Radhiyatul Fajri

@brin.go.id

Radhiyatul Fajri


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https://researchid.co/radhiyatulfajri
16

Scopus Publications

62

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Maintaining Academic Integrity in the Era of Large Language Models: A Guideline for Responsible Prompt Engineering
    Nuraisa Novia Hidayati, Agung Santosa, Elvira Nurfadhilah, Andi Djalal Latief, Kokoy Siti Komariah, Asril Jarin, Siska Pebiana, Yuyun Wabula, Radhiyatul Fajri, Tri Sampurno
    Advanced AI and Prompt Engineering Techniques and Resources, 2025
    This chapter provides comprehensive guidance for academic researchers on effectively integrating Large Language Models (LLMs) in research workflows. Beginning with technical foundations and capabilities, it examines LLMs' architecture, training mechanisms, and specific applications in academic tasks such as text summarization, literature review, data analysis, and code generation. The chapter then offers detailed criteria for model selection and presents advanced prompt engineering techniques, including specificity guidelines, constraint formatting, chain-of-thought prompting, and in-context learning approaches. Particular attention is given to ensuring academic integrity through robust reasoning frameworks, validation protocols, and ethical considerations regarding plagiarism and transparency. The chapter concludes with systematic approaches to critical evaluation, including quality assessment criteria, adapted peer review processes, and standardized documentation practices for LLM implementation in academic research.
  • Strategies for Addressing the Limited Labeled Datasets in Fake News Detection: A Systematic Review
    Yaniasih Yaniasih, Asril Jarin, Andi Djalal Latief, Dian Isnaeni Nurul Afra, Elvira Nurfadhilah, Gita Citra Puspita, Hayuning Titi Karsanti, Nuraisa Novia Hidayati, Radhiyatul Fajri, Siska Pebiana, Siti Shaleha
    SN Computer Science, 2025
  • Prompt-Based Assessment of Generative Artificial Intelligence Tools for Scholarly Use
    Yaniasih Yaniasih, Ambar Yoganingrum, Radhiyatul Fajri
    International Conference on Computer Control Informatics and Its Applications Ic3ina, 2025
    This study evaluates ten generative AI applications based on their capabilities to support academic research tasks. The assessment centers on five main academic functions: literature search and summarization, conceptual understanding and scientific reasoning, citation and referencing, academic writing, and logical and scientific validity evaluation. Each application was tested using prompt-based tasks designed to assess these capabilities. Six criteria were used to evaluate responses, namely relevance, completeness, academic style, coherence, transparency/verifiability of sources, and factual accuracy. The result showed academic-purpose AI tools such as Consensus, Perplexity, and SciSpace scored higher in most categories. In contrast, general-purpose models like ChatGPT, Gemini, and DeepSeek demonstrated strong academic tone and coherence but often lacked verifiable sources and accurate references. Consensus and Perplexity emerged as top performers, suggesting that domain-specific design significantly enhances academic reliability. The discussion highlights the risks of hallucination in general-purpose models and the advantages of utilizing specific AI tools for academic workflows. The study concludes that while large language models offer valuable support for academic tasks, selecting purpose-built scholarly AI tools is critical to maintain research integrity. Future research may explore performance variations across academic disciplines and non-English contexts. These findings provide practical guidance for researchers and institutions, and contribute to the discourse on ethical and practical AI integration in academia.
  • Evaluating Retrieval Augmented Generation (RAG) Chunking Strategy for Question Answering in Indonesian Law of The Sea
    Iftitahu Ni’Mah, Lyla Ruslana Aini, Radhiyatul Fajri, Siska Pebiana, Nuraisa Novia Hidayati, Rini Wijayanti
    International Conference on Computer Control Informatics and Its Applications Ic3ina, 2025
    We study the evaluation of Retrieval-Augmented Generation (RAG) for question and answering (QA) in legal domain, particularly Law No. 17 of 2008 about maritime law and shipping in Indonesia. First, we create document contexts for RAG pipeline by extracting text from a legal PDF document and segmenting it into text chunks based on three methods: (i) Naive Token-level Chunking; (ii) Hierarchical Sentence-level Chunking; and (iii) Recursive Chunking. Simultaneously, we construct QA pairs from the legal PDF as an evaluation dataset for our RAG pipeline. We further analyze the strengths and limitations of currently available RAG evaluation metrics for investigating three evaluation aspects: Answer Correctness, Answer Groundedness, and Quality of Retrieved Chunks on our constructed QA dataset.
