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All Journal Dinamik Building of Informatics, Technology and Science Jurnal Media Computer Science Malcom: Indonesian Journal of Machine Learning and Computer Science
Mahenra, Ridwan
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Computer Science & IT

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Journal : Building of Informatics, Technology and Science

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Optimasi Hyperparameter Gaussian Naive Bayes Untuk Prediksi Risiko Stroke Pada Data Tidak Seimbang Nida, Khoirun; Mahenra, Ridwan; Susanto, Erliyan Redi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8497

Abstract

Stroke is a serious disease with global impact that requires high-accuracy early detection. Significant difficulties in designing machine learning-based predictive models arise due to disproportionate data conditions (imbalanced datasets). This occurs because the number of stroke cases (minority class) is very small compared to non-stroke cases. This imbalanced data situation often causes models to become biased and potentially produce high false negative rates, which is very risky in a clinical setting. This study focuses on improving the sensitivity of the Gaussian Naive Bayes (GNB) model through hyperparameter optimization and classification threshold adjustment. The research process included data preprocessing, stratified dataset division (70% training and 30% testing), feature scaling, var_smoothing parameter optimization using GridSearchCV, and threshold adjustment to maximize the Recall value. The results showed that the standard GNB model only achieved a Recall value of 0.4400. However, after var_smoothing optimization (1.00×10⁻¹⁰) and threshold adjustment to 0.0100, the Recall value increased significantly to 0.8000. This increase was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). This improvement was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). The high Recall (0.8000) indicates that the model is better for mass screening (early detection phase), although it must be balanced with further diagnostic processes due to low precision. This high Recall value confirms the model's success in minimizing False Negatives, which is a top priority in stroke risk prediction cases.
Analisis Respon Publik Terhadap Tren Penggabungan Foto Gemini AI Menggunakan Naive Bayes Afiani, Nanda; Mahenra, Ridwan
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8504

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has brought numerous innovations to the digital world, one of which is Gemini AI — an application capable of automatically merging photos based on user instructions. This phenomenon has gone viral on the TikTok platform and has sparked diverse public reactions, ranging from admiration for its visual results to concerns about ethical issues and the potential misuse of deepfake technology. This study aims to analyze public sentiment toward the trend of Gemini AI photo merging on TikTok using a sentiment analysis method based on the Naïve Bayes algorithm. Data were collected through a web scraping technique using the Apify platform, resulting in 5,061 user comments. The data processing stages included text preprocessing, TF-IDF transformation, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that neutral sentiment dominates (4,059 comments), followed by positive (745 comments) and negative (257 comments). The dominance of neutral sentiment occurs because most user comments are informative or descriptive, expressing ordinary responses without strong emotional tones, rather than showing indifference to ethical concerns. The Naïve Bayes model demonstrated good performance with an accuracy of 85.72%, precision of 87.84%, recall of 85.72%, and F1-score of 81.95% through 5-fold cross-validation. These findings confirm that the Naïve Bayes algorithm is effective for classifying public opinion toward generative AI technologies. Overall, this study contributes to a deeper understanding of public perception of AI innovations in the creative digital domain and their social implications on social media platforms.
Co-Authors Afiani, Nanda Al Farhan, M Haidar Amir Nida, Khoirun Prasetyo, Khoir Setiawan, Dandi Susanto, Erliyan Redi Wicaksono, Aditya Eka Putra Yuanggara, Virnu