Cloud-based learning technologies play a central role in ensuring that learning and teaching practice can be conducted properly in the Covid-19 pandemic era. Although such technologies are advanced in mediating educational practice, the teacher’s acceptance toward the technology is usually far less to be understandable. This research aimed to investigate teachers’ acceptance toward cloud-based learning technology, in particular, for Microsoft 365 that currently being used massively in facilitating the educational process in Indonesia. In terms of theoretical framework, Technology Acceptance Model was employed with the inclusion of two original constructs, i.e., perceived usefulness (PU), perceived ease of use (PEU), and two extensive variables, i.e., perceived risk (PR) and social influence (SI) that will predict teacher’s intention to use the cloud-based learning technology during the Covid-19 pandemic. We collected data from teachers as research participants ( N = 75 ) using the online survey method and employed Structured Equation Modeling (SEM) using SmartPLS for quantitative data analysis. The result revealed significant relationship among factors affecting the use of the technology, except for PU (path coefficient = 0.120, p value = 0.415) and PEU (path coefficient = 0.224, p value = 0.147). Regarding this interesting result, we add final reflections and recommendations for future lines of works.

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding layer is then combined with a 100 node-hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6,632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross-validation, it has an accuracy score of 0.914, an F1-score of 0.914, a precision score of 0.916, and a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. The usage of multilingual BERT in the Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of clickbait detection are discussed.

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The quality of life for Indonesia's population can be measured from the human development index in each province. People who have a good quality of life indicate a prosperous life. The government has the responsibility to advance the welfare of the nation under the mandate of the constitution. The clustering of the human development index (HDI) in Indonesia is used to determine the distribution of quality of life or the distribution of social welfare. In this study, the K-Means method, which is a popular non-hierarchical clustering method, is used to classify human development in each province based on HDI indicators, namely Expected Years of Schooling, Mean Years of Schooling, Adjusted Per Capita Expenditure, and Life Expectancy at Birth. Provinces in Indonesia are clustered into 4 clusters. These results were also compared with the clustering based on HDI categories determined by Statistics Indonesia based on certain cut-off values. According to the HDI category, provinces in Indonesia fall into the medium, high, and very high categories. The results of the two groupings show that there is a trend toward appropriate characteristics for each group. Thus, K-Means can classify provinces in Indonesia according to the characteristics of the HDI indicators.

Birth interval is closely related to maternal and infant health. According to world health organization (WHO), the birth interval between two births is at least 33 months. This study is the first to discuss the short birth interval (SBI) in Indonesia and used data from the Indonesian Demographic and Health Surveys 2017 with a total of 34,200 respondents. Birth interval means the length of time between the birth of the first child and the second child. Categorized as SBI if the distance between births is less than 33 months. The variables used include mother's age, mother's age at first giving birth, father's age, household wealth, succeeding birth interval, breastfeeding status, child sex, residence, mother's education, health insurance, mother's working status, contraception used, child alive, total children, number of living children, and household members. Machine learning algorithms including logistic regression, Naïve Bayes, lazy locally weighted learning (LWL), and sequential minimal optimization (SMO) are applied to classify SBI. Based on the values of accuracy, precision, recall, F-score, matthews correlation coefficient (MCC), receiver operator characteristic (ROC) area, precision-recall curve (PRC) area, the Naïve Bayes is the best algorithm with scores obtained 0.891, 0.889, 0.891, 0.885, 0.687, 0.972, and 0.960 respectively. Additionally, 18.25% of mothers were classified as still giving birth within a short interval.