Bebras Computational Thinking Challenge atau Tantangan Bebras merupakan suatu kegiatan kompetisi untuk mengukur kemampuan cara berpikir komputasi dengan cara menyelesaikan soal-soal mengenai computational thinking yang disajikan melalui uraian dengan disertai gambar yang menarik. Sekolah Tinggi Teknologi Garut sebagai salah satu Biro dari Bebras Indonesia telah menyelenggarakan kegiatan ini sejak tahun 2016 hingga saat ini. Pada Tantangan tahun 2020 jumlah peserta Bebras Computational Thinking Challenge berjumlah 724 siswa yang terdiri dari 95 siswa Sekolah Dasar/MI, 313 siswa Sekolah Menengah Pertama/MTs, 316 siswa Sekolah Menengah Atas/SMK/MA. Terdapat 42 sekolah yang mengikuti kegiatan Tantangan Bebras tahun 2020. Hasil kompetisi menunjukkan kemampuan berpikir komputasi pada siswa-siswa di Garut sudah cukup tinggi dengan pencapaian nilai 100 untuk kategori Sikecil dan 90,28 untuk kategori Siaga.

Cryptocurrency trends, especially Bitcoin, have gained a place in a group of people and there are even countries that already use Bitcoin as a legal transaction tool. The dynamics that occur in this Bitcoin trend make many new users. This lack of understanding of the technology can cast doubt on those who want to get started, so it is necessary to conduct sentiment analysis to increase knowledge of what Bitcoin is and how it works. This study aims to conduct a Sentiment Analysis regarding Bitcoin through Twitter social media, so that their opinion on this technology will be known. The method used is by using Tweet data that has been downloaded on the www.data.world.com website. The data is the result of using the Crawling technique, then sentiment analysis is carried out to classify a tweet into Neutral, Positive, or Negative. The results showed that from the 1998 dataset, 46.69% were classified as Neutral, then Positive, 43.54%, and 9.75% Negative.

Startup is becoming a trend in Indonesia. Various success stories from local startups such as Gojek, Tokopedia or Bukalapak, has become trigger for the emergence of new startups. The potential of internet users in Indonesia which is increasing from year to year is also a catalyst for establishing a startup. At an early stage, startup must identify user experience priorities and problems in developing software product phase. This chapter describes work in progress on a shortened version of Design Sprint approach, and its application to designing Software product in Startup. Google Ventures initially introduced Design Sprint to tackle critical business problems and come up with viable solutions within five days.

The articles aim to analyze and design of human resource information system (HRIS) for micro small and medium enterprises in Indonesia. The problem some times occurred happen in human resource when the business starts to grow. To solve this start with analyzing and design in this HRIS system using RUP (Rational Unified Process) to handle a problem such as attendance, payroll, manage employee and other Human resource problem in the scope of small or medium business. The result is a design of HRIS that can be a start for a blueprint and can be implemented for micro small and medium enterprises with condition and regulation that fit in Indonesia and also will provide the results of a design document analyzing the business needs of the process and the design of the HRIS system using the RUP method.

The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.