Waste pickers search for and collect recyclable waste to be handed to other parties can reprocessed them into goods of new values. However, the information of the locations of those materials is not always present. The availability of this information can reduce the time of searching, increase the number of collected materials and therefore raise waste pickers income. The purpose of this work is to develop an information system model of recyclable waste availability. This model based on SWOT Analysis where data environment internal and external faced situation and condition. Build design of use case high-level to be as a grand design management, database, and user interface where a structured approach using prototype method. Input of this model is data where comes from the local society and is validated & verified by the local government and output of the model is information provided in texts and geographics map. This model is an effort to help waste pickers to obtain information about potential areas of waste sourced from the society together with the local government.

Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perkreditan Rakyat (BPR) Bandung City, it has experienced credit complications such as bad credits. Therefore, this study aims to help decision-makers in determining creditworthiness and preventing bad credits from occurring. To realize this solution, the study uses the Fuzzy Logic method to calculate the creditworthiness of each prospective loaner based on the inquiries done in the field survey. Fuzzy Logic is known to be a “counting” methodology with varying words. In addition, it can implement human expertise into machine language with ease and adequately. Based on numerous testing performed, the results demonstrate a level of 90% in accuracy when inputting within the valid ranges of each fuzzy set and membership function. However, the level of accuracy is only based on the clarification result which is determined by a researcher and BPR director, not a general level of accuracy for other microfinance institutions. Nevertheless, the findings of this study prove the method has a high enough accuracy to support decision-makers in determining the loan eligibility of prospective loaners and through this application in the surveying process, survey workers can work more efficiently. Hence, in future has a higher chance of predicting bad credits from potential loaners.