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All Journal Journal of Electrical Technology UMY METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Saragih, Eka Parima
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Library & Information Science

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EVALUATING THE EFFECTIVENESS OF DISTILBERT FOR SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN GAME DEVELOPMENT Ahmad Fadhil N; Saragih, Eka Parima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4498

Abstract

Real-time sentiment analysis (SA) plays an increasingly vital role in enhancing player experience through emotion-aware game design. By enabling systems such as dynamic difficulty adjustment, adaptive non-playable character (NPC) behavior, and responsive narrative progression, SA allows games to respond intelligently to player emotions. This study investigates the effectiveness of DistilBERT, a lightweight transformer-based language model, for multi-label emotion classification using the GoEmotions dataset, which includes 27 fine-grained emotion categories. The model’s performance was evaluated in terms of classification accuracy and computational efficiency. Experimental results reveal that DistilBERT delivers surprisingly strong performance despite its reduced size, making it a viable candidate for real-time applications in resource-constrained environments. These findings indicate that lightweight transformer models can support emotionally adaptive gameplay without significant trade-offs in latency or accuracy. Future work will focus on integrating DistilBERT into a live game environment to assess its impact on player engagement and real-time system responsiveness.
Stance Detection of Controversial Articles Using TF-IDF and BERT Saragih, Eka Parima; Sekarlangit, Anggraini Dyah Ayu; Suman, Faqih Al
Journal of Electrical Technology UMY Vol. 9 No. 1 (2025): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v9i1.26965

Abstract

Online misinformation and polarized discussions require better methods for automatically detecting a text's stance. As digital content increases, identifying whether a news article supports, opposes, or is neutral towards its headline is crucial for fighting the spread of false information. This study presents a hybrid model designed for this task. We combine lexical features from Term Frequency-Inverse Document Frequency (TF-IDF), which captures word-level patterns, with contextual semantic information from a pretrained BERT model (bert-base-uncased). The features from both TF-IDF and BERT's [CLS] token were concatenated and used to train a logistic regression classifier. The model was trained and tested on a filtered version of the Fake News Challenge (FNC-1) dataset, with "unrelated" pairs removed to focus on more nuanced stance classification. The final evaluation of this model achieved 83% accuracy with a macro F1-score of 0.68. This model evaluates best in the Neutral stance (F1-score 0.91), but has some difficulty detecting the stance in the Oppositional class (with an F1-score 0.39). The results of this evaluation show that surface level lexical features combined with deep contextual understanding can improve the performance of stance detection.
Co-Authors Ahmad Fadhil N Sekarlangit, Anggraini Dyah Ayu Suman, Faqih Al