Pelatihan Berpikir Komputasional untuk Peningkatan Kompetensi Guru Telkom Schools sebagai Bagian dari Gerakan PANDAI

Berpikir komputasional (BK) atau computational thinking (CT) merupakan salah satu keahlian esensial yang diperlukan sumber daya manusia Indonesia dalam rangka menghadapi revolusi industri 4.0 dan masyarakat 5.0. Gerakan PANDAI (Pengajar Era Digital Indonesia) merupakan suatu gerakan nasional yang merupakan kolaborasi nirlaba antara komunitas Bebras Indonesia, Kementerian Pendidikan dan Kebudayaan Indonesia, dan Google Indonesia dalam rangka meningkatkan kompetensi BK yang dimiliki oleh guru sekolah dasar dan menengah. Pada tahun 2022, Biro Bebras Universitas Telkom mengadakan pelatihan BK kepada lebih dari 60 guru Telkom Schools sebagai bagian dari gerakan ini. Pelatihan ini terdiri dari lima tahapan besar yang meliputi lokakarya luring, pembelajaran mandiri, lokakarya daring, dan dua kegiatan microteaching. Hasil analisis kuantitatif menunjukkan peningkatan kemampuan konseptual peserta terkait BK, meskipun masih banyak hal yang perlu dibenahi dari sisi kemampuan teknis dalam pengerjaan soal-soal BK

Prosiding konferensi nasional sistem informasi 2007 : information system bridging gap between theories and practices

xiv, 727 hlm.; ilus.; 24 c

Prosiding konferensi nasional sistem informasi 2007 : information system bridging gap between theories and practices

xiv, 727 hlm.; ilus.; 24 c

Berbagai makalah sistem informasi

Buku ini merupakan perpaduan ilmu pengetahuan, perpaduan antara seni dan ilmu pengetahuan, perpaduan keduanya akan menciptakan peluang dalam situasi yang dinamis guna mencapai tujuan yang di inginkanxvi,728 hlm.; 25 c

Matrix Factorization for Package Recommendations

Research in recommendation systems has to date focused on recommending individual items to users. However there are contexts in which combinations of items need to be recommended, and there has been less research to date on how collaborative methods such as matrix factorization can be applied to such tasks. The research contributions of this paper are threefold. First, we formalize the collaborative package recommendation task as an extension of the standard collaborative recommendation task. Second, we describe and make available a novel package recommendation dataset in the clothes domain, where a combination of a “top” (e.g. a shirt, t-shirt or top) and “bottom” (e.g. trousers, shorts or skirts) needs to be recommended. Finally, we describe several extensions of matrix factorization to predict user ratings on packages, and report RMSE improvements over the standard matrix factorization approach for recommending combinations of tops and bottoms

Understanding how to Explain Package Recommendations in the Clothes Domain

Recommender system explanations have been widely studied in the context of recommending individual items to users. In this paper, we present and evaluate explanations for the more complex problem of package recommendation, where a combination of items that go well together are recommended as a package. We report the results of an empirical user study where participants try to select the most appropriate combination of a “top” (e.g. a shirt) and a “bottom” (e.g. a pair of trousers) for a hypothetical user based on one of 5 types of explanation communicating item-feature preferences and/or appropriateness of feature combinations. We found that the type of explanation significantly impacted decision time and resulted in selection of different packages, but found no difference in how participants appraised the different explanation types

Bumblebee friendly planting recommendations with citizen science data

Several citizen science projects engage with the public around pollinator species, typically requesting data (e.g. in the form of photo-records of different species tagged by place and date). While such projects help scientists collect data, these data are rarely fed back to the public in any meaningful manner. In this paper, we address this through a recommender system based on Matrix Factorization over a matrix of observed bumblebee-plant interactions derived from data submitted to a citizen science project BeeWatch. The system recommends pollinator-friendly plants for domestic gardens and takes into account both the fact that different bumblebee species exhibit differing preferences for flowers, and that plants flower at different times of the year. The goal is to attract a range of bumblebee species to a garden and to ensure that these species have sufficient food sources through the season