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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika: Jurnal Pengembangan IT Parameter: Jurnal Matematika, Statistika dan Terapannya Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Suhaeni, Cici
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Evaluating Fasttext and Glove Embeddings for Sentiment Analysis of AI-Generated Ghibli-Style Images Sentana Putra, I Gusti Ngurah; Yusran, Muhammad; Sari, Jefita Resti; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10600

Abstract

The development of text-to-image generation technology based on artificial intelligence has triggered mixed public reactions, especially when applied to iconic visual styles such as Studio Ghibli. This research aims to evaluate public sentiment towards the phenomenon of Ghibli-style AI images by comparing two static word embedding methods, namely FastText and GloVe, on three classification algorithms: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN). Data in the form of Indonesian tweets were collected from Twitter using hashtags such as #ghibli, #ghiblistyle, and #hayaomiyazaki during the period 25 March to 25 April 2025. Each tweet was manually labelled with positive or negative sentiment, then preprocessed and represented using pre-trained FastText and GloVe embeddings. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, both macro and weighted. Results showed that FastText consistently performed the best on most models, especially in terms of precision and overall accuracy, thanks to its ability to handle sub-word information and spelling variations in social media texts. The combination of CNN with FastText yielded the highest performance with a macro F1-score of 76.56% and accuracy of 84.69%. However, GloVe still showed competitive performance in recall on the Logistic Regression model, making it relevant for contexts that prioritise sentiment detection coverage. This study emphasizes the importance of selecting embeddings and models that are appropriate to the characteristics of the data and the purpose of the analysis in informal social media-based sentiment classification.
The Impact of the L1/L2 Ratio on Selection Stability and Solution Sparsity along the Elastic Net Regularization Path in High-Dimensional Genomic Data Fahira, Fani; Sadik, Kusman; Suhaeni, Cici; M Soleh, Agus
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12059

Abstract

High-dimensional genomic datasets (p>n) pose persistent challenges for predictive modeling and biomarker-oriented feature selection due to multicollinearity and instability of selected feature sets under resampling. Although Elastic Net is widely used to address correlated predictors via combined L1/L2 regularization, the practical role of the L1/L2 mixing ratio (α) is often treated as a secondary tuning choice driven primarily by predictive accuracy. This study investigates how varying α shapes the trade-off among selection stability, solution sparsity, and predictive performance along the Elastic Net regularization path. Experiments were conducted using the publicly available METABRIC breast cancer cohort (n = 1,964) with 21,113 gene expression features and a binary overall survival status outcome. Logistic regression with Elastic Net penalty was fitted across a grid of α values, with the regularization strength (λ) selected by cross-validation. Feature selection stability was evaluated under repeated resampling using the Jaccard index, Dice coefficient, and Adjusted Rand Index (ARI), while sparsity was summarized by the average number of non-zero coefficients; predictive performance was assessed using AUC, accuracy, and F1-score. Results show a monotonic decline in stability as α increases: α = 0.2 yields the highest stability (Jaccard 0.324, Dice 0.487, ARI 0.434), whereas LASSO (α = 1.0) produces the lowest stability (Jaccard 0.278, Dice 0.431, ARI 0.400). In contrast, predictive performance varies only marginally across α (AUC 0.696–0.704; accuracy 0.666–0.671; F1-score 0.738–0.742), while sparsity changes substantially (average selected features 110–204). Coefficient path analyses further illustrate abrupt shrinkage under LASSO versus smoother, group-preserving shrinkage under Elastic Net, consistent with improved reproducibility under lower-to-moderate α. Frequency-of-selection analysis highlights genes repeatedly selected across resampling, supporting interpretability of stable configurations without claiming causal biomarker validity. Overall, the findings demonstrate that α is a substantive modeling choice that materially affects stability and sparsity even when accuracy is similar, motivating stability-aware tuning for high-dimensional genomic prediction and reproducible feature discovery.
Co-Authors Abdullah, Adib Roisilmi Agus Mohamad Soleh Alfa Dito, Gerry Anang Kurnia Andespa, Reyuli Bagus Sartono Dito, Gerry Alfa Endriani, Desy Fahira, Fani Hari Wijayanto Kamila, Sabrina Adnin Kusman Sadik M Soleh, Agus Muhammad Yusran Nisrina Az-Zahra, Putri NURADILLA, SITI Resiloy, Unique Desyrre A. Saleh, Agus Muhammad Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Sunan, Muh. Zahra, Latifah