Issi

This study aimed to analyze the effect of macroeconomic variables on Indonesian Islamic stock index. Macroeconomic variables used is the interest rate of Bank Indonesia (BI-rate), inflation, exchange rate, Bank Indonesia Certificates Sharia (SBIS) and world oil prices. The data used in this research is secondary data during the period May 2011 until September 2015 using a model error correction model (ECM) where the end result is going to measure the effect of macroeconomic variables on Indonesian Sharia Stock Index in the short term and long term.

We argue in favour of artificial neural networks for exploratory data analysis, clustering and mapping. We propose the Kohonen self-organizing map (SOM) for clustering and mapping according to a multi-maps extension. It is consequently called Multi-SOM. Firstly the Kohonen SOM algorithm is presented. Then the following improvements are detailed: the way of naming the clusters, the map division into logical areas, and the map generalization mechanism. The multi-map display founded on the inter-maps communication mechanism is exposed, and the notion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization, exploration or browsing, and moreover for scientific and technical information analysis. A case study in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also show that the inter-map communication mechanism provides support for watching the plants on which patented genetic technology works. It is the first map. The other four related maps provide information about the plant parts that are concerned, the target pathology, the transgenic techniques used for making these plants resistant, and finally the firms involved in genetic engineering and patenting. A method of analysis is also proposed in the use of this computer-based multi-maps environment. Finally, we discuss some critical remarks about the proposed approach at its current state. And we conclude about the advantages that it provides for a knowledge-oriented watching analysis on science and technology. In relation with this remark we introduce in conclusion the notion of knowledge indicators.

We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.