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All Journal International Journal of Electrical and Computer Engineering JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Nova Noor Kamala Sari
University of Palangka Raya
Computer Science & IT Electrical & Electronics Engineering

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THE EMOTIONAL ANALYSIS OF SONG LYRICS AND VIDEO COMMENTS ON YOUTUBE USING DEEP LEARNING Nova Noor Kamala Sari; Widiatry Widiatry; Inda Fitria Maharisty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7321

Abstract

Digital media platforms shape public perception of music through song lyrics and audience comments. This study analyzes emotions expressed in the lyrics and YouTube comments of Taylor Swift’s “Fortnight” using deep learning models. The dataset consists of 42 lyric lines and 13,406 user comments collected from April to December 2024. Emotion labeling was manually performed based on Plutchik’s eight basic emotions with an additional neutral category. This research applies two models: Long Short-Term Memory (LSTM) and DistilRoBERTa, with random oversampling to address class imbalance. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. As a single-song case study, this research provides a focused comparison of sequential and transformer-based architectures for simultaneous emotion analysis of lyrics and audience responses. The results show that DistilRoBERTa achieved higher accuracy (94.07%) than LSTM (90.75%), indicating the advantage of contextual transformer models in capturing nuanced emotional expressions within this dataset. However, the findings are limited to the thematic characteristics of this single-song dataset and should be interpreted within this contextual scope.
Emotion-conditioned podcast recommendation using context-aware collaborative filtering Nova Noor Kamala Sari; Viktor Handrianus Pranatawijaya; Efrans Christian; Dina Meiliana
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2182-2191

Abstract

The rapid growth of podcast consumption on video-based platforms introduces new challenges for recommender systems, as user media choices are influenced not only by historical preferences but also by dynamic psychological states. Conventional approaches primarily model similarity and often ignore emotional context, particularly in long-form media. This study investigates emotion-conditioned recommendation behavior by treating user emotion as a contextual variable within a hybrid recommendation framework. This framework models emotion as a conditioning mechanism that constrains candidate selection prior to collaborative ranking. A dataset of 4,468 YouTube podcast transcripts was collected and preprocessed. Emotional labels were generated using the NRC emotion lexicon and validated through manual verification. User emotional state was detected using a support vector machine (SVM), while implicit preferences were estimated from engagement indicators using a random forest (RF) model. The recommendation stage integrates contextual pre-filtering based on Plutchik’s emotional relationships with collaborative filtering using k-nearest neighbors (KNN) with cosine similarity. Evaluation using stratified 5-fold cross-validation, baseline comparison, and Wilcoxon signed-rank testing shows that emotional context alters recommendation ranking behavior and improves ranking quality and retrieval coverage within the candidate space. These findings indicate that emotion acts as a conditioning mechanism in long-form media consumption, influencing recommendation outcomes beyond predictive accuracy.
From climate time series to planting windows in chili (Capsicum frutescens): a SARIMA–SVM–XGBoost framework with balanced-accuracy thresholding Efrans Christian; Nova Noor Kamala Sari; Ressa Priskila; Septian Geges
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2210-2219

Abstract

This study proposes a spatio-temporal decision-support framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to derive adaptive planting windows for chili (Capsicum frutescens) at the sub-district level. The framework addresses key challenges in climate-sensitive agriculture, including spatial data leakage and class imbalance, by employing Leave-One-Group-Out (LOGO) cross-validation and Balanced Accuracy–based threshold optimization. The proposed system transforms heterogeneous environmental data into actionable recommendations by combining climate forecasting, land suitability assessment, and yield prediction within a unified pipeline. Experimental results indicate that the framework effectively captures seasonal climate dynamics and produces consistent planting recommendations aligned with agronomic conditions, enabling multiple planting cycles per year. The primary contribution of this work lies in a transparent and generalizable integration of statistical and machine learning models into a practical decision-support framework. The proposed approach bridges predictive modeling and real-world agricultural decision-making and can be extended to other crops and regions for climate-adaptive agricultural planning.
Co-Authors Dina Meiliana Efrans Christian Inda Fitria Maharisty Ressa Priskila Septian Geges Viktor Handrianus Pranatawijaya Widiatry Widiatry