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All Journal Jupiter Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma IJOEM: Indonesian Journal of Elearning and Multimedia Finger : Jurnal Ilmiah Teknologi Pendidikan Jurnal Pengabdian Masyarakat Bangsa Jurnal Pendidikan Teknologi Informasi (J-Diteksi) Jurnal Rekayasa Perangkat Lunak
Ikna Awaliyani
Universitas Aisyah Pringsewu
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Pemanfaatan Teknologi Digital sebagai Penguat Modal Sosial-Budaya dalam Pendidikan Karakter Siswa Pedesaan rizky12 nurwahid; Ikna Awaliyani
Jurnal Pendidikan Teknologi Informasi (J-Diteksi) Vol. 5 No. 1 (2026): Jurnal Pendidikan Teknologi Informasi (J-Diteksi)
Publisher : Universitas Aisyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30604/diteksi.v5i1.2235

Abstract

This research examines how digital technology can help strengthen social and cultural capital in the character education of students living in rural areas. Currently, technology is widely used in schools, but its use is still limited to communication and assignment submission. However, technology can be utilized more effectively to support local cultural values ​​and strengthen relationships between teachers, parents, and the community. Furthermore, villages possess strong social and cultural capital, such as the practice of mutual cooperation (gotong royong), a sense of togetherness, and local traditions and values ​​that are still practiced daily. These values ​​form the foundation for children's character education. A literature review of various studies found that technology can be a bridge to introduce and preserve local culture, for example through the creation of cultural videos, documentation of traditions, or other digital media. When local culture is packaged in digital form, students become more engaged and more easily understand the character values ​​being taught. The results of this study indicate that character education in rural areas will be stronger if technology is combined with existing social and cultural capital. Thus, technology is not simply a modern tool, but can be a new way to maintain cultural identity and shape students' character, keeping it relevant to current developments.
Deep Learning Framework for Automatic Tagging of Multimedia Content Ikna Awaliyani; Nur Aminudin; Dita Septasari; Ulfa Isni Kurnia
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18168720

Abstract

Penandaan otomatis konten multimedia merupakan komponen penting dalam manajemen aset digital, rekomendasi konten, dan sistem pengambilan visual skala besar. Namun, kompleksitas konteks visual, variasi objek, dan karakteristik multi-label membuat tugas ini menantang untuk pendekatan konvensional. Studi ini mengusulkan kerangka kerja pembelajaran mendalam ujung ke ujung yang mengintegrasikan Vision Transformer (ViT) sebagai ekstraktor fitur utama dengan kepala klasifikasi multi-label adaptif, termasuk modul fusi multimoda opsional untuk memanfaatkan hubungan semantik antara gambar dan teks. Eksperimen dilakukan pada dataset skala besar seperti MS-COCO, NUS-WIDE, dan Open Images Dataset menggunakan strategi pelatihan termasuk augmentasi data, fine-tuning progresif, dan fungsi kehilangan adaptif. Model yang diusulkan mencapai peningkatan yang konsisten, mengungguli baseline CNN sebesar 4–6% dan arsitektur ViT murni sebesar 2–3%, dengan Presisi Rata-rata (mAP) rata-rata 0,78 dan skor F1 0,82. Integrasi multimoda semakin meningkatkan kinerja pada label abstrak seperti aktivitas, luar ruangan, dan acara. Temuan ini menunjukkan bahwa Vision Transformers, dikombinasikan dengan pembelajaran multimoda, secara signifikan meningkatkan akurasi dan kualitas semantik penandaan konten multimedia otomatis.
Co-Authors Abdul Aziz, RZ Agus Wantoro Andini, Dwi Yana Ayu Ariyanti, Septika Aviv Fitria Yulia Dita Septasari Dwi Feriyanto Fitriana Fitriana Kraugusteeliana Kraugusteeliana Mukaromah, Hafsah Nur Aminudin Nur Asini Nurul Hidayat rizky12 nurwahid Shima Asadi Sukarni Ulfa Isni Kurnia Wahyu Caesarendra Yudha Pratama, Rendy Zulkifli