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All Journal Journal Of Engineering Sciences (Improsci) Advance Sustainable Science, Engineering and Technology (ASSET) Dedikasi Nusantara
Kurniawan, Sandy
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Utilizing Translation to Enhance NLP Models in Offensive Language and Hate Speech Identification Kurniawan, Sandy; Budi, Indra
Jurnal Improsci Vol 1 No 4 (2024): Vol 1 No 4 February 2024
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v1i4.187

Abstract

The number of social media users in Indonesia has increased in recent years. The surge in social media users leads to more offensive language on these platforms. The use of offensive language can trigger conflicts between users. Therefore, it is necessary to identify the use of offensive language on social media. This study focused on identifying offensive language, hate speech, and hate speech targets on Twitter. The data used were obtained from previous research on identifying offensive language and hate speech. The amount of data is very influential on the performance of the classification. Therefore, data was added using translation in this study. Classical machine learning (SVM et al.) and deep learning (BiLSTM, CNN, and LSTM) algorithms are used as classification algorithms with word n-gram and word embedding as the features. Three scenarios were done based on the training data used in the classification model development. The result shows that scenario 3, which uses translation for data augmentation, can improve the classification model’s performance by 5%.
Pemanfaatan Web E-Ecommerce sebagai Solusi Pemasaran bagi UMKM di Kecamatan Mayong Kabupaten Jepara Saputra, Ragil; Arif Wibawa, Helmie; Rismiyati; Khadijah; Kurniawan, Sandy
Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Vol. 1 No. 3 (2025): Inovasi Teknologi dan Pemberdayaan Masyarakat Desa
Publisher : IndoCompt Publisher

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Abstract

Program pengabdian masyarakat ini dilaksanakan untuk memberdayakan Usaha Mikro, Kecil, dan Menengah (UMKM) di Kecamatan Mayong, Kabupaten Jepara yang masih memiliki tingkat adopsi teknologi digital rendah. Dari 80.000 UMKM di Kabupaten Jepara, hanya 3,4% yang memiliki daya saing di pasar akibat keterbatasan literasi digital, kurangnya pelatihan, dan minimnya infrastruktur pendukung. Kegiatan ini bertujuan meningkatkan akses pasar dan daya saing UMKM melalui pelatihan dan implementasi platform web e-commerce. Program dilaksanakan melalui identifikasi masalah dengan survei dan wawancara, pengembangan web e-commerce, pendampingan penggunaan platform, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan sebanyak 18 pelaku UMKM berpartisipasi dalam kegiatan ini memahami pentingnya pemasaran digital dan mampu menggunakan platform web https://www.umkmkecmayong.com. Program ini berperan dalam meningkatkan keterampilan digital dan membuka peluang pasar yang lebih luas bagi UMKM
Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection Kurniawan, Sandy; Pramayoga, Adhe Setya; Ashari , Yeva Fadhilah; Muhammad Afrizal Amrustian
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2637

Abstract

The rapid growth of digital news platforms necessitates reliable and automated systems for maintaining content quality at scale. This study presents the engineering and evaluation of an IndoBERT-based Natural Language Processing (NLP) framework for automated clickbait detection in Indonesian news headlines. The proposed framework is designed as an end-to-end text classification pipeline, incorporating data preprocessing, tokenization, fine-tuning of a pretrained IndoBERT model, and systematic performance evaluation. Experiments were conducted using the CLICK-ID dataset comprising 15,000 Indonesian news headlines, with an 80:20 stratified train–test split. The fine-tuned model achieved an accuracy of 0.83, with a precision of 0.82, recall of 0.77, and an F1-score of 0.79 for the clickbait class. Further evaluation using threshold-independent metrics yielded a ROC-AUC value of 0.89 and an average precision of 0.88, indicating strong discriminative capability under moderate class imbalance. Comparative analysis shows that the proposed approach outperforms prior CNN, Bi-LSTM, and ensemble-based methods evaluated on the same dataset. These results demonstrate that IndoBERT provides a robust foundation for engineering automated clickbait detection systems tailored to Indonesian-language news streams.
Co-Authors Adhe Setya Pramayoga, Adhe Setya Arif Wibawa, Helmie Ashari , Yeva Fadhilah Budi, Indra Khadijah Muhammad Afrizal Amrustian Ragil Saputra Rismiyati