Judul: Perencanaan Pendirian Industri Kompos Berbahan Baku Sampah Oganik dan Lumpur Air Limbah (Studi Kasus pada Dinas Kebersihan DKI Jakarta)
Abstrak: Jakarta setiap harinya menghasilkan 25 404 m3 sampah, dimana 73.92 persen diantaranya merupakan sampah organik. Dari jumlah tersebut yang dapat ditangani oleh Instansi Dinas Kebersihan DKI Jakarta, baru mencapai 18.583 m3 (Dinas Kebersihan DKI Jakarta, 1995). Penanganan sampah yang dilakukan sampai dengan akhir tahun 1995, menggunakan sanitary landfill di daerah Bantar Gebang, Bekasi dengan luas 108 Ha dan diperkirakan akan penuh dalam waktu 15-20 tahun mendatang.
Keyword: 

Judul: Formulasi kebijakan sistem pengolahan sampah perkotaan berkelanjutan
Abstrak: Solid waste is still considered as worthless goods, so that it is still causing many problems. The purpose of this study is to formulate a sustainable urban waste management policy. The study was conducted in DKI Jakarta. The research is devided into four stages of analyses which include the availability and needed of land for a solid waste treatment, waste treatment technology optimization analysis of environmentally friendly, multi-criteria evaluation and policy analysis. Indonesia has many waste management policy, but not synergize together and not yet operational. Solid Waste management in DKI Jakarta has led to efforts to minimize waste, and waste reduction programs integrated between recycling, composting, combustion (incineration) and waste disposal system with a system of sanitary landfills, and will be pursued to zero waste program. DKI Jakarta needs to improve management efficiency and improved quality of service to the community, one with separation of regulator and operator functions, but managed by one institution to improve its performance and to cover the financing gap, ideally to cooperate with the private sector. The result of regression analysis shows a significant effect of economic growth in waste generation and characteristics. Increased in prosperity will increase the amount of inorganic waste, and lower amount of organic waste. Increased inorganic waste will increase the calorie content which is more beneficial if done with incinerator waste to energy (WTE), while providing the understanding that the waste is a resource that can be utilized as an energy alternative to electricity. Production of electricity from WTE incinerator is influenced by the calorie content, and type of waste. WTE incinerators have the advantage in processing speed and the required land area, but WTE incinerator required high initial investment. The result of CBA analysis shows that WTE incinerator is more expensive, but it is the most cost effective in the long term (25 years). The variables have a very strong influence on the cost of processing waste is waste transportation costs. In the context of environmental, sanitary landfill waste treatment system, which is placed far away from services area, can lead to a much larger greenhouse gases and generate leachate. The existence of designated land use and changes that are uncontrolled by DKI Jakarta complicate the placement of garbage processing unit. WTE technology or HRC which indoor system is more acceptable to local communities, compared to the open SLF. Incinerator technology is more efficient for processing large scale with a capacity of more than 500 tons / day, with the optimal point on the capacity of 3000 tons / day, which is supported by segregation. Organic waste treatment system is most optimal at high-rate composting system (HRC). WTE processing unit and HRC, should be placed in administrative of DKI. The sensitivity analysis shows that if the government would buy in a good price the electricity production and energy from solid waste, will raise the level of feasibility WTE incinerator system. Implementation of WTE incinerator technology, need to involve the private sector.
Keyword: High-Rate Composting (HRC), Sanitary Landfill

Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis