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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PRoMEDIA
fitri noor Febriana
Universitas Airlangga
Computer Science & IT Engineering Languange, Linguistic, Communication & Media

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Transformasi Media Penyiaran Digital dalam Strategi Komunikasi Publik: Sebuah Tinjauan Literatur Sistematis Dimas Sulthon Ubaidillah Lubis; fitri noor Febriana; sendy ayu mitra uktutias; Achmad Zaky Faiz; Ansell Cohen Harjono
PRoMEDIA Vol 12, No 1 (2026): PROMEDIA
Publisher : UNIVERSITAS 17 AGUSTUS 1945 JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52447/promedia.v12i1.8699

Abstract

Penelitian ini bertujuan untuk memetakan dan menyintesis lanskap penelitian mengenai perpotongan antara transformasi penyiaran digital dengan strategi komunikasi publik, mengkaji bagaimana Media Layanan Publik (PSM) beradaptasi terhadap disrupsi teknologi sambil mempertahankan mandatnya. Metode yang digunakan adalah Tinjauan Literatur Sistematis (SLR) mengikuti pedoman PRISMA. Pencarian komprehensif pada basis data utama menghasilkan 20 studi relevan yang dianalisis menggunakan sintesis tematik kualitatif. Hasil utama mengidentifikasi enam tema kunci, termasuk evolusi penyiaran ke ekosistem Over-the-Top (OTT), adaptasi komunikasi ke model partisipatif, dan transformasi organisasional melalui konvergensi ruang redaksi. Temuan menunjukkan bahwa adopsi teknologi berjalan cepat, namun adaptasi strategis dan organisasional sering kali lebih lambat dan kompleks. Orisinalitas studi ini terletak pada sintesis komprehensifnya yang menghubungkan inovasi teknologi, strategi, dan organisasi. Nilainya adalah menyediakan panduan berbasis bukti bagi praktisi dan pembuat kebijakan, serta mengidentifikasi arah penelitian masa depan mengenai model monetisasi berkelanjutan dan implikasi etis dari teknologi baru.
Benchmarking Machine Learning Paradigms for Resume Screening on Imbalanced Data Fitri Noor Febriana; Ira Puspitasari
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.v9i6.7123

Abstract

Manual resume screening is an inefficient and bias-prone process, yet comprehensive benchmarks of machine learning models on imbalanced, real-world recruitment data remain scarce. This study addresses this gap by benchmarking seven models from classical, ensemble, and deep learning paradigms for automated resume classification. Using a private dataset of 2,483 resumes across 24 job categories, this study evaluates the models with distinct TF-IDF and BERT embedding feature pipelines and an adaptive strategy for handling class imbalance (Class Weights, SMOTE, SMOTEENN). The results showed that the XGBoost model achieved the highest performance (weighted F1-score of 0.779), followed by the highly competitive BERT (F1 0.728) and Random Forest (F1 0.711) models. Despite these methods, all models struggled with extreme minority classes, confirming data scarcity as a primary limitation. This study provides a valuable benchmark and an evidence-based framework for HR practitioners, highlighting the critical trade-off between predictive performance (XGBoost), interpretability (Random Forest), and semantic capability (BERT). The findings conclude that the primary challenge is data representation, steering future work towards data augmentation and fairness audits.
Detecting AI-Generated and Authentic Artworks Using a ResNet50 Convolutional Neural Network Architecture Nadia Putri Nabila; Bambang Suharto; Fitri Noor Febriana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7362

Abstract

The rapid advancement of generative artificial intelligence (AI) has obscured the distinction between human- and machine-created art, posing significant challenges to authentication, copyright, and artistic integrity. This study addresses the critical need for reliable verification tools by developing and evaluating a deep learning model to automatically classify artworks based on their origin. A ResNet50 Convolutional Neural Network architecture was fine-tuned for the binary classification task. The model was trained on a custom, perfectly balanced dataset comprising 868 images (434 AI-generated, 434 authentic artworks). The training protocol included extensive data augmentation to enhance generalization and an early stopping mechanism to prevent overfitting. The experimental results demonstrate a high level of classification performance. The model achieved a validation accuracy of 86.21%, with a precision of 0.88 and a recall of 0.84 for the AI-Generated class. A Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.929, indicating robust discriminative capability. Qualitative error analysis revealed that the model's primary challenges lie in classifying hyper-realistic AI-generated images and authentic artworks with surreal or digitally abstract styles. This study validates the effectiveness of the ResNet50 architecture as a reliable and accessible tool for digital art authentication. It contributes a well-documented performance baseline on a balanced, custom dataset, providing a practical foundation for future research. This work highlights key challenges and suggests future directions, such as the exploration of more advanced architectures and the development of larger, more diverse datasets to further improve detection accuracy.
Co-Authors Bambang Suharto Dimas Sulthon Ubaidillah Lubis Harjono, Ansell Cohen Ira Puspitasari Nadia Putri Nabila sendy ayu mitra uktutias