  • Utilization of Large Language Models for NER Tasks with Synonym-Based Text Generation in Health News Classification
    Nur Annisa Putri Rezkia, Nazla Dzaalika Ainaya, Radhiyatul Fajri, Ekasari Nugraheni, Aang Nuryaman, Netti Herawati
    International Conference on Computer Control Informatics and Its Applications Ic3ina, 2025
    The imbalanced of data is a key challenge in classifying events in health news texts. The disparity in the number of instances between event and non-event classes can degrade model performance, particularly in deep learning systems. This study employs two dataset scenarios. The first scenario uses InaCOVED data without augmentation (imbalanced dataset), while the second leverages Named Entity Recognition (NER) including Person, Organization, Location, Disease, and Others entities obtained from a Large Language Model (LLM) using a Few-Shot approach, followed by synonym based text generation using the Kateglo API (balanced dataset). The research employs a tri-architecture evaluation methodology consisting of fully connected (MLP), convolutional (CNN), and recurrent (LSTM) neural network designs. Evaluation results show that all three models perform better on the balanced dataset generated through LLM based NER and augmentation. The best performance for each model is as follows: MLP achieved an accuracy of 98.11%, CNN reached 97.66%, and LSTM recorded 97.58%. These results indicate that NER labeling assisted by LLMs, combined with synonym-based augmentation to balance the data improves text classification performance across all model types.
  • Revolutionizing Language Learning and Proficiency With Adaptive Assessments and AI-Driven System
    Elvira Nurfadhilah, Dian Isnaeni Nurul Afra, Prabu Kresna Putra, Nimas Ayu Untariyati, Radhiyatul Fajri, Siska Pebiana, Hammam Riza
    AI Powered Solutions for Bilingual Proficiency and Communication, 2025
    Bilingual and multilingual education integrated with artificial intelligence (AI) has fundamentally altered conventional education paradigms, increasing the engagement of learning, individualized, and adaptive. This chapter examines AI technologies for instance Intelligent Tutoring Systems (ITS), adaptive learning algorithms, and Natural Language Processing (NLP) aid language acquisition by personalizing material delivery, offering real-time feedback, gamification, and promoting cultural competence. AI-driven curriculum caters to a variety of learning styles and boost understanding, motivation, and retention while addressing cognitive problems. However, issues such as algorithmic prejudice, data privacy, and the digital divide need fair technological access and ethical considerations. The balance of AI and human supervision is critical to developing effective learning experiences. Future breakthroughs in agentic AI will provide adaptive, context-aware interactions that improve language learning, but they must be carefully implemented to ensure inclusivity and ethical integrity.
  • Feature Selection and Performance Evaluation of Buzzer Classification Model
    Dian Isnaeni Nurul Afra, Radhiyatul Fajri, Harnum Annisa Prafitia, Ikhwan Arief, Aprinaldi Jasa Mantau
    Jurnal Optimasi Sistem Industri, 2024
    In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.
  • Twitter dataset on public sentiments towards biodiversity policy in Indonesia
    Mohammad Teduh Uliniansyah, Indra Budi, Elvira Nurfadhilah, Dian Isnaeni Nurul Afra, Agung Santosa, Andi Djalal Latief, Asril Jarin, Gunarso, Meganingrum Arista Jiwanggi, Nuraisa Novia Hidayati, Radhiyatul Fajri, Ryan Randy Suryono, Siska Pebiana, Siti Shaleha, Tosan Wiar Ramdhani, Tri Sampurno
    Data in Brief, 2024
    In recent years, biodiversity has emerged as a prominent and pressing topic due to the urgent need to address biodiversity loss and the recognition of its connections to climate change and sustainable development. Additionally, increased public awareness and the consideration of economic factors have further underscored the significance of biodiversity conservation. To investigate the sentiment of the Indonesian people towards biodiversity, we conducted a comprehensive data collection on Twitter, focusing on keywords we have set. We amassed a substantial dataset of 500,000 Indonesian tweets from January 2020 to March 2023. These tweets encompassed a wide range of discussions on biodiversity, including its subdomains such as food security, health, and environmental management. Three annotators labeled each tweet with a sentiment class (positive, negative, neutral), or label none for unrelated tweet. The final label was determined using the majority voting method. The tweets with the final label none and those with undecided sentiment class were considered invalid and excluded in the subsequent process. Before labeling, a team of 18 experts jointly developed a labeling guide. This document served as a reference in labeling. After going through a series of processes, including cleaning (removing duplications, irrelevant tweets, and tweets written other than in Indonesian) and preprocessing, we prepared a dataset containing 13,435 tweets. We measured the inter-annotator agreement level, made several models using different algorithms and the K-Fold cross-validation method, and evaluated the models. The Fleiss' Kappa value of the dataset was 0.62187 as the value of the inter-annotator agreement level, and the F1-score value with the best model using the pre-trained IndoBERT model was 0.7959. The Fleiss' Kappa and F1-score values suggest that the annotators have a substantial comprehension and agreement of how to label a tweet, thus ensuring consistency and reliability of our dataset, and the reusability of our dataset is quite suitable for further research on sentiment analysis on biodiversity, respectively. This dataset will benefit various research, including topic modeling, sentiment analysis, public opinion analysis on Twitter, etc., especially biodiversity-related policies.
