This paper describes the conceptual framework of ISDSS (Intelligence Spatial Decision Support System) for the fishery industry logistic. The decision problem involved is complex due to the fish’s seasonal, perishable, size variability characteristics as raw material. The ISDSS will be supported by three main models, i.e. raw material origin selection, distribution route selection and product tracking and tracing. Multiple criteria decision models will be used for raw material selection. Multi-objective optimization will be used for optimum route selection, considering time, distance, fish quality, and spatial information. Internet of Things (IoT) model will be used for product tracking and tracing system. The geographical information system (GIS) integration with the DSS will also be supported by Artificial Intelligence Technique, i.e. data mining in classifying and clustering the raw material characteristic and origin data. The data collection will be supported by the Internet of Things (IoT). The best technique for each model will be investigated further based on the field experiment and simulation.

Analisis korespondensi ordinal merupakan sebuah analisis multivariat untuk memetakan tingkat kepuasan produk atau tingkat pelayanan jasa dalam sebuah grafik. Analisis ini mengkombinasikan penguraian singular dari analisis korespondensi klasik  dan polynomial orthogonal melalui penguraian momen bivariate. Penggabungan kedua fitur tersebut berfungsi untuk mengakomodasi struktur urutan dalam skala ordinal, sehingga informasi dari variabel-variabel ordinal tersebut dapat diukur.

Decision Support Systems (DSS) are systems that assist decision-makers and aim to synthesize domain and technical knowledge and package it so non-scientists can use and comprehend it. This study aims to compile initial empirical studies that can objectively reject or confirm the central hypothesis. The materials were retrieved after applying the filtered query across all sources. All search engine providers use five query strings. In each example, five findings were collected, sorted, and compared to one another, and 152 papers were generated. Seventy-six documents were discovered after applying the inclusion and exclusion criteria. Each of the 70 papers was independently examined and analyzed. The method of study followed a specific procedure explicitly developed to minimize the risk of researcher bias. It is primarily concerned with whether fisheries have decision-making systems and what empirical outcomes these systems produce, particularly in real-time analysis. The result shows a dearth of research on intelligent DSS, which accounts for less than 3% of all DSS types discussed in the article. This study offers academics and professionals an overview of the implementation of DSS. These new contributions imply the form of several different new contributions to further research. Furthermore, the possibility of identifying research gaps increases once merged with geoinformation technology or spatial information. We introduced a new model that combines spatial logistics techniques with GIS-based tracing and tracking. The envisioned logistics ensure spatial and logistical traceability in the process of fish products.

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