As archipelagic region, an effort to effectively enhance and accelerate the development of each sub-districts to boost the rapid development of Southeast Maluku district in Indonesia cannot happen as long as the local government fails to identify the real potentials and power in fisheries sector of each sub-districts. Identification of each sub-district fisheries top sub-sector has to be based on the human resources, natural resources, infrastructure, current and potential market, current policy of local and central government. A multi-criteria decision making (MCDM) is one of the powerful tools to provide a better result based on complicated factors involved. This paper proposed an integrated MCDM, to tackle the complicated factors in order to provide the best commodities on each sub-districts. Bottom-up concept was used to have a comprehensive result, by combining Fuzzy logic with Analytical Hierarchy Process (AHP) to measure the factors using fuzzy logic with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for determining the top sub-sector in fisheries. For comprehensive assessment of macro factors the study used experts ranging from government, scientists, practitioners to NGOs. On the other hand, for micro factors the survey used field instructor, field officer, fishers and farmers. The results provide a guideline for local and central government to form a better policy regarding the development of each sub-district including farmers, fishers and coastal communities in each sub-district to focus on commodities that benefited their regions’ resources and coastal community’s capabilities. By doing so, we hope to contribute on crafting an integrated and collective path on reaching the goal which is the welfare of coastal region.

The geographical constrain of small islands associated with fisheries commodities, is due to the lack of insufficient infrastructure and socio-economy characteristics, in addition to the nature of fisheries commodities where the durability and inconsistency of quality and quantity of the particular region become very vulnerable to uncertainty of supply chain. This study aims to formulate a strategy to mitigate the existing effect of uncertainty supply chain in Southeast Maluku Regency. The data mining approach with naïve bayes was used to determine the clusters of areas with high to low interdependency in villages and sub-districts. In addition, the House of Risk (HOR) was used to identify the cause of risks and provided the most effective and efficient alternative strategy to mitigate the uncertainty of the fisheries' supply chain sector. Data were collected from 28 villages to obtain 322 datasets by using the purposive sampling technique and expert assessment method for the data mining process, and HOR, respectively. The result show that the village location, resources, and fishermen's ability to sell and buy their gear were the main determinant factors of interdependency. Furthermore, there were, at least, three clusters of interdependencies with the integration of a web-based system to help spread information to fishermen and aquaculture farmers. In conclusion, policymakers need to be more adaptable to the use of information technology to efficiently improve the welfare and performance of coastal communities, aside from constantly enhancing the region's infrastructure.

As in agriculture, fisheries sector faces long list of uncertainty and unsustainability in supply of the products. This is due to the great possibility of natural disaster and moral hazard by the actors in fisheries which lead to the instability of value chain on fisheries commodity from downstream to upstream. Thus, a study must be done in order to explore and minimize the disturbance of the stream of value chain in fisheries. This study aims to investigate the attributes playing big role on affecting value chain cooperation among actors in fisheries commodity in Kei Islands. Data mining is used on the study to determine factors playing major roles in smoothing cooperation in value chain of fisheries commodity, with 220 datasets obtained from actors in fisheries micro-enterprises in Kei Islands taken from August to November, 2019. The result shows that, on average, weights of four data mining techniques; namely support vector machine, fast large margin, logistic regression, naïve bayes and generalized linear model all appear to put management as the leading factor that contributes the most for the cooperation of value chain of fisheries commodity in Kei Islands. This finding implicates that management on all micro-enterprises of fisheries business in Kei Islands has to be the main focus, which is mainly related to basic knowledge on conducting cooperation and maintaining relationship among all actors in fisheries sector, therefore, NGO and local government need to step in and play role on educating all actors for better good governance of the business to obtain the great sustainability in the value chain of fisheries commodity in the region.