  • Decoding the Twitter Tapestry: A Comprehensive Analysis of Public Sentiment and Engagement in Supply Chain Discourse
    Ahmad Syafruddin Indrapriyatna, Asep Kuswandi Supriatna, Yedi Purwanto, Ikhwan Arief, Radhiyatul Fajri
    2nd International Symposium on Information Technology and Digital Innovation Creative Trends in Sustainable Information Technology Design and Innovation Isitdi 2024, 2024
    The study delves into the Twitter conversations about supply chain. It fills a notable void in existing scholarly work by examining the subtle shades of public opinion and how deeply people are involved with related subjects on social networks. Twitter users and organize the theme in supply chain conversations. The investigation meticulously reviewed tweets within a two-month window, utilizing a solid approach based on natural language processing and data analysis, this included assessing the sentiment, measuring the levels of interaction, and analyzing user conduct. We have sophisticated techniques in data illustration that were used to make complex data patterns easily understandable. The research found that the general tone of supply chain discussions was mostly impartial, underscoring that interaction metrics tended towards less active forms of engagement, with users showing a preference for endorsing posts with likes over responses or shares. The study also spotlighted influential individuals leading these online conversations and shed light on the main thematic issues, with an analysis of keywords bringing prevalent words and expressions to the fore. The findings aid in the tactical shaping of communication and engagement approaches for supply chains, underscoring the need to adjust to the changing patterns of digital dialogue.
  • End-to-End Phoneme Recognition in Bahasa Indonesia with Pretrained Speech Embeddings and 1D-CNN Using CTC
    Agung Santosa, Asril Jarin, Elvira Nurfadhilah, Mohammad Teduh Uliniansyah, Tri Sampurno, Radhiyatul Fajri
    International Conference on Computer Control Informatics and Its Applications Ic3ina, 2024
    Phoneme recognition in low-resource languages like Bahasa Indonesia is hindered by the limited availability of annotated datasets and linguistic resources. This study proposes an end-to-end phoneme recognition system for Bahasa Indonesia using pre-trained speech embeddings from Wav2Vec2 and a one-dimensional Convolutional Neural Network (1D-CNN) with Connectionist Temporal Classification (CTC). The model is compared against a baseline employing fully connected layers. Our findings highlight the effectiveness of integrating pre-trained speech embeddings and CTC in improving phoneme recognition accuracy. The proposed models utilizing a 1D-CNN kernel size of 9 achieve a phoneme error rate (PER) of 5.27 after 100 epochs and 4.89 after 200 epochs of training, using 156.1 hours of the training dataset.
