration of TF-IDF feature extraction technique with K-NN algorithm produces superior performance in hate speech classification.

A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) Muhammad Ricky Perdana Putra; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24061

The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and NaÃ¯ve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%.

Analysis for Detecting Banana Leaf Disease Using the CNN Method Nita Helmawati; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24514

Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector.

A Multispectral YOLOv8-Based System for Real-Time Object Detection and Distance Estimation in Blind Navigation Ema Utami; Erwin Syahrudin; Anggit Dwi Hartanto; Suwanto Raharjo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28373

Developing reliable real-time navigation systems for visually impaired individuals remains challenging, particularly in dynamic and low-light environments. This study proposes an integrated framework combining YOLOv8, OpenCV-based monocular distance estimation, and RGB–NIR multispectral imaging to enhance detection robustness and distance awareness. A dataset of 1,700 annotated images collected from diverse indoor and outdoor environments was used for training and evaluation using preprocessing techniques such as resizing, normalization, and data augmentation. System performance was evaluated using Precision, Recall, F1-Score, mean Average Precision (mAP), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Frames Per Second (FPS). Experimental results show that YOLOv8x achieved the best performance with an F1-Score of 0.91, mAP@50 of 0.74, MAE of 0.15 m, RMSE of 0.20 m, and a processing speed of 22 FPS. Multispectral RGB–NIR integration further improved low-light performance, increasing the F1-Score from 0.83 to 0.89 and reducing MAE from 0.28 m to 0.19 m with only a minor reduction in speed. These findings demonstrate that the proposed system provides an effective balance between accuracy and real-time performance for assistive navigation applications.

A Hybrid Case-Based Reasoning Framework Using KNN, Word2Vec, and Cosine Similarity for Employee Attrition Analysis Akhmad Arif Faisal Siregar; Ema Utami; Tika Novita Sari
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28523

Employee attrition prediction remains a longstanding challenge in human resource analytics, as organizations increasingly depend on computational decision-support systems that are transparent, consistent, and operationally accountable. Conventional methods that rely solely on numerical attributes are restricted in their ability to accurately capture the structural and contextual relationships inherent in categorical and text-based employee descriptors. To overcome this limitation, the current study investigates a hybrid Case-Based Reasoning (CBR) retrieval framework that combines K-Nearest Neighbors (KNN) with Word2Vec embeddings derived from the dataset's limited textual attributes, specifically Department, Gender, EducationField, MaritalStatus, and OverTime. Eight experimental configurations were assessed to examine the impact of alternative similarity metrics and diverse feature representations. The optimal configuration of KNN, enhanced with Word2Vec embeddings and cosine similarity, attained an accuracy of 0.8526 and a weighted F1-score of 0.8000, thereby exceeding the performance of baseline models based solely on numerical features and those utilizing Manhattan distance. Nonetheless, the improvements in performance remained limited owing to dataset-specific limitations, such as class imbalance and the inherently superficial characteristics of the textual descriptors, which restrict the semantic richness of Word2Vec embeddings. Furthermore, the IBM attrition dataset does not encompass downsizing or termination situations, highlighting conceptual and ethical constraints when utilizing similarity-based predictions for high-stakes HR decisions. Overall, the findings indicate that hybrid similarity representations, particularly the combination of Word2Vec embeddings with cosine distance, can improve the structural expressiveness of CBR, although their predictive effectiveness is still limited by data sparsity and considerations of fairness.

