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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT)
Ronal Watrianthos
Politeknik Negeri Padang
Computer Science & IT Engineering

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A Text Mining Approach to Analyzing the Role of Negative Sentiment Words in News Articles on Suicide and Related Incidents Selamat Subagio; Samsir Samsir; Abdul Hakim Dalimunthe; Ronal Watrianthos
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1745

Abstract

This study examines the role of negative sentiment words in news media coverage of suicide and related incidents through analysis of 1,515 news articles published between 2019 and 2024. Using advanced text mining techniques and sentiment analysis, we investigated patterns in emotional language use and their impact on public discourse. The research revealed frequent usage of negative sentiment words such as "crisis" (256 occurrences), "despair" (214 occurrences), and "death" (189 occurrences), which significantly influenced the emotional framing of these sensitive topics. Statistical analysis showed strong correlations between negative sentiment words and mental health-related terms (correlation value 0.75), indicating consistent patterns in media narrative construction. Temporal analysis identified a notable increase in negative sentiment during the COVID-19 pandemic (2020-2021), followed by a shift toward more solution-focused coverage in 2022-2024. The findings suggest that while negative sentiment words are inherent in covering suicide-related topics, their use can be balanced with solution-oriented language to promote more responsible reporting. This research contributes to understanding how emotional language shapes public discourse on mental health crises and provides insights for developing more effective guidelines for responsible journalism.
Detecting AI-Generated Text with Fine-Tuned RoBERTa: A Cross-Validated Study with Interpretability Analysis Yuhefizar Yuhefizar; Ronal Watrianthos; Dony Marzuki
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7660

Abstract

The rapid proliferation of large language models capable of generating fluent and contextually coherent text has made reliable authorship attribution both a practical and ethical necessity. Outputs from systems such as GPT-4, LLaMA 2, and Claude are increasingly indistinguishable from human-authored prose, yet current detection methodologies remain insufficient to meet this challenge. This study aims to develop and evaluate a fine-tuned RoBERTa-base binary classifier for distinguishing AI-generated text from human-authored text under stratified cross-validation conditions; assess the model's robustness to a known length-based confound through a controlled ablation study; and provide interpretable explanations of the classifier's decisions through dual token-level attribution using Gradient × Input Saliency and Attention Rollout. The model is trained and evaluated via stratified five-fold cross-validation on a balanced corpus of 6,069 samples (3,069 AI-generated and 3,000 human-authored) and benchmarked against four established baselines spanning classical, neural, and zero-shot paradigms. The classifier achieves a mean accuracy of 99.93%, a weighted F1-score of 0.9993, and a mean AUC-ROC of 1.0000 across all five folds. A length-balanced ablation produces an accuracy decline of only 0.11 percentage points, providing empirical evidence that the model has learned genuine semantic and stylistic distinctions rather than superficial length-based cues. Gradient × Input Saliency maps indicate that AI-generated texts are characterised by technically precise, domain-specific vocabulary, whereas human-authored texts are identified through discourse markers and rhetorically structured expression, with both attribution methods converging on the same high-saliency tokens.
ResNet34-Encoded U-Net with Transfer Learning for Breast Cancer Cell Segmentation Under Data Scarcity Ronal Watrianthos; Yuhefizar Yuhefizar; Rayendra Rayendra; Ervan Asri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7661

Abstract

Histopathological examination of hematoxylin and eosin (H&E)-stained tissue remains the clinical gold standard for breast cancer diagnosis; however, manual cell segmentation is labor-intensive, subjective, and prone to substantial inter-observer variability. Deep learning-based segmentation models have demonstrated considerable promise in computational pathology, yet their performance under conditions of severe data scarcity remains insufficiently characterized. This study proposes and evaluates a ResNet34-encoded U-Net trained via transfer learning as a solution to this challenge, benchmarking it against a vanilla U-Net trained from random initialization as a controlled baseline. The experimental dataset comprises 42 H&E-stained breast cancer whole-slide image patches acquired at Qingdao Central Hospital (2019–2022), partitioned into 34 training and 8 test images. To mitigate the severe class imbalance inherent in the dataset — in which cancerous cell regions constitute only 16.2% of image pixels on average — a weighted Binary Cross-Entropy and Dice composite loss function was employed alongside a differential learning rate strategy to preserve pretrained encoder representations during fine-tuning. Data augmentation was applied to the training set to improve generalization under extreme data constraint. Evaluation was conducted using the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) as primary metrics. The ResNet34-UNet achieved a DSC of 0.6071 and IoU of 0.4392, outperforming the vanilla U-Net (DSC: 0.5554, IoU: 0.4004) by 9.3% and 9.7% respectively, while converging 37.5% faster. These findings demonstrate that ImageNet-pretrained encoder features transfer effectively to H&E pathology domains even under extreme data constraints, providing a reproducible and computationally efficient baseline pipeline for automated cell segmentation in resource-limited clinical settings.
Co-Authors Abdul Hakim Dalimunthe Dony Marzuki Rayendra Rayendra Samsir Samsir Selamat Subagio Yuhefizar Yuhefizar