ObjectivesMalaria remains a public health concern worldwide, including Indonesia. Purworejo is a district in which endemic of malaria, they have re-setup to entering malaria elimination in 2021. Accordingly, actions must be taken to accelerate and guaranty that the goal will reach based on an understanding of the risk factors for malaria. Thus, we analysed malaria risk factors based on human and housing conditions in Kaligesing, Purworejo, Indonesia.MethodsA case-control study was carried out in Kaligesing subdistrict, Purworejo, Indonesia in July to August 2017. A structured questionnaire and checklist were used to collect data from 96 participants, who consisted of 48 controls and 48 cases. Univariate, bivariate, and multivariate analyses were performed.ResultsBivariate analysis found that education level, the presence of a cattle cage within 100 m of the house, not sleeping under a bednet the previous night, and not closing the doors and windows from 6 p.m. to 5 a.m. were significantly (p≤0.25) associated with malaria. Of these factors, only not sleeping under a bednet the previous night and not closing the doors and windows from 6 p.m. to 5 a.m. were significantly associated with malaria.ConclusionsThe findings of this study demonstrate that potential risk factor for Malaria should be paid of attention all the time, particularly for an area which is targeting Malaria elimination.

Clustering is a technique used to classify objects or cases into groups based on their similarity, called clusters or groups. Objects in each group tend to resemble each other and differ greatly (not the same) with objects from other clusters. Public welfare is a condition of fulfilling the material, spiritual and social needs of citizens in order to be able to live properly. Fuzzy Subtractive Clustering (FSC) method is a clustering algorithm that can form the number and centroid of clusters in accordance with data conditions. This study aims to determine the FSC results in grouping the level of welfare of the Indonesian people in 2017. The testing results of the cluster validity index show 2 values of Partition Entropy and Classification Entropy forming into 2 clusters that have the best value, indicating that the provincial group has a high welfare level and the provincial group has a low welfare level.