The Effectiveness of Salt Business Empowering Program (Pugar) on Salt Farmers’ Income Improvement in Kedungmutih Village of Demak Regency

Indonesian national low productivity of salt which is unequal between the need level and salt consumption eventually results in salt import made mainly to meet the industrial needs. The government through the Ministry of Marine Affairs and Fisheries has released a Public Salt Business Empowering Program (known as PUGAR/Pemberdayaan Usaha Garam Rakyat). PUGAR is an empowering program focusing on the improvement of job opportunities and welfare for salt farmers/managers to achieve the self-sufficiency of salt for consumptions and industries. This research aims at examining the influence of salt business empowering program on the salt farmers’ income improvement in Kedungmalang village of Jepara Regency; the implementation level of salt business empowering program in Kedungmutih Village of Demak Regency when compared to that in Kedungmalang Village of Jepara Regency; and the influence of salt business empowering program on the salt farmers’ income improvement in Kedungmutih village of Demak regency. The research was conducted using a descriptive method with a case study approach. The program implementation was quantitatively analyzed. Furthermore, the effectiveness of PUGAR on income was analyzed using T-test. The test result showed that there were income differences in Kedungmutih and Kedungmalang Village before and after the implementation of PUGAR. This finding showed that PUGAR was proven as an effective program to improve the salt farmers’ welfare through income improvement.

Spatio – Temporal Data Mining Framework for Natural Resource Exploration

Technology has allowed for a substantial increase in success rate of identifying the presence of energy sources such as oil and natural gas. Data mining, an emerging technology characterized by significantly advanced analytical tools, can contribute to this success rate as it has the potential to guide or at least assist opportunists in hydrocarbon prediction. To make a prediction about presence of hydrocarbon reserve beneath the surface of earth involves geological, geochemical, seismic and microbial prospecting. The test methods involved in the mentioned process need a great deal of cost and time. This project is aimed at developing decision support system to improve the process of hydrocarbon need evaluation and reserve detection by integrating the methods and tools from data mining and potential surface analysis to approach the problem from an interdisciplinary stance. In the thesis, the world countries are classified with respect to sustainable energy development with the underlying assumption that hydrocarbon is the major source of energy all over the world. The addressed question is whether the hydrocarbon reserves in the world comply with its consumption? As a result, two possibilities arose to ensure energy sustainability: (1). To provide an optimal framework for improvement in hydrocarbon exploration process. (2). To provide a framework for improvement in hydrocarbon consumption. The study is about the aforementioned. The presence of hydrocarbon reserves beneath earth‘s surface is predicted on the basis of either (a). Surface indicators or (b). Beneath surface parameters. The surface indicators which are considered in this project may consist of geological and microbial indicators. In state of the art geological and microbial methodologies, the cost and time involved is in affordable. The research attempts to replace geological predictions with intelligent remote sensing and microbial indications with microbe data mining. But the existing techniques of data mining cannot produce desired accuracy if applied to surface indicators database. Some data mining techniques for mining temporal spatial and non spatial data related to surface indicators of hydrocarbon reserves are proposed. The model includes the classification mechanism of world countries on the basis of sustainable hydrocarbon development and then extraction of useful patterns from surface indicators to predict hydrocarbon reserve in a time and cost effective manner. A series of empirical investigations have been made to evaluate the performance of proposed techniques using different and diverse databases that show the effectiveness of methodology.