Preprint / Version 1

A framework design web-based information system for sustainable fisheries supply chain in coastal communities of small islands Indonesia

Perancangan kerangka sistem informasi berbasis web untuk rantai pasok perikanan berkelanjutan di masyarakat pesisir pulau-pulau kecil Indonesia


  • Wellem Anselmus Teniwut Politeknik Perikanan Negeri Tual
  • Cawalinya Livsanthi Hasyim Politeknik Perikanan Negeri Tual
  • Dawamul Arifin


DSS, sustainable, SIRIPIKAN, supply chain, fisheries


Recent advances of development in information technology and the rapid use of decision support systems have become one of the advantages that can be utilized for use in the fisheries sector specifically in small islands. With geography conditions that caused limited access between and within the region, a need for efficient and effective tools for interconnecting supply and production includes managing the marine resources available then Web-DSS one way to choose. This study design DSS for sustainable supply chain of sectors in Southeast Maluku Regency, Indonesia (SIRIPIKAN). The DSS framework that was built consists of the first three parts, identification of fishing locations, then identification of supplier and seller locations, the second is the measurement of the level of sustainability of marine resources and the third is the managerial fisheries business carried out. SIRIPIKAN aims to increase the profitability of coastal communities in the region related to fisheries business activities and also reserving the marine resources in the region. In this system, we used data mining combining with spatial analysis and feasibility study as an approach to as the basis of the  development of the system. The model is able to provide an integrated sustainable production  by using input from the user to optimize the decision making related to optimize the profitability and sustainable existing marine resources.


V. Mohagheghi, S. M. Mousavi, M. Mojtahedi, and S. Newton, “Evaluating large, high-technology project portfolios using a novel interval-valued Pythagorean fuzzy set framework: An automated crane project case study,” Expert Syst. Appl., 2020, doi: 10.1016/j.eswa.2019.113007.

S. Ma, Y. Zhang, J. Lv, Y. Ge, H. Yang, and L. Li, “Big data driven predictive production planning for energy-intensive manufacturing industries,” Energy, 2020, doi: 10.1016/

M. K. C. S. Wijewickrama, N. Chileshe, R. Rameezdeen, and J. J. Ochoa, “Information sharing in reverse logistics supply chain of demolition waste: A systematic literature review,” Journal of Cleaner Production. 2021, doi: 10.1016/j.jclepro.2020.124359.

J. Wei, Y. Wang, and J. Lu, “Information sharing and sales patterns choice in a supply chain with product’s greening improvement,” J. Clean. Prod., 2021, doi: 10.1016/j.jclepro.2020.123704.

P. Howson, “Building trust and equity in marine conservation and fisheries supply chain management with blockchain,” Mar. Policy, 2020, doi: 10.1016/j.marpol.2020.103873.

W. A. Teniwut, K. D. Betaubun, Marimin, and T. Djatna, “A conceptual mitigation model for asymmetric information of supply chain in seaweed cultivation,” in IOP Conference Series: Earth and Environmental Science, 2017, vol. 89, no. 1, doi: 10.1088/1755-1315/89/1/012022.

W. A. Teniwut, M. Marimin, and T. Djatna, “GIS-Based multi-criteria decision making model for site selection of seaweed farming information centre: A lesson from small islands, Indonesia,” Decis. Sci. Lett., vol. 8, pp. 137–150, 2019, doi: 10.5267/j.dsl.2018.8.001.

W. A. Teniwut and R. M. K. Teniwut, “Minimizing the instability of seaweed cultivation productivity on rural coastal area?: a case study from Indonesia,” vol. 11, no. 1, pp. 259–271, 2018.

W. A. Teniwut, Y. K. Teniwut, R. M. K. Teniwut, and C. L. Hasyim, “Family vs Village-Based: Intangible View on the Sustainable of Seaweed Farming,” IOP Conf. Ser. Earth Environ. Sci., vol. 89, no. 1, p. 012021, Oct. 2017, doi: 10.1088/1755-1315/89/1/012021.

W. A. Teniwut, “For sustainable revenue of fisheries sector in small islands: Evidence of Maluku, Indonesia,” AACL Bioflux, 2016, doi: 10.5281/zenodo.245507.

Y. Yun, D. Ma, and M. Yang, “Human–computer interaction-based Decision Support System with Applications in Data Mining,” Futur. Gener. Comput. Syst., 2021, doi: 10.1016/j.future.2020.07.048.

W. Deng, L. Feng, X. Zhao, and Y. Lou, “Effects of supply chain competition on firms’ product sustainability strategy,” J. Clean. Prod., 2020, doi: 10.1016/j.jclepro.2020.124061.

J. Wang and W. Zhuo, “Strategic information sharing in a supply chain under potential supplier encroachment,” Comput. Ind. Eng., 2020, doi: 10.1016/j.cie.2020.106880.

