The Analysis of Factor-factor That Affect the Safety Stock in Kapok Seed Oil Company

This research examines about analysis safety stock of Kapok Seed Oil in UD. Gajah Mas Company. The objectives of the research is to know the level of risk availability of raw materials and also stock for this Kapok Seed Oil processing in this company. Factor-factor research uses qualitative and secondary data, The data will be collected by interview and literatures review. The outcome is the analysis of that affect to safety stock in UD. Gajah Mas Company. Keywords : safety stock, kapok seed oil, qualitative data, secondary data, UD. Gajah Mas Compan

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Application of Factor Analysis to Public Sector Integrity in Indonesia

The main purpose of this study is to analyze interrelationships among variables used on the survey of public sector integrity by Indonesiaâs Corruption Eradication Commission (Komisi Pemberantasan Korupsi, KPK). The nine variables include corruption experiences, corruption perceptions, working environments, administration systems, the behavior of individuals, corruption prevention efforts, integrity experiences, integrity potencies, and integrity total. Using factor analysis, the approach is to explain these variables in terms of their common underlying dimensions, well-known as factors. Technically, factor analysis involves condensing the information contained in a number of original variables into a smaller set of new composite factors with a minimum loss of information. The results show that based on eigen values the first factor alone accounts for 70.7% of the common variance. The second factor alone accounts for 13,4%. The common variance of the nine variables explained by two factors is 84.1%. Using the varimax rotation and based on values of factor loadings the first factor makes high contribution to the variance of corruption experiences, corruption perceptions, working environments, the behavior of individuals, integrity experiences, and integrity total variables. The second factor makes high contribution to the variance of corruption prevention efforts and integrity potencies variables. Similar results, also, are obtained by quartimax rotation and equamax rotatio