  • Latest Research in Data Augmentation for Low Resource Language Text Translation: A Review
    Andi Djalal Latief, Asril Jarin, Yaniasih Yaniasih, Dian Isnaeni Nurul Afra, Elvira Nurfadhilah, Siska Pebiana, Nuraisa Novia Hidayati, Radhiyatul Fajri
    International Conference on Computer Control Informatics and Its Applications Ic3ina, 2024
  • Artificial Intelligence Technologies in Mental Health: Transforming Depression Care Through Innovation
    Elvira Nurfadhilah, Ambar Yoganingrum, Andi Djalal Latief, Armita Widyasuri, Asril Jarin, Dian Isnaeni Nurul Afra, Gunarso Gunarso, Kokoy Siti Komariah, Mohammad Teduh Uliniansyah, Nimas Ayu Untariyati, Nuraisa Novia Hidayati, Radhiyatul Fajri, Retno Anggreini Dyah Ayuningtias, Siska Pebiana, Yaniasih Yaniasih, Yuyun Yuyun, Hayuning Titi Karsanti, Gita Citra Puspita
    Humanizing Technology with Emotional Intelligence, 2024
  • The Impact of Downsampling Methods on Face Recognition in Electronic Identity Card
    Muhammad Nurkhoiri Hindratno, Auliati Nisa, Muhammad Imaduddin Abdur Rohim, Radhiyatul Fajri, Mohammad Hamdani, Gembong Satrio Wibowanto, Nova Hadi Lestriandoko, Pesigrihastamadya Normakristagaluh
    Proceedings 2023 10th International Conference on Computer Control Informatics and Its Applications Exploring the Power of Data Leveraging Information to Drive Digital Innovation Ic3ina 2023, 2023
  • Performance Face Image Quality Assessment under the Difference of Illumination Directions in Face Recognition System using FaceQnet, SDD-FIQA, and SER-FIQ
    Auliati Nisa, Radhiyatul Fajri, Erwin Nashrullah, Fandy Harahap, Junanto Prihantoro, Gembong Wibowanto, Jemie Muliadi, Anto Nugroho
    ACM International Conference Proceeding Series, 2022
  • Developing Sentiment Analysis of Indonesian Social Media Based on Convolutional Neural Network for Smarter Society
    Dian Isnaeni Nurul Afra, Agung Santosa, Radhiyatul Fajri, Nuraisa Novia Hidayati, Elvira Nurfadhilah, Siska Pebiana, Lyla Ruslana Aini, Harnum Annisa Prafitia, Yosi Sahreza, Junanto Prihantoro, Gunarso, Andi Djalal Latief, M. Teduh Uliniansyah, Hammam Riza
    9th International Conference on ICT for Smart Society Recover Together Recover Stronger and Smarter Smartization Governance and Collaboration Iciss 2022 Proceeding, 2022
  • Experimentation of Various Preprocessing Pipelines for Sentiment Analysis on Twitter Data about New Indonesia's Capital City Using SVM and CNN
    Siska Pebiana, Nuraisa Novia Hidayati, Dian Isnaeni Nurul Afra, Elvira Nurfadhilah, Harnum Annisa Prafitia, Junanto Prihantoro, Radhiyatul Fajri, M. Teduh Uliniansyah, Agung Santosa, Lyla Ruslana Aini, Yosi Sahreza, Aulia Haritsuddin Karisma Muhammad Subekti, Josua Geovani Pinem, Muhammad Reza Alfin, Agung Septadi, Siti Shaleha, Gembong Satrio Wibowanto, Asril Jarin, Gunarso, Andi Djalal Latief, Hammam Riza
    2022 25th Conference of the Oriental Cocosda International Committee for the Co Ordination and Standardisation of Speech Databases and Assessment Techniques O Cocosda 2022 Proceedings, 2022

RECENT SCHOLAR PUBLICATIONS

  • Revolutionizing Language Learning and Proficiency With Adaptive Assessments and AI-Driven System
    E Nurfadhilah, DIN Afra, PK Putra, NA Untariyati, R Fajri, S Pebiana, ...
    AI-Powered Solutions for Bilingual Proficiency and Communication, 311-376 , 2026
    2026
  • Maintaining Academic Integrity in the Era of Large Language Models: A Guideline for Responsible Prompt Engineering
    NN Hidayati, A Santosa, E Nurfadhilah, AD Latief, KS Komariah, A Jarin, ...
    Advanced AI and Prompt Engineering Techniques and Resources, 121-170 , 2026
    2026
  • Utilization of Large Language Models for NER Tasks with Synonym-Based Text Generation in Health News Classification
    NAP Rezkia, ND Ainaya, R Fajri, E Nugraheni, A Nuryaman, N Herawati
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Prompt-Based Assessment of Generative Artificial Intelligence Tools for Scholarly Use
    Y Yaniasih, A Yoganingrum, R Fajri
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Evaluating Retrieval Augmented Generation (RAG) Chunking Strategy for Question Answering in Indonesian Law of The Sea
    I Ni’Mah, LR Aini, R Fajri, S Pebiana, NN Hidayati, R Wijayanti
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Maintaining Academic Integrity in the Era of
    AD Latief, R Fajri
    Advanced AI and Prompt Engineering Techniques and Resources, 121 , 2025
    2025
  • Strategies for Addressing the Limited Labeled Datasets in Fake News Detection: A Systematic Review
    Y Yaniasih, A Jarin, AD Latief, DI Nurul Afra, E Nurfadhilah, GC Puspita, ...
