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All Journal The IJICS (International Journal of Informatics and Computer Science) Jurnal Informatika dan Rekayasa Perangkat Lunak
Arjon Samuel Sitio
Tjut Nyak Dhien University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Classification of Coming‑of‑Age Song Lyrics Using Convolutional Neural Network (CNN) Architecture Arjon Samuel Sitio; Fricles Ariwisanto Sianturi; Anita Sindar
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9775

Abstract

The coming-of-age theme in song lyrics is rich in emotional expressions, identity transitions, and nostalgia. This study implements a 1-Dimensional (1D) Convolutional Neural Network (CNN) architecture to automatically classify coming-of-age-themed song lyrics. Raw lyrics data were collected through a custom scraping function get_song_lyrics, which then went through a structured text preprocessing stage to remove stopwords and non-semantic components. The cleaned words were represented into a vector space using a pre-trained 50-dimensional GloVe embedding (GloVe 50d) of size (max_length, 50) to capture semantic relationships between words. A 1D CNN model was applied to extract local features in the form of phrase combinations (n-grams) through filter shifting, followed by a Global Max Pooling layer to filter out the most dominant emotional information before the final classification process. As a comparison method and additional analysis, a rule-based approach using TextBlob was applied to extract polarity and subjectivity scores, while the Word Cloud technique was used to visualize the dominance of transitional lexical terms such as grow, leave, and remember. The gap between training and validation performance suggests that the model learned dataset-specific patterns rather than generalized semantic representations. Similar overfitting behavior has been reported in CNN-based lyric and sentiment classification studies when training data are limited or insufficiently diverse. The results showed that the integration of GloVe 50d semantic representation and local feature extraction by 1D CNN was able to produce high and stable accuracy in recognizing the unique characteristics of song lyrics with a maturity theme.
Synthetic Data Pattern Simulation of Patient Care Journey Using K-Means Clustering Arjon Samuel Sitio; Richard Parlindungan; Anita Sindar Sinaga
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 4 (2025): Volume 6 Number 4 Desember 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i4.1498

Abstract

Heterogeneous synthetic data is artificial data that can include many types of features (demographics, examinations, therapies). Complex patients (many procedures & medications) but fast service process and low complications. All patients are divided into 4 clusters, patient segmentation includes cluster 1 including mild patients, Cluster 2 including complex patients, Cluster 3 including high costs, Cluster 4 including high readmission risk. The highest silhouette score is 0.2187, which is obtained when the number of clusters (k) is 2. Based on previous calculations, the Davies-Bouldin Index result for the current clustering solution is 2.33. The Calinski-Harabasz index for the clustering solution with k=4 is 367.72. Clustering results are simply groups, without labels. Further analysis is needed to assign clinical meaning to each cluster.
Co-Authors Anita Sindar Fricles Ariwisanto Sianturi Richard Parlindungan Sinaga, Anita Sindar R M