Y. Wang and S. H. Zhang, “Optimal production and inventory rationing policies with selective-information sharing and two demand classes,” Eur. J. Oper. Res., 2021, doi: 10.1016/j.ejor.2020.05.051.

J. Mar-Ortiz, M. D. Gracia, and N. Castillo-García, “Challenges in the design of decision support systems for port and maritime supply chains,” in Studies in Computational Intelligence, 2018.

G. Dellino, T. Laudadio, R. Mari, N. Mastronardi, and C. Meloni, “A reliable decision support system for fresh food supply chain management,” Int. J. Prod. Res., 2018, doi: 10.1080/00207543.2017.1367106.

F. Geri, S. Sacchelli, I. Bernetti, and M. Ciolli, Urban-rural bioenergy planning as a strategy for the sustainable development of inner areas: A GIS-based method to chance the forest chain, no. 9783319757. 2018.

M. Dev, P. Kaur, and K. K. Sarma, “Fuzzy Approach to Decision Support System Design for Inventory Control and Management,” J. Intell. Syst., 2017, doi: 10.1515/jisys-2017-0143.

M. Chica and W. Rand, “Building agent-based decision support systems for word-of-mouth programs: A freemium application,” J. Mark. Res., 2017, doi: 10.1509/jmr.15.0443.

Marimin, W. Adhi, and M. A. Darmawan, “Decision support system for natural rubber supply chain management performance measurement: A sustainable balanced scorecard approach,” Int. J. Supply Chain Manag., 2017.

G. Dutta, N. Gupta, J. Mandal, and M. K. Tiwari, “New decision support system for strategic planning in process industries: Computational results,” Comput. Ind. Eng., 2018, doi: 10.1016/j.cie.2018.07.016.

P. K. Tarei, J. J. Thakkar, and B. Nag, “Development of a decision support system for assessing the supply chain risk mitigation strategies: an application in Indian petroleum supply chain,” J. Manuf. Technol. Manag., 2020, doi: 10.1108/JMTM-02-2020-0035.

M. Alkahtani, A. Choudhary, A. De, and J. A. Harding, “A decision support system based on ontology and data mining to improve design using warranty data,” Comput. Ind. Eng., 2019, doi: 10.1016/j.cie.2018.04.033.

H. Talebian, O. E. Herrera, and W. Mérida, “Spatial and temporal optimization of hydrogen fuel supply chain for light duty passenger vehicles in British Columbia,” Int. J. Hydrogen Energy, 2019, doi: 10.1016/j.ijhydene.2019.07.218.

E. P. Sarabi and S. A. Darestani, “Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing,” Appl. Soft Comput., 2020, doi: 10.1016/j.asoc.2020.106849.

B. Imène and T. Noria, “Towards a new supporting platform for collaboration in industrial diagnosis within an agent-based WEB DSS,” Int. J. Comput. Aided Eng. Technol., 2018, doi: 10.1504/IJCAET.2018.092865.

H. Allaoui, Y. Guo, and J. Sarkis, “Decision support for collaboration planning in sustainable supply chains,” J. Clean. Prod., 2019, doi: 10.1016/j.jclepro.2019.04.367.

M. Yazdani, P. Zarate, A. Coulibaly, and E. K. Zavadskas, “A group decision making support system in logistics and supply chain management,” Expert Syst. Appl., vol. 88, pp. 376–392, 2017, doi: 10.1016/j.eswa.2017.07.014.

G. Im and A. Rai, “IT-enabled coordination for ambidextrous interorganizational relationships,” Inf. Syst. Res., 2014, doi: 10.1287/isre.2013.0496.

J. Xin, “A supply chain optimization DSS Web-services-based for e-retail industry,” 2011, doi: 10.1109/PEAM.2011.6135052.

A. Bonfante et al., “LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study,” Agric. Syst., 2019, doi: 10.1016/j.agsy.2019.102646.

K. L. Carder, F. R. Chen, Z. Lee, S. K. Hawes, and J. P. Cannizzaro, MODIS Ocean Science Team Algorithm Theoretical Basis Document ATBD 19: Case 2 Chlorophyll a. 2003.

O. B. Brown and P. J. Minnett, “MODIS infrared sea surface temperature algorithm - Algorithm Theoretical Basis Document Version 2.0,” 1999.

N. Zendrato, H. W. Dhany, N. A. Siagian, and F. Izhari, “Bigdata Clustering using X-means method with Euclidean Distance,” 2020, doi: 10.1088/1742-6596/1566/1/012103.

O. Stavrakidis-Zachou, N. Papandroulakis, A. Sturm, P. Anastasiadis, F. Wätzold, and K. Lika, “Towards a computer-based decision support system for aquaculture stakeholders in Greece in the context of climate change,” 2018, doi: 10.1504/IJSAMI.2018.099235.

Á. Cobo, I. Llorente, L. Luna, and M. Luna, “A decision support system for fish farming using particle swarm optimization,” Comput. Electron. Agric., 2019, doi: 10.1016/j.compag.2018.03.036.