    SN Computer Science 6 (6), 725 , 2025
    2025
    Citations: 1
  • Artificial intelligence technologies in mental health: Transforming depression care through innovation
    E Nurfadhilah, A Yoganingrum, AD Latief, A Widyasuri, A Jarin, DIN Afra, ...
    Humanizing Technology With Emotional Intelligence, 219-262 , 2025
    2025
    Citations: 1
  • Feature Selection and Performance Evaluation of Buzzer Classification Model
    DIN Afra, R Fajri, HA Prafitia, I Arief, AJ Mantau
    Jurnal Optimasi Sistem Industri 23 (1), 1-14 , 2024
    2024
    Citations: 3
  • End-to-end phoneme recognition in bahasa indonesia with pretrained speech embeddings and 1d-cnn using ctc
    A Santosa, A Jarin, E Nurfadhilah, MT Uliniansyah, T Sampurno, R Fajri
    2024 International Conference on Computer, Control, Informatics and its … , 2024
    2024
    Citations: 2
  • Latest research in data augmentation for low resource language text translation: A review
    AD Latief, A Jarin, Y Yaniasih, DIN Afra, E Nurfadhilah, S Pebiana, ...
    2024 international conference on computer, control, informatics and its … , 2024
    2024
    Citations: 5
  • Decoding the Twitter Tapestry: A Comprehensive Analysis of Public Sentiment and Engagement in Supply Chain Discourse
    AS Indrapriyatna, AK Supriatna, Y Purwanto, I Arief, R Fajri
    2024 2nd International Symposium on Information Technology and Digital … , 2024
    2024
  • Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution
    MIA Rohim, A Nisa, MN Hindratno, R Fajri, GS Wibowanto, ...
    Jurnal Teknologi Informasi dan Ilmu Komputer 11 (1), 199-208 , 2024
    2024
    Citations: 2
  • Twitter dataset on public sentiments towards biodiversity policy in Indonesia
    MT Uliniansyah, I Budi, E Nurfadhilah, DIN Afra, A Santosa, AD Latief, ...
    Data in Brief 52, 109890 , 2024
    2024
    Citations: 22
  • The impact of downsampling methods on face recognition in electronic identity card
    MN Hindratno, A Nisa, MIA Rohim, R Fajri, M Hamdani, GS Wibowanto, ...
    2023 International Conference on Computer, Control, Informatics and its … , 2023
    2023
    Citations: 3
  • Experimentation of various preprocessing pipelines for sentiment analysis on twitter data about new indonesia’s capital city using svm and cnn
    S Pebiana, NN Hidayati, DIN Afra, E Nurfadhilah, HA Prafitia, J Prihantoro, ...
    2022 25th Conference of the Oriental COCOSDA International Committee for the … , 2022
    2022
    Citations: 13
  • Performance face image quality assessment under the difference of illumination directions in face recognition system using FaceQNet, SDD-FIQA, and SER-FIQ
    A Nisa, R Fajri, E Nashrullah, F Harahap, J Prihantoro, G Wibowanto, ...
    Proceedings of the 2022 International Conference on Computer, Control … , 2022
    2022
    Citations: 3
  • Developing Sentiment Analysis of Indonesian Social Media Based on Convolutional Neural Network for Smarter Society
    DIN Afra, A Santosa, R Fajri, NN Hidayati, E Nurfadhilah, S Pebiana, ...
    2022 International Conference on ICT for Smart Society (ICISS), 1-7 , 2022
    2022
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • Twitter dataset on public sentiments towards biodiversity policy in Indonesia
    MT Uliniansyah, I Budi, E Nurfadhilah, DIN Afra, A Santosa, AD Latief, ...
    Data in Brief 52, 109890 , 2024
    2024
    Citations: 22
  • Experimentation of various preprocessing pipelines for sentiment analysis on twitter data about new indonesia’s capital city using svm and cnn
    S Pebiana, NN Hidayati, DIN Afra, E Nurfadhilah, HA Prafitia, J Prihantoro, ...
    2022 25th Conference of the Oriental COCOSDA International Committee for the … , 2022
    2022
    Citations: 13
  • Developing Sentiment Analysis of Indonesian Social Media Based on Convolutional Neural Network for Smarter Society
    DIN Afra, A Santosa, R Fajri, NN Hidayati, E Nurfadhilah, S Pebiana, ...
