(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 7, 2020 Modeling of a Tourism Group Decision Support System using Risk Analysis based Knowledge Base Putu Sugiartawan 1 , Sri Hartati 2 , Aina Musdholifah 3 Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada, Yogyakarta, Indonesia 1, 2, 3 Informatics Program, STMIK STIKOM Indonesia, Bali, Indonesia 1 Abstract—The increasing number of tourist destination becomes the main factor for export earning, job vacancy, business development, and infrastructure. The problem that occurs is the difference in regional income (GDP) that is quite significant in each region. Thus, it is necessary for the government to make a decision or policy in increasing tourist visits, mainly in Bali. In this case, choosing the most efficient decision from a number of decisions is for the government, tourists, community leaders, academics, and entrepreneurs in the tourism sector, especially in Bali. It is important to have a modeling decision support group (GDSS). GDSS modeling by integrating a knowledge-based (KB) risk analysis can determine decisions, extract information, and identify problems in the tourism sector especially, tourism objects in each region, more specifically. Problem identification in risk analysis modeling is determining decisions in handling risks and finding solutions from alternative tourism decisions that are potentially enlarged and knowledge gained from each decision-maker (DM). The process of identifying knowledge starts with comparing the assessment criteria on each tourism object and knowledge of tourism decision-makers. The results of GDSS modeling are subsequently integrated into knowledge-based risk analysis so that a decision is obtained in the form of an impact or risk and solution or recommendation in developing the specified tourism object. The purpose of combining the result is to understand the impacts or risks that may arise, and recommendations recommended so that the impacts or risks can be avoided. Keywords—GDSS modeling; risk analysis; tourism site; knowledge base; Bali tourism I. INTRODUCTION The development of tourism sector in Indonesia, especially in Bali, makes tourism sector as a significant factor in export earning, job creation, business development, and infrastructure. Tourism has gotten continuous expansion and diversification, becoming one of the most significant and fastest-growing economic sectors in Bali and increasing every year [1], [2], became the biggest income in the area. For foreign exchange earnings from each of the main sectors in Indonesia, tourism is in the fourth position after oil & gas, coal, and palm oil [2], and the sector's income continues to increase every year. Even though the global crisis has occurred several times, the number of international tourist trips shows growth. Data from UNWTO World Tourism Barometer shows that the number of tourist visits every year is increasing [3], and following the growth of world population. The problem is that the number of tourist visits in each region is different, causing a gap in income from the tourism sector [4], and public sector development depends on the area. Different Local Own Revenues (PAD) in each district has an impact on the development of public facilities such as roads, sidewalks, street lighting, integrated parks, and others. One of the policies applied is to increase the potential of each tourist attraction in each region to attract tourists visiting them. The increasing number of tourist visits can add an income of the region The policy made by tourism office has not only an impact on the tourist area, but also an extensive influence on stakeholders engaged in tourism such as travel agents, hotel & villa businesses, restaurants, minimarkets, and the economy of surrounding communities [5], [6], associated with an increase in the tourism sector. The government policy determines to make decision recommendations and impacts of risks by combining opinions and thoughts of decision-makers using Group Decision Support System (GDSS) modeling. GDSS modeling is a system that can be used to support the meeting of a group of people who interact with each other in accomplishing a job [7]–[10], from several people with different skills. GDSS modeling uses AHP method to determine alternative individual decisions [11]–[14], while for the incorporation of alternative individual decisions into group decisions use BORDA method [11], [15], [16] which is one of the GDSS models, ranks voting preferentially. GDSS method can make a decision that accommodates alternative decisions from decision-makers [17]–[19], according to the preferences given by the decision maker. AHP method is to combine logic for quantitative and qualitative data, experience, insight, and intuition, and can be implemented into an algorithm [20], [21], and the depth of the hierarchical structure which makes the model calculation more detailed. Thus, it allows decision- makers to find each criterion's weight and the level of comparison among the alternatives, especially in the tourism sector [12], [14], based on an assessment of the preferences of each decision maker. Because GDSS modeling only obtains alternative tourism selection decisions following the preferences of each DM, the risk of determining these alternatives is unknown. Before incorporating risk analysis into GDSS modeling, it can show the risks of alternative group decision choices and decision recommendations given through expert knowledge in tourism. This knowledge is implemented into knowledge-based (KB) