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All Journal Brilliance: Research of Artificial Intelligence Jurnal Pengabdian Masyarakat Bangsa
Cut Agusniar
Universitas Malikussaleh, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Pendampingan Penggunaan Aplikasi GeoGebra Berbasis Teknologi Informasi dalam Pengembangan Media Pembelajaran Interaktif Eri Saputra; Cut Agusniar; Rizky Putra Fhonna; Yesy Afrillia; Effan Fahrizal; Muhammad Ikhwanus
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 4 (2026): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i4.4563

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi siswa matematika di SMA Negeri 1 Kota Lhokseumawe dalam memanfaatkan aplikasi GeoGebra berbasis teknologi informasi untuk mengembangkan media pembelajaran interaktif. Permasalahan yang ditemukan di lapangan menunjukkan bahwa pemanfaatan teknologi pembelajaran masih terbatas dan belum terintegrasi secara optimal dalam perangkat pembelajaran, khususnya Rencana Pelaksanaan Pembelajaran (RPP). Metode pelaksanaan kegiatan menggunakan pendekatan pelatihan dan pendampingan yang terdiri atas empat tahap, yaitu analisis kebutuhan, pelatihan, pendampingan implementasi, serta evaluasi dan refleksi program. Hasil kegiatan menunjukkan adanya peningkatan kompetensisiswa dalam penguasaan GeoGebra, pengembangan media pembelajaran interaktif, serta integrasi teknologi dalam RPP. Hasil evaluasi menunjukkan peningkatan pada berbagai indikator, termasuk penguasaan GeoGebra sebesar 85% dan kepuasansiswa mencapai 92%. Kegiatan ini menunjukkan bahwa pendampingan berbasis teknologi efektif dalam meningkatkan kompetensi pedagogik digitalsiswa serta mendorong inovasi pembelajaran matematika yang lebih interaktif dan bermakna.
Topic Classification on Twitter Using a Multi-View Graph Neural Network (GNN) Model Dhea Sila Mukti; Rizal Tjut Adek; Cut Agusniar
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9061

Abstract

Political discussions on social media have become an important source of information for understanding public opinion and information dissemination. However, most existing political topic classification methods rely primarily on textual features and tend to overlook structural and temporal relationships between users. In this study, we propose a Multi-View Graph Attention Network (MV-GAT) that improves political topic classification by integrating three complementary graph representations: a semantic content graph, a user interaction graph, and a temporal propagation graph. We collected a dataset containing 15,131 Indonesian tweets from Twitter(X), of which 1,677 tweets were manually labeled as political or apolitical, and the remaining tweets were kept as unlabeled nodes to maintain the graph structure. Each graph view was independently constructed and aligned using tweet_id before being processed by the proposed MV-GAT model. The model was trained using weighted cross-entropy loss with an attention-based fusion mechanism to automatically learn the contribution of each graph view. Experimental results showed that the proposed method achieved an accuracy of 84.23%, a macro F1 score of 83.04%, and an F1 score of 78.54% in political topic classification. Attention analysis revealed that the semantic content graph contributed most significantly to the classification process, while the interaction graph and time graph provided complementary structural information. Furthermore, post-classification graph analysis revealed relationship patterns among users and the propagation of political information within the Twitter network. These results demonstrate that integrating multiple graph views improves both the classification performance and interpretability of political topic analysis on social media.
Co-Authors Dhea Sila Mukti Effan Fahrizal Eri Saputra Muhammad Ikhwanus Rizal Tjut Adek Rizky Putra Fhonna Yesy Afrillia