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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Catha Saintifica
Jalia, Kunti Najma
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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PROMOTION COPYWRITING GENERATOR MODELING USING PROBABILISTIC PARSING TECHNIQUE IN NLP: CASE STUDY AT CV. BERKAH TIGA DEWI Mardiyantoro, Nahar; Ngatoilah, Mohamad; Jalia, Kunti Najma; Hidayaturofingah, H
CATHA SAINTIFICA Vol 2 No 1 (2024): May 2024
Publisher : Sentra Kekayaan Intelektual dan Inovasi Teknologi (INOTEK) Universitas Sains Al-Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/cathasaintifica.v2i1.7632

Abstract

This study aims to develop a promotional copywriting generator model based on the Probabilistic Parsing technique in Natural Language Processing (NLP), applied to CV. Berkah Tiga Dewi, is a snack production and sales company located in Bumirejo Village, Mojotengah District, Wonosobo. The proposed model was evaluated using Precision, Recall, F1-Score, and Perplexity metrics. The results showed a significant increase in the quality of the promotional text, with the F1-Score of the Probabilistic Parsing model reaching 0.86, compared to the traditional method which only reached 0.70. In addition, a lower Perplexity value indicates that the resulting text is more natural and easy to understand. Validation through cross-validation techniques produced a consistent performance with an average Precision of 0.88 and Recall of 0.85. This study proves the effectiveness of the Probabilistic Parsing technique in producing persuasive and relevant copywriting, providing practical solutions to the company's marketing needs. The impacts include increasing product appeal and corporate image. Development prospects include adapting the model to other products and integrating with digital marketing platforms. In conclusion, the research objectives were achieved with relevant and significant results in the practical application of CV. Berkah Tiga Dewi marketing.
PROMOTION COPYWRITING GENERATOR MODELING USING PROBABILISTIC PARSING TECHNIQUE IN NLP: CASE STUDY AT CV. BERKAH TIGA DEWI Mardiyantoro, Nahar; Ngatoilah, Mohamad; Jalia, Kunti Najma; Hidayaturofingah, H
CATHA SAINTIFICA Vol 2 No 1 (2024): May 2024
Publisher : Sentra Kekayaan Intelektual dan Inovasi Teknologi (INOTEK) Universitas Sains Al-Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/cathasaintifica.v2i1.7632

Abstract

This study aims to develop a promotional copywriting generator model based on the Probabilistic Parsing technique in Natural Language Processing (NLP), applied to CV. Berkah Tiga Dewi, is a snack production and sales company located in Bumirejo Village, Mojotengah District, Wonosobo. The proposed model was evaluated using Precision, Recall, F1-Score, and Perplexity metrics. The results showed a significant increase in the quality of the promotional text, with the F1-Score of the Probabilistic Parsing model reaching 0.86, compared to the traditional method which only reached 0.70. In addition, a lower Perplexity value indicates that the resulting text is more natural and easy to understand. Validation through cross-validation techniques produced a consistent performance with an average Precision of 0.88 and Recall of 0.85. This study proves the effectiveness of the Probabilistic Parsing technique in producing persuasive and relevant copywriting, providing practical solutions to the company's marketing needs. The impacts include increasing product appeal and corporate image. Development prospects include adapting the model to other products and integrating with digital marketing platforms. In conclusion, the research objectives were achieved with relevant and significant results in the practical application of CV. Berkah Tiga Dewi marketing.
LSTM-Based Causal Attribution Modeling of the 2025 Sumatra Flash Flood Discourse on YouTube Jalia, Kunti Najma; Suwondo, Adi; Sibyan, Hidayatus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2132

Abstract

Existing disaster sentiment analysis mainly focuses on emotional polarity classification, while often over-looking the causal reasoning that shapes public discourse on responsibility for disaster outcomes. This study proposes and assesses a Long Short-Term Memory (LSTM)-based causal attribution classification framework to examine YouTube comments related to the 2025 Sumatra flash flood. It compares LSTM performance with Sup-port Vector Machine (SVM) and Naïve Bayes baselines. A total of 17,503 publicly available comments were collected through the YouTube Data API v3 and processed into a final dataset of 12,299 comments. The com-ments were classified into two causal categories, human factor and nature/prayer factor, using lexicon-based scoring validated by three independent annotators (Cohen's κ = 0.81). The experimental results show that LSTM achieves 98.17% accuracy with strong stability (±0.25% standard deviation) under stratified five-fold cross-validation, substantially outperforming SVM (82.83%) and Naïve Bayes (75.04%). These findings indi-cate that sequence-based architectures can capture the contextual dependencies in causal attribution dis-course, offering a replicable framework for disaster risk communication monitoring systems.
Co-Authors Hidayaturofingah, H Mardiyantoro, Nahar Ngatoilah, Mohamad Sibyan, Hidayatus Suwondo, Adi