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All Journal JURNAL PENDIDIKAN DAN KEGURUAN International Journal of Computer Technology and Science
Salsabila Septiani
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Computer Science & IT Education Mathematics Other

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Sentiment Analysis Of Social Media Data Using Deep Learning Techniques Salsabila Septiani; Nabila Putri; Dara Jessica; Arya Saputra
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.59

Abstract

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.
PENGGUNAAN METODE DIFFERENTIAL REINFORCEMENT OF ALTERNATIVE BEHAVIOR DALAM MENURUNKAN PERILAKU AGRESIF PADA ANAK TUNAGRAHITA DI SLB BINA INSANI Salsabila Septiani; Toni Yudha Pratama; Sistriadini Alamsyah Sidik
JURNAL PENDIDIKAN DAN KEGURUAN Vol. 3 No. 2 (2025): Februari
Publisher : CV. ADIBA AISHA AMIRA

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Abstract

This study aims to determine the effect of using the differential reinforcement of alternative behavior method in reducing aggressive behavior in first-grade students with intellectual disabilities at SLB Bina Insani Purwakarta. This research employs a quantitative approach using the Single Subject Research (SSR) experimental method with an A – B – A design, consisting of a baseline phase 1 (A1) of 8 sessions, an intervention phase (B) of 16 sessions, and a baseline phase 2 (A2) of 8 sessions, each session lasting 30 minutes. The subject of this research is one student with intellectual disabilities in first grade at SLB Bina Insani Purwakarta. The targeted behavior in this study is to reduce aggressive hitting behavior in the subject. Data collection was conducted through observation and documentation methods. The obtained data were analyzed using descriptive statistics presented in tables and graphs. Based on the results of the study, the use of the differential reinforcement of alternative behavior method can reduce aggressive hitting behavior in children with intellectual disabilities. The rate obtained during the baseline phase 1 (A1) was 0.23 times/minute, during the intervention phase (B) it was 0.10 times/minute, and during the baseline phase 2 (A2) it was 0.06 times/minute. Based on this data, it can be concluded that the use of the differential reinforcement of alternative behavior method can reduce aggressive behavior in children with intellectual disabilities at SLB Bina Insani Purwakarta.
Co-Authors Arya Saputra Dara Jessica Nabila Putri Sistriadini Alamsyah Sidik Toni Yudha Pratama