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ObjectivesThe number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults.MethodsThe process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3.ResultsThe evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease.ConclusionsThe research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

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Background The main obstacle for local and daily or weekly time-series mapping using very high-resolution satellite imagery is the high price and availability of data. These constraints are currently obtaining solutions in line with the development of improved UAV drone technology with a wider range and imaging sensors that can be used. Findings Research conducted using Inspire 2 quadcopter drones with RGB cameras, developing 3D models using photogrammetric and situation mapping uses geographic information systems. The drone used has advantages in a wider range of areas with adequate power support. The drone is also supported by a high-quality camera with dreadlocks for image stability, so it is suitable for use in mapping activities. Conclusions Using Google earth data at two separate locations as a benchmark for the accuracy of measurement of the area at three variations of flying height in taking pictures, the results obtained were 98.53% (98.68%), 95.2% (96.1%), and 94.4% (94.7%) for each altitude of 40, 80, and 100 m. The next research is to assess the results of the area for more objects from the land cover as well as for the more varied polygon area so that the reliability of the method can be used in general

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Penelitian yang dilakukan bertujuan mengembangkan sistem informasi geografis (SIG) yang dapat digunakan untuk mengelola data dan menyajikan informasi kegempaan. Metodologi penelitian terdiri dari pengumpulan data spasial dan data non spasial dan perancangan sistem. Perancangan sistem terdiri dari perancangan aliran data, perancangan basisdata, perancangan menu dan perancangan layar. Kesimpulan yang dapat diambil dari penelitian ini adalah bahwa SIG yang dikembangkan dapat menampilkan informasi mengenai bagaimana gempa yang pernah dan sedang terjadi dengan parameter magnitudenya dalam bentuk visual sesuai kondisi yang ada. SIG ini dirancang menggunakan basisdata sendiri dan memiliki kapasitas untuk melakukan pengolahan data spasial dan non-spasial untuk menghasilkan sebuah sistem peringatan dini tsunami. Sistem ini juga dapat mempermudah dan mempercepat penyampaian informasi kegempaan kepada masyarakat.

Indonesian genetic variability of rubber germplasm has been enriched by conserving of the clones from the IRRDB (International Rubber