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Journal : Jurnal Computech

PENGEMBANGAN FRAMEWORK MOBILE LEARNING PADA PERTANIAN SAYURAN Erlangga Erlangga; Herbert Siregar; Yaya Wihardi
Jurnal Computech & Bisnis (e-Journal) Vol 13, No 2 (2019): Jurnal Computech & Bisnis
Publisher : STMIK Mardira Indonesia, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1391.864 KB) | DOI: 10.55281/jcb.v13i2.200

Abstract

In vegetable production, farmers face many obstacles, such as the problems related to seeds, pest and disease control, commodity prices, and marketing of produces. There is almost no useful information and technology easily accessible to improve the situation. With the better penetration of the Internet to the villages and the wide availability of inexpensive mobile devices, mobile learning provides a good solution. This study is aimed to create a mobile learning framework that provides information and interactive communication about vegetable production needed by farmers. The method used was instructional design of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). Usability surveys of the proposed prototype to farmers, extension agents (field technical assistants), and researchers, result in 79.4%, 87.3%, and 87% satisfaction testing, respectively, in information needs fulfillment. Summative Testing in the aspect of User Acceptance Testing of validity and reliability indicated that the prototype could be used by farmers. Based on the assessment by experts, 87.3% of them agreed that the mobile learning framework for vegetable farming could provide learning information about vegetable production. Keywords: Mobile Learning, Mobile Learning Framework, Mobile Learning Agriculture, ADDIE (Analysis, Design, Development, Implementation, Evaluation)DOI: http://doi.org/10.5281/zenodo.3631040
Temu Kembali Citra Berbasis Konten Pada Citra Lintas Domain Yaya Wihardi; Herbert Siregar; Ali Mulyawan
Jurnal Computech & Bisnis (e-Journal) Vol 12, No 1 (2018): Jurnal Computech & Bisnis
Publisher : STMIK Mardira Indonesia, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55281/jcb.v12i1.169

Abstract

This research aimed to develop a cross domain content based image retrieval system by using Histogram of Oriented Gradient (HoG) features. To compute visual similarity, we use Gaussian Approximation for Fast Image Similarity (GAFIS), Online Algorithm for Scalable Image Similarity Learning (OASIS), and Hybrid method that combine both of GAFIS and OASIS. The results show that the hybrid method outperforms the others, it is because the methods could extract uniqueness of each image query and image database by utilizing Gaussian parameters from negative-set and combine it with OASIS parameters Keywords : Image Retrieval, CBIR, Gaussian Approximation, OASIS.