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The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy > = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear.

Most research concerning user satisfaction in using Information Technology focuses on user feedback resulting from the use of information systems as a tool to retrieve or produce information. In this case, information technology facilities provide offered hardware and software that can be used by customers to produce information, such as data analyzing software, without considering the specific needs of users. This research focused on customer responses when IT service providers understand their needs before giving them service, which includes hardware, software, staff, room and utilities provided by the information technologyservice provider. The factors that affect the level of user satisfaction were analyzed and the result showed, firstly, that there are three factors that affect the level of user satisfaction, which are: staff service, the utility that is gained, and the equipment system. Secondly, a model has been developed to information technology facilityservice.

In the field of remote sensing, in addition to the weather forecast, atmospheric dynamics, oceans, cloud cumulonimbus, and Tornado are part of the phenomenon of chaos. Because in the clouds cumulonimbus, there are some layers with a gray border indicating irregular and uncertain. There is a boundary line on the layers of Cumulonimbus Clouds that could be identified based on the pixel where the differences in the intensity values are extremes. A cloud layer cumulonimbus with a gray edge border can be used as the basis for predicting the occurrence of a tornado based on a pixel location that has specific characteristics. In this research, a Supervised Image Classification algorithm with Spectral Angle Mapper was performed to get the minimum and maximum pixel intensity interval values based on spectral angles in cumulonimbus clouds. Spectral angles allow for quick mapping in determining the spectral similarities between two spectrums on cumulonimbus cloud layers. The spectral similarities are calculated by referring to the angle between the spectral forming the same dimensional vector space on the RGB color spectrum. Early detection in cumulonimbus cloud layers will indicate the occurrence of chaos phenomenon, which could be used to predict tornadoes. The results showed that the Spectral Angle Mapper approach gave minimum and maximum pixel intensity values interval of the Average Correlation Angle in the dataset image Cumulonimbus Cloud with a classification accuracy value of 95.83%.

This article aims to develop an expert-based interest-based measurement model to provide an overview of the interests that can assist in decision making of vocational interest decisions in the vocational field to be on the right target. The method used by designing vocational interest measurement instrument by producing four personality types used as knowledge base is Tangible, Thinking, Flexible and Entrepreneur (TTFE) and integrated with expert system concept which makes a practical and efficient measurement model. The generated interest measurement model can help students quickly to overview the interest in decision making majors for higher education, can conduct online consultation, documentation, and can be used as a documentation file consultation portal at an institution.