Judul: Design of intelligent decision support system model for sugar factory performance improvement analysis
Abstrak: Main problems faced by sugar factories in Indonesia are low productivity and efficiency. These two aspects became the most important factors in improving sugar factory performance. Performance improvement consist of performance measurement, performance objectives, and improvement priorities setting. Since analysis of performance improvement research in sugar factories Indonesia were very limited, more research in this area are required. The purpose of this research was to design an intelligent decision support system model for sugar factory performance improvement analysis. Aspects of performance being studied were strategic performance, operational performance, and tactical performance. Objects of research are small, medium and large scale sugar factories under PTPN X. Model and prototype of performance improvement analysis resulted from this study consist of five sub-models: classification, performance measurement, best performance selection, best practice analysis, and setting performance improvement priority. To verify and validate the model, we use performance data from sugar factories during 2008 and confirmation from experts. The implementation of model results indicate that: 1) intelligence decision support system model for the analysis of the performance improvement of the sugar factory is an integrated model to achieve the objective analysis of the performance improvement in terms of determining the performance, performance targets, and priorities for performance improvement; 2) model can be used to measure strategic performance, operational performance, and especially on the tactical performance of the internal process perspective of the sugar factory; 3) output of the model selection of the best performance set as the a minimum target performance can improve the performance of strategic and operational performance of the sugar factory; and 4) model can be used to determine the priority of the sugar factory improvements and suggestions for improvement.
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Judul: Design of intelligent decision support system model for sugar factory performance improvement analysis
Abstrak: Main problems faced by sugar factories in Indonesia are low productivity and efficiency. These two aspects became the most important factors in improving sugar factory performance. Performance improvement consist of performance measurement, performance objectives, and improvement priorities setting. Since analysis of performance improvement research in sugar factories Indonesia were very limited, more research in this area are required. The purpose of this research was to design an intelligent decision support system model for sugar factory performance improvement analysis. Aspects of performance being studied were strategic performance, operational performance, and tactical performance. Objects of research are small, medium and large scale sugar factories under PTPN X. Model and prototype of performance improvement analysis resulted from this study consist of five sub-models: classification, performance measurement, best performance selection, best practice analysis, and setting performance improvement priority. To verify and validate the model, we use performance data from sugar factories during 2008 and confirmation from experts. The implementation of model results indicate that: 1) intelligence decision support system model for the analysis of the performance improvement of the sugar factory is an integrated model to achieve the objective analysis of the performance improvement in terms of determining the performance, performance targets, and priorities for performance improvement; 2) model can be used to measure strategic performance, operational performance, and especially on the tactical performance of the internal process perspective of the sugar factory; 3) output of the model selection of the best performance set as the a minimum target performance can improve the performance of strategic and operational performance of the sugar factory; and 4) model can be used to determine the priority of the sugar factory improvements and suggestions for improvement.
Keyword: Performance Improvement Analysis, Intelligent Decision Support System, Artificial inteligence technology, Saccharum officinarum

Judul: Distribution and Diversity of Ectoparasite Mites on Geckos in Indonesia
Abstrak: Data on the diversity and dispersal of parasitic mites on house geckos in Indonesia are very scarce. In this work, the distribution and diversity of mites living on three species of house geckos, namely Cosymbotus platyurus, Hemidactylus frenatus, and H. garnotii collected throughout Indonesia, has been elaborated. Geckos and mites were captured and immediately preserved in 70% ethanol. Whole mount of the mites was prepared by clearing in lactophenol followed by mounting on polyvinyl lactophenol solutions. The SEM preparation was conducted to examine the detail morphological characters of the mites. The results showed that among 448 individuals of geckos, 221 geckos were infected by Geckobia mites. Prevalences of C. platyurus, H. frenatus, and H. garnotii infested by mites were 14.8%, 50.69%, and 79.6%, respectively. Three different spesies of Geckobia (G1, G2, and G3) could be differentiated; and based on similarities of their morphological characters to ones described in published literatures, Geckobia G2 is Geckobia glebosum and Geckobia G3 is Geckobia bataviensis while G1 could not be identified to the species level. The highest mean intensity of Geckobia G1 infestation was found on H. garnotii (I=7.0), G. glebosum infestation on H. frenatus (I=3.5), and G. bataviensis infestation on H. garnotii (I=11.8). In general, C. platyurus was infested by the least number of mites. Geckobia G1 were found living on the skin folds on the claws, G. glebosum were found mainly on the body and thigh, and G. bataviensis were found on almost all parts of the host’s body. Geckobia mites are distributed randomly throughout Indonesian Archipelago, following the pattern of distribution of their hosts. So it is concluded that Geckobia G1, G. glebosum, and G. bataviensis are sympatric.
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