Edy Irwansyah

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier

Objectives The 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. Methods The 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. Results The 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. Conclusions The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

Earthquake hazard zonation using peak ground acceleration (PGA) approach

The objective of this research is to develop seismic hazard area zones in the building infrastructure of the Banda Aceh City Indonesia using peak ground acceleration (PGA) measured using global and local attenuation function. PGA is calculated using attenuation function that describes the correlation between the local ground movement intensity the earthquake magnitude and the distance from the earthquakes epicentre. The data used comes from the earthquake damage catalogue available from the Indonesia meteorology, climatology and geophysics agency (BMKG) with range from year 1973 – 2011. The research methodology consists of six steps, which is developing the grid, calculation of the distance from the epicentre to the centroid of the grid, calculation of PGA values, developing the computer application, plotting the PGA values to the centroid grid, and developing the earthquake hazard zones using kriging algorithm. The conclusion of this research is that the global attenuation function that was developed by [20] can be applied to calculate the PGA values in the city of Banda Aceh. Banda Aceh city in micro scale can be divided into three hazard zones which is low hazard zone with PGA value of 0.8767 gals up to 0.8780 gals, medium hazard zone with PGA values of 0.8781 up to 0.8793 gals and high hazard zone with PGA values of 0.8794 up to 0.8806 gals.

Data Clustering and Zonationof Earthquake Building Damage Hazard Area Using FKCN and Kriging Algorithm

The objective of this research is to construct the zonation of earthquake building damage hazard area using fuzzy kohonen clustering network (FKCN) algorithm for data clustering and kriging algorithm for data interpolation. Data used consists of the earth data in the form of peak ground acceleration (PGA), lithology and topographic zones and Iris plant database for algorithm validation. This research is comprised into three steps which are data normalization, data clustering and data interpolation using FKCN and kriging algorithm and the construction of zonation. Clusterization produces three classes of building damage hazard data. The first class is consisting of medium PGA,dominantby high compaction lithology in the topography of inland area. The second class with low PGA, dominant low compaction lithology in the lowland topographic zone and the third class with high PGA, dominant by un-very low compactionlithology in swamp topographic zone. Banda Aceh cityas location sample is divided into three building damage hazard zone which is high hazard zone, medium hazard zone and low hazard zone for building damage which is located towards inland area.

Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Morans I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to sup...

Ordinary Kriging and Spatial Autocorrelation Identification to Predict Peak Ground Acceleration in Banda Aceh City, Indonesia

Peak ground acceleration (PGA) is a measure of earthquake acceleration in the ground. The prediction information about PGA is important to minimize the effect of earthquake. The method for prediction is Ordinary Kriging. It is geostatistic method used to predict data in certain locations which have autocorrelation. The sample data used in this research are PGA in Meuraxa, Banda Aceh 2006. The steps of research methodology consist of autocorrelations identify by Moran’s I and LISA, build semivariograms, and prediction by Ordinary Kriging. The results is Ordinary Kriging can be applied to predict PGA. It was shown by evaluate of mean and MSE value. According to mean value of three prediction, all models (Gaussian, Spherical, and Exponential) have mean 0,3534; 0,3584; and 0,3555 which approaches the actual PGA mean 0.34. According to MSE value, it can be seen that all models have small MSE or relatively closed to zero.

Earthquake Ground Motion Attenuation Modeling Using Levenberg-Marquardt and Brute-Force Method

In this paper, we discuss the results of research on the optimization modeling of ground motion attenuation in the subduction zone of the model Youngs et al. [1] using two methods: the Levenberg-Marquard and Bruce-Force method. This modeling is particularly important in the case of seismicity. Given that it takes a good model for predicting the strength of earthquakes in order to reduce the risk of the impact of natural disasters. Two major contributions of this study are ground motion attenuation model specific to the subduction zone that has been optimized with the Levenberg-Marquard method and Bruce-Force uses a model Youngs et al. [1] and a proof that the Levenberg-Marquard method for optimization model is better than Bruce-Force method. The Levenberg-Marquardt method has been proven to provide more accurate results on the modeling of ground motion attenuation which is indicated by a very small deviation between the values of PGA predictable results with the PGA actual values.

Computer-Based Ground Motion Attenuation Modeling Using Levenberg-Marquardt Method

In this paper, we present the results of research on the optimization modeling of ground motion attenuation of the two establish models by Youngs et al. [25] and the model of Lin and Lee [13] using the Levenberg-Marquardt method. This modeling is particularly important in the case of ground motion given that it takes a good model for predicting the strength of earthquakes in order to reduce the risk of the impact of the natural disaster. There are two main contributions of this research is the optimization of ground motion attenuation models with Levenberg-Marquardt method on two models that have been extensively used and the development of computer applications to help accelerate modeling, especially on large data with an area of extensive research. Levenberg-Marquardt method proved to give a good contribution to the modeling of ground motion attenuation that is indicated by the very small deviations between the predicted values with the actual value.