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Nuraina
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Pelatihan dan Pendampingan Digital Marketing bagi Pelaku Usaha Rumah Makan untuk Meningkatkan Daya Saing Nurhayati; Arisman; Nuraina; Suminar Ariwibowo; Nanda felani Baihaqi; Syahrial Sitorus
Jurnal Masyarakat Indonesia (Jumas) Vol. 5 No. 01 (2026): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jumas.v5i01.359

Abstract

The culinary business sector, especially small and medium-scale restaurants, faces increasingly competitive challenges in the digital era. Many restaurant business owners still rely on conventional promotional methods and have not optimally utilized digital technology to expand market reach and improve competitiveness. This community service activity was carried out with the aim of providing training and mentoring related to digital marketing strategies for restaurant business actors. The methods used in this activity include observation, socialization, training, mentoring, and evaluation. The training materials covered the use of social media, digital content creation, online promotion strategies, customer engagement, and the utilization of digital platforms such as Instagram, WhatsApp Business, Google Maps, and food delivery applications. The results of this activity showed an increase in participants’ understanding and skills in managing digital promotions, creating attractive content, and utilizing online platforms to support business development. Through this activity, restaurant business actors are expected to be able to improve service quality, expand market reach, and strengthen business competitiveness in the digital era.
INDOBERTWEET DENGAN TEMPORAL ATTENTION MECHANISM UNTUK DETEKSI ISU BENCANA DINAMIS MULTIPLATFORM Nurhayati; Tanti; Nuraina; Arisman; Felix
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/qtam3c42

Abstract

Abstract: The development of disaster-related information in the digital space is very rapid and is often detected earlier through social media than through official government channels. This condition highlights the need for a system capable of detecting disaster-related issues quickly and dynamically across various digital platforms. This study aims to develop a dynamic disaster issue detection model based on IndoBERTweet with a Temporal Attention Mechanism using multiplatform big text data in Indonesia. The research methodology includes collecting textual data from various digital platforms such as Twitter, YouTube, TikTok, Quora, and Medium. The data are processed through preprocessing stages using Natural Language Processing (NLP) techniques, timestamp extraction to obtain temporal information, and the application of fine-grained labeling for more detailed classification of disaster-related issues. Subsequently, the IndoBERTweet model is trained with a Temporal Attention Mechanism to capture the relationship between textual context and temporal dynamics in the development of disaster-related issues. The expected results of this research are a model capable of dynamically detecting disaster-related issues by considering informal language contexts and temporal changes. This model is expected to support early warning systems and data-driven disaster management decision-making in Indonesia.   Keywords: Disaster Issue Detection; Social Media Text Analysis; Multiplatform Big Data; IndoBERTweet; Temporal Attention.   Abstrak: Perkembangan informasi kebencanaan di ruang digital berlangsung sangat cepat dan sering kali lebih dahulu terdeteksi melalui media sosial dibandingkan melalui kanal resmi pemerintah. Kondisi ini menunjukkan perlunya sistem yang mampu mendeteksi isu bencana secara cepat dan dinamis dari berbagai platform digital. Penelitian ini bertujuan mengembangkan model deteksi isu bencana dinamis berbasis IndoBERTweet dengan Temporal Attention Mechanism pada big data teks multiplatform di Indonesia. Metode penelitian meliputi pengumpulan data teks dari berbagai platform digital seperti Twitter, YouTube, TikTok, Quora, dan Medium. Data diproses melalui tahapan preprocessing menggunakan teknik Natural Language Processing (NLP), ekstraksi timestamp untuk memperoleh informasi temporal, serta penerapan fine-grained labeling untuk klasifikasi isu bencana yang lebih rinci. Selanjutnya, model IndoBERTweet dilatih dengan Temporal Attention Mechanism untuk menangkap hubungan antara konteks teks dan dinamika waktu dalam perkembangan isu bencana. Hasil penelitian diharapkan menghasilkan model yang mampu mendeteksi isu bencana secara dinamis dengan mempertimbangkan konteks bahasa informal dan perubahan waktu. Model ini diharapkan mendukung sistem peringatan dini dan pengambilan kebijakan kebencanaan berbasis data di Indonesia.   Kata kunci: Deteksi Isu Bencana; Analisis Teks Media Sosial; Big Data Multiplatform; IndoBERTweet; Temporal Attention.
Co-Authors Arisman Felix Nanda felani Baihaqi Nurhayati Suminar Ariwibowo Syahrial Sitorus Tanti