Madrasah teachers are currently considered to have lower cognitive competence when compared to formal school teachers in general, although this statement is not entirely correct. Researchers have conducted computational thinking training in several madrasa of Central Java Provinces, like MIN 1 Kendal, MTs N 1 Jepara, and MAN 1 Grobogan. Computational Thinking (CT) involves problem solving and system design by breaking it down into several stages that are effective, efficient, and comprehensive, including decomposition, pattern recognition, abstraction, and algorithms which are some of the basic concepts of computer science. The purpose of this study is the implementation of CT by madrasa’ teachers on each lesson to students in order to increase student learning interest. The research method used is blended learning which is a combination of an online course (introduction to Bebras Indonesia and CT) and an onsite course (training on CT and implementation of CT to students). The results showed that there was an increase in the average score of the trainees between the pre-test and post-test of the teachers at MIN 1 Kendal, MTs N 1 Jepara, and MAN 1 Grobogan i.e. 70.23%, 70.01% and 80. 64%, respectively. Furthermore, student testimonials after the implementation of CT in subjects taught by the majority of teachers at 66.79% filled in very interesting so that CT learning was very effective in increasing student learning interest in madrasah.
The purpose of this study was to analyze the quality of groundwater in terms of physical, chemical, and microbiological parameters at three campus locations of the Universitas Islam Negeri (UIN) Walisongo Semarang, Indonesia then the results were compared to the Regulation of the Ministry of Health of the Republic of Indonesia (Permenkes) Number 32 of 2017 regarding the water quality standards and environmental health. Principal Component Analysis (PCA) is one of chemometric modelling which can be used to analyze correlations among different physical, chemical and microbiological parameters to assess the groundwater quality at the water intake of dug wells as the main source of water in this study. The content of organic and inorganic materials are then associated with environmental factors, research activities and industrial pollution around campus. The implementation of PCA data in the water intake at dug wells offers new possibilities for improved quality assurance and control procedures for dug wells management and its strategy. Before Cluster Analysis (CA) and PCA methods are applied, the data was checked the normal distribution using the Saphiro-Wilk test. Furthermore, three different patterns of water quality based on groundwater characteristics and anthropogenic effects were found by CA and PCA methods.
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