K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method.  The object detected outliers are then removed.  Then new centroid for each group is obtained using the K-Means method again. This dataset was taken from the FIRM are provided by the National Aeronautics and Space Administration (NASA).  Clustering was done by varying the number of clusters (k = 10, 15, 20, 25, 30, 35, 40, 45 and 50) with cluster optimal is k = 20. The result based on the value of Sum of Squared Error (SSE) shown the LOF K-Means method was better than the K-Means method. 

Flash flood led to high levels of water in the urban areas, causing many problems such as bridge collapse, building damage and the victim died. It is impossible to avoid risks of floods or prevent their occurrence, however, it is plausible to work on the reduction of their effects and to reduce the losses which they may cause. The objective of this paper is to generate a flash flood map in Suoh region, using satellite images, UAVs images and GIS tools. Analytical Hierarchical Process is used to determine the relative impact weight of flood causative factors to get a composite Flood Hazard Index (FHI). The causative factors in this study are flow accumulation (F), rainfall intensity (I), geology (G), land use (U), slope (S), and elevation (E). The presented methodology has been applied to an area in Suoh region, where recurring flood events have appeared. Initially, FIGUSE method resulted in an FHI and a corresponding flood map. A sensitivity analysis on the parameter’s values revealed some interesting information on the relative importance of each criterion, presented and commented in the discussion section.

Test of Teaching English of Foreign Language or TEFL is one of the scourges for students who must take this test for their graduation term. Many of them got a lack of motivation to learn, especially engineering students. Reading comprehension is one of the tests in TEFL test. However, many students failed in this section because they did not understand the text well and they had less of vocabulary. The students needed a mobile application which able to bring everywhere and they could learn the material and do the exercise anytime. Mobile application based on Android platform is the answer to help the students. In this application the students would learn the material of reading comprehension and do the exercise which divided into 3 levels; from the easier to the hardest.

No abstract

Particle Swarm Optimization (PSO) is the population-based optimization algorithm and the generation of random values. The deficiency of the PSO algorithm is prematurely convergent, meaning it quickly finds solutions to local solutions. PSO tidak mampu untuk mencari ruang solusi lebih luas. PSO can not afford to search for wider solution space. In this study modification of the combination of PSO with Genetic Algortihm (GA) or we call M-PSOGA. The advantage of GA taken is to find a wider solution space. M-PSOGA is evaluated on non-linear function problem. The results obtained by M-PSOGA produce the best solution from its predecessor method, PSO and PSOGA. Better on the results of the solutions obtained and the convergent velocity on global solutions.Keywords: Particel Swarm Optimization, Genetic Algorithm, Non-Linier Function.