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Khairan Marzuki
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Universitas Bumigora Jl. Ismail Marzuki-Cilinaya-Cakranegara-Mataram 83127 Indonesia
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UPGRADE: Jurnal Pendidikan Teknologi Informasi
Published by Universitas Bumigora
ISSN : -     EISSN : 30217067     DOI : https://doi.org/10.30812/upgrade
Core Subject : Science, Education,
Computer Science & IT Education Other
Jurnal Upgrade Pendidikan Teknologi Informasi menerima artikel riset dan kajian ilmiah (review) dengan lingkup ilmu pendidikan teknologi beserta aplikasinya. Adapun fokus dan ruang lingkup topik yang diterbitkan pada jurnal ini adalah sebagai berikut. 1. Teknologi Pendidikan 2. Kecerdasan Buatan & Aplikasi 3. Jaringan & Keamanan Komputer 4. Pengambilan Multimedia Berbasis Komputer 5. Sistem Pendukung Keputusan 6. Gudang Data & Penambangan Data 7. Sistem-E, Logika Fuzzy 8. Sistem Informasi Geografis (SIG) 9. Interaksi Manusia & Komputer 10. Pemrosesan Citra, Sistem Informasi 11. Mobile Computing & Application 12. Multimedia System 13. Neural Network 14. Pattern Recognition 15. Inovatif pengembangan multimedia pendidikan dan e-learning
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 12 Documents
 1   2 
Search results for , issue "Vol 1 No 1 (2023)" : 12 Documents clear
Peran Adaptasi Game (Gamifikasi) dalam Pembelajaran untuk Menguatkan Literasi Digital: Systematic Literature Review Sari, Dwi Novita; Alfiyan, Ahmad Rifqy
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 1 No 1 (2023): Vol. 1 No. 1 Agustus 2023
Publisher : Pendidikan Teknologi Informasi Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v1i1.3157

Abstract

This research is a systematic literature review that aims to analyze the role of game adaptation or gamification in learning to strengthen digital literacy. Using the SLR method, this research identifies, synthesizes, and analyzes relevant scientific articles about the use of game adaptations in the context of learning digital literacy. This review explores how game elements and principles, such as point systems, challenges, competitions, and prizes, are applied in learning to improve students' digital literacy. In addition, this study analyzes the impact of using game adaptations on motivation, engagement, active participation, and the development of students' digital literacy skills. The implications of these SLR findings provide important insights for educators, curriculum developers, and researchers in designing effective learning strategies to increase students' digital literacy.
Identifikasi Pola Obyek Kain Tenun Sumba dengan Menggunakan Metode K-Nearest Neighbor (KNN) Budiati, Haeni; Himamunanto, Agustinus Rudatyo; Bolo, Naomi Tena
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 1 No 1 (2023): Vol. 1 No. 1 Agustus 2023
Publisher : Pendidikan Teknologi Informasi Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v1i1.3149

Abstract

Woven fabrics originating from Sumba have their own patterns that distinguish them from other woven fabric patterns throughout Indonesia. The pattern is a distinctive feature that describes the culture of the people in Sumba which is very diverse. To distinguish fabric patterns, one of the algorithms for object recognition is the K-Nearest Neighbor (KNN) algorithm. The KNN algorithm classifies objects based on training data that is closest to the object. Processing works by using metric and eccentricity parameters on training data and input images. This processing will produce text data which is the identification of objects in Sumba woven fabric motifs. Based on the testing that has been done, it successfully identifies the type of object contained in the training data. For types of objects that are not contained in the training data, identification is based on their proximity to the types of objects in the group that contain Sumba woven fabric patterns. The accuracy level of Sumba woven fabric pattern object identification in testing 70 different fabric motif images obtained 62 objects in the input image can be identified correctly (88.57%), while 8 objects in the input image cannot be identified (11.43%).

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