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The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have four research clusters consisting of vocational education, system development, learning media, and vocational learning systems.

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Salah satu dampak dari pandemi COVID-19 adalah pemberlakuan pembelajaran daring. Kegiatan pembelajaran daring banyak mengalami kendala, sehingga menyebabkan tidak tercapainya kompetensi pembelajaran dengan baik. Kemudian pemerintah memberlakukan program Pertemuan Tatap Muka Terbatas (PTMT), dan banyak tanggapan masyarakat terhadap program tersebut. Untuk mengetahui bagaimana persepsi masyarakat terhadap PTMT, kita dapat melakukan analisis terkait PTMT melalui media sosial. Karena media sosial merupakan salah satu media yang paling banyak digunakan oleh masyarakat selama pandemi untuk berkomunikasi, menyampaikan pendapat, mencari berita, dan lain-lain. Adapun analisis tren PTMT dari persepsi warganet yang dapat dilakukan adalah malalui topic modeling. Dengan mengambil data dari Twitter terkait PTMT menggunakan API (Application Programming Interface), selanjutnya memproses topic modeling dengan metode Latent Dirichlet Allocation (LDA), sebuah metode yang paling populer dan banyak digunakan pada penelitian text mining. Hasil pemodelan topik menghasilkan sepuluh klaster topik dengan nilai koheren 0,50344, dengan tiga topik yang paling banyak dibicarakan oleh warganet. Yakni tentang banyaknya tugas yang harus dikerjakan di luar jam sekolah, mereka sudah mencapai titik kebosanan menjalani sekolah dari rumah hingga menyebabkan kemalasan, dan banyak warganet yang membicarakan bahwa mereka akan melaksanakan kegiatan PTMT.

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This research is motivated by the problem of student learning interaction and communication in online learning which is low with inconsistent learning achievement between personal and classical tests. The research objective is to improve students' communication skills through integrated online project-based learning. The research subjects are students who take the odd semester Educational Research Methodology (MPP) course in 2021-2022 in the Informatics Engineering Education study program, Faculty of Engineering, Yogakarta State University.The results showed that the discussion of problem solving was felt by most of the students to be very helpful in mastering the material and completing projects. The improvement of communication skills in the first cycle was 23.53% in the less category and 76.47% good to 100% good and very good in cycle 2. The largest increase in mastery of the material, but still the lowest communication indicator achievement score.

This study aims to explain the development of adaptive MOOCs that support personalized learning. This study was designed with a mixed method design of sequential explanatory type at the association level. Quantitative analysis used confirmatory factor analysis (CFA) (n = 110) and was deepened with qualitative analysis of the Miles and Huberman model. Quantitatively measured domains include accessibility, learning curriculum, competence, motivation, satisfaction, efficacy, and self-study. The domain was used as a reference for qualitative data mining through focus group discussions (FGD) involving lecturers and doctoral students (n = 25). The analysis results show that the curriculum domain and one of the motivational indicators should be removed because it did not meet the requirements after bootstrapping. The second running algorithm showed all valid and reliable variables. Some domains that significantly affect MOOC user satisfaction are efficacy, competence, and motivation. R square results showed 37% influenced by motivation, accessibility, efficacy, and self-study, and the rest influenced by other variables. In the qualitative analysis, 19 subcodes were found that were included in the three main codes. In conclusion, there is new information in the accessibility domain that expands quantitative data, including information on MOOCs, marketing traps, regulation, and dropouts. Meanwhile, what strengthens and deepens quantitative data is found in the information on metacognitive and personalized coding that strengthens the domain of efficiency, the domain of competence, which is strengthened by content, mentoring collaboration, and motivation reinforced by coding the user's motivations and goals.