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All Journal Jurnal Masyarakat Informatika Journal Of Engineering Sciences (Improsci) Dedikasi Nusantara
Kurniawan, Sandy
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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%.
An Ensemble-Based Approach for Detecting Clickbait in Indonesian Online Media Kurniawan, Sandy; Pramayoga, Adhe Setya; Ashari, Yeva Fadhilah
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73115

Abstract

Clickbait headlines are widely used in online media to attract readers through exaggerated or misleading titles, potentially leading to user dissatisfaction and information overload. This study proposes a machine learning approach for detecting clickbait in Indonesian news headlines using classical classification models and ensemble learning. The dataset consists of labeled clickbait and non-clickbait headlines in Bahasa Indonesia, which were processed and represented using TF-IDF vectorization. Three base classifiers, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, were integrated using soft voting and stacking ensemble methods. The experimental results indicate that the stacking ensemble model achieved the highest accuracy of 0.7728, while the voting ensemble recorded the best F1-score of 0.7080, outperforming individual classifiers. Despite these gains, the SVM model demonstrated the most substantial decline in accuracy after stopwords removal, dropping by 0.0410. These findings highlight the effectiveness of ensemble learning in enhancing clickbait detection performance and suggest potential for further optimization in model selection and integration strategies.
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
Co-Authors Adhe Setya Pramayoga, Adhe Setya Arif Wibawa, Helmie Ashari, Yeva Fadhilah Budi, Indra Khadijah Ragil Saputra Rismiyati