    2022 International Conference on ICT for Smart Society (ICISS), 1-7 , 2022
    2022
    Citations: 7
  • Latest research in data augmentation for low resource language text translation: A review
    AD Latief, A Jarin, Y Yaniasih, DIN Afra, E Nurfadhilah, S Pebiana, ...
    2024 international conference on computer, control, informatics and its … , 2024
    2024
    Citations: 5
  • Feature Selection and Performance Evaluation of Buzzer Classification Model
    DIN Afra, R Fajri, HA Prafitia, I Arief, AJ Mantau
    Jurnal Optimasi Sistem Industri 23 (1), 1-14 , 2024
    2024
    Citations: 3
  • The impact of downsampling methods on face recognition in electronic identity card
    MN Hindratno, A Nisa, MIA Rohim, R Fajri, M Hamdani, GS Wibowanto, ...
    2023 International Conference on Computer, Control, Informatics and its … , 2023
    2023
    Citations: 3
  • Performance face image quality assessment under the difference of illumination directions in face recognition system using FaceQNet, SDD-FIQA, and SER-FIQ
    A Nisa, R Fajri, E Nashrullah, F Harahap, J Prihantoro, G Wibowanto, ...
    Proceedings of the 2022 International Conference on Computer, Control … , 2022
    2022
    Citations: 3
  • End-to-end phoneme recognition in bahasa indonesia with pretrained speech embeddings and 1d-cnn using ctc
    A Santosa, A Jarin, E Nurfadhilah, MT Uliniansyah, T Sampurno, R Fajri
    2024 International Conference on Computer, Control, Informatics and its … , 2024
    2024
    Citations: 2
  • Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution
    MIA Rohim, A Nisa, MN Hindratno, R Fajri, GS Wibowanto, ...
    Jurnal Teknologi Informasi dan Ilmu Komputer 11 (1), 199-208 , 2024
    2024
    Citations: 2
  • Strategies for Addressing the Limited Labeled Datasets in Fake News Detection: A Systematic Review
    Y Yaniasih, A Jarin, AD Latief, DI Nurul Afra, E Nurfadhilah, GC Puspita, ...
    SN Computer Science 6 (6), 725 , 2025
    2025
    Citations: 1
  • Artificial intelligence technologies in mental health: Transforming depression care through innovation
    E Nurfadhilah, A Yoganingrum, AD Latief, A Widyasuri, A Jarin, DIN Afra, ...
    Humanizing Technology With Emotional Intelligence, 219-262 , 2025
    2025
    Citations: 1
  • Revolutionizing Language Learning and Proficiency With Adaptive Assessments and AI-Driven System
    E Nurfadhilah, DIN Afra, PK Putra, NA Untariyati, R Fajri, S Pebiana, ...
    AI-Powered Solutions for Bilingual Proficiency and Communication, 311-376 , 2026
    2026
  • Maintaining Academic Integrity in the Era of Large Language Models: A Guideline for Responsible Prompt Engineering
    NN Hidayati, A Santosa, E Nurfadhilah, AD Latief, KS Komariah, A Jarin, ...
    Advanced AI and Prompt Engineering Techniques and Resources, 121-170 , 2026
    2026
  • Utilization of Large Language Models for NER Tasks with Synonym-Based Text Generation in Health News Classification
    NAP Rezkia, ND Ainaya, R Fajri, E Nugraheni, A Nuryaman, N Herawati
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Prompt-Based Assessment of Generative Artificial Intelligence Tools for Scholarly Use
    Y Yaniasih, A Yoganingrum, R Fajri
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Evaluating Retrieval Augmented Generation (RAG) Chunking Strategy for Question Answering in Indonesian Law of The Sea
    I Ni’Mah, LR Aini, R Fajri, S Pebiana, NN Hidayati, R Wijayanti
    2025 International Conference on Computer, Control, Informatics and its … , 2025
    2025
  • Maintaining Academic Integrity in the Era of
    AD Latief, R Fajri
    Advanced AI and Prompt Engineering Techniques and Resources, 121 , 2025
    2025
  • Decoding the Twitter Tapestry: A Comprehensive Analysis of Public Sentiment and Engagement in Supply Chain Discourse
    AS Indrapriyatna, AK Supriatna, Y Purwanto, I Arief, R Fajri
    2024 2nd International Symposium on Information Technology and Digital … , 2024
    2024