EVALUATING POST-DIVORCE WOMEN'S AND CHILDREN'S RIGHTS FUNDING APPLICATION USING OWASP TOP TEN AND ISO 25010:2023 Deta Oktariani; Ema Utami
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 1 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9490

Evaluating an information system from both performance and security aspects is crucial for anticipating and improving the quality of the information system. A High Religious Court in collaboration with the Provincial Government developed a web-based application to support one of its services, to monitor court decisions regarding alimony payments from former husbands to former wives and children in divorce cases involving civil servants. This is certainly very important because before the existence of this application, there were many complaints filed due to the non-payment of alimony. To ensure that the application runs in accordance with its purpose and that the data is secure, a comprehensive system evaluation is required. The main objective of this evaluation is to identify vulnerabilities and their mitigations, as well as to ensure that the functions in the application work as expected, so that the application's goals are achieved. To achieve this goal, this study uses the ISO 25010:2023 information system standard integrated with OWASP Top Ten to evaluate its security This study uses five ISO 25010:2023 characteristics selected according to the system's goals. The results show that the combination of ISO 25010:2023 and OWASP Top Ten effectively identifies vulnerabilities in the application's functions and security comprehensively. Overall, the functions in the application have run as expected, although there are still several things that need to be improved to enhance the quality and secure its data.

A Memory-Efficient and Gradient-Stable Lightweight ANFIS for Real-Time Humidity Prediction in Precision Agriculture Eddy Nurraharjo; Ema Utami; Kusrini; Kumara Ari Yuana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2700

Precision agriculture demands artificial intelligence solutions that are both accurate and deployable on resource-constrained hardware, yet conventional machine learning models require excessive memory while traditional ANFIS architectures suffer from training instability. This study developed a memory-efficient and gradient-stable lightweight Adaptive Neuro-Fuzzy Inference System (ANFIS) for real-time humidity prediction on microcontroller-class devices. The proposed architecture strategically reduced the rule base from 27 to only 4 interpretable fuzzy rules and limited membership functions to two per input, achieving an 85.2% reduction in learnable parameters. A gradient-stable training mechanism was introduced, combining physics-informed parameter initialization with adaptive gradient clipping to prevent gradient explosion. The model was trained and validated using 31,474 real-world greenhouse samples collected over 218 days, with 80% allocated for training and 20% for temporal testing. Experimental results demonstrated that the gradient-stable architecture successfully converged from a catastrophic R² of -64.08 to 0.9148, with a root mean square error of 1.32% and mean absolute error of 1.05%. The model required only 0.211 KB of memory, representing a 99.9% reduction compared to baseline Random Forest models, while achieving inference time of 8.2 milliseconds on Arduino UNO. The system was successfully deployed on three independent hardware modules, maintaining consistent performance with average RMSE of 1.99% over 168 hours of continuous operation. This study concludes that strategic simplification and stability-aware training enable interpretable neuro-fuzzy systems to operate effectively on ultra-low-resource devices, bridging the gap between predictive accuracy and hardware feasibility in embedded agricultural IoT applications.

Co-Authors , Anggit Dwi Hartanto A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdul Malik Zuhdi Abdullah Ardi Abdullah, Riska K Abdulrahmat E Ahmad Abyan Fauzi Widihasani Achmad Yusron Arif Ade Pujianto Adi Surya Adiatma, Biva Candra Lutfi Adipradana, Candra 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 Agustina, Nova 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 Akbar Akbar Akhmad Arif Faisal Siregar Akhmad Dahlan Al Fathir As, Rahmat Saudi Aldy A Kulakat Alfansani, Abdul Rauf Alfin Mahadi Alimuddin Yasin Alin, Octhavia 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 Anggit Hartanto 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 Arvi Pramudyantoro Arwin, Dwi Septiyani 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 Bambang Sumantri, R Bagus Bangun Watono Banu Dwi Putranto Basri, Nur Faizal Bayu Setiaji Béjar, Rodrigo Martínez 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 Yuli Prasetyo Dzulhijjah, Dwi Ahmad 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 Ermawan, Bagas Restya Erwin Syahrudin Erwin Syahrudin 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 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 Ghifari, Dloifur Rohman Al Gusti F Rahman Gusti Fathur Rakhman Habib, Muhammad Hafidh Rezha Maulana Hafidz Sanjaya Hafidz Sanjaya, Hafidz Hafiz Ridha Pramudita Hafiz Ridha Pramudita, Hafiz Ridha Hajar, Muhammad Rizky 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 . Hendi Muhammad, Alva HENDRA SETIAWAN Hendrawan, Ivan Rifky Hendrik Setiawan, Hendrik Herda Dicky Ramandita Herlandro Tribiakto Hidayat, Jati Arif Hikmianto, Riki Hirmayanti Hirmayanti, Hirmayanti Hudha, Yans Safarid I Dewa Bagas Suryajaya, I Dewa Bagas I Made Adi Purwantara I Wayan Rangga Pinastawa Idris Idris Idris Idris Ikrahmi Ikrahmi Imam Ainuddin P Ina Sholihah Widiati, Ina Sholihah Indarto Indarto Irawan, Ridwan Dwi Irawan, Rio 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 Kuncoro Hadi Kuncoro Hadi Kurniawan, Muhammad Bayu 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 Marianti, Dina Juni Maringka, Raissa 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 Muhajir, Fadhil Muhamad Arldi Megantara Muhamad Ekhsan Muhamad Fatahillah Z Muhamad Fatchan Muhamad Paliya Sadana Muhamad Ridwan Muhammad Akbar Maulana Muhammad Altoumi Alsyaibani Muhammad Anwar Fauzi Muhammad Arfina Afwani Muhammad Fadli Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Ilyas Prakanada Muhammad Iqbal Muhammad Lathifuddin Arif Muhammad Noor Arridho Muhammad Noor Arridho Muhammad Paliya Sadana Muhammad Resa Arif Yudianto Muhammad Ricky Perdana Putra Muhammad Ricky Perdana Putra Muhammad Rosikhu Muhammad Rusdi Rahman Muhammad Surahmanto Muhammad Suyanto, Muhammad 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, Ni Nyoman Nita Helmawati Nita Helmawati Nova Agustina Nova Noor Kamala Sari Nugroho Setio Wibowo Nugroho, Jangkung Tri Nugroho, Muhammad Agung Nuk Ghurroh Setyoningrum 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 Pratiwi, Annisa 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 Pramudyantoro, Arvi 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 Purwidiantoro, Moch. Hari Purwoko, Agus Putu Putrayasa Qolbun Salim As Shidiqi Qolbun Salim As Shidiqi Quratul Ain Raditya Maulana Anuraga Rahardyan Bisma Setya Putra Rahmad Ardhani Rahmat Rahmat Rahmat Taufik R.L Bau Rahmatullah, Sidik Rakhma Shafrida Kurnia Ramadoni, Ramadoni Ramdhani, Arie Rahmat Rantung, Tessa Vatma Rasyida, Zulfa Raynaldi Fatih Amanullah Resty Wulanningrum Reyhan Dwi Putra Reyhan Dwi Putra Rhomita Sari Ria Andriani Rianansyah, Anggit Ricki Firmansyah Rifki Fahmi Rifqi Anugrah Rifqi Mizan Aulawi Riska Kurniyanto Abdullah Risma, Vita Melati Rismayani Rismayani Riyanto Riyanto Rizki Firdaus Mulya Rizky Arya Kurniawan Rizky Handayani Rizky Handayani Rizqa Luviana Musyarofah Rizy, M. Alfa Rodney Maringka Ronaldus Morgan James Roshandri, Wien Fitrian Roshandri, Wien Fitrian S, Muhammad Sabri Sabar, Alfrida Safor Madianto Saiful Bahri Salibana, Chlyfen Richard Samsul Bahri Samuel Adhi Bagaskoro Sapta Hary Surya Wibowo Saputra, Artha Gilang Saputra, Artha Gilang Sari, Rita Novita Sari, Yunita Sartika Sarkawi - Sartje Mala Rangkoly Sasoko, Wasis Haryo 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 Sodikin, Muh Ikbal Sofyan Pariyasto Sofyawati, Siti Sri Hartati Sri Hartati Sri Wahyuni 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 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 Triwerdaya, Aji 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 Widijanuarto, Satyo 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 Zakaria, Fariz Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zulfa Rasyida Zulpan Hadi

Title

Found 441 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 441 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 441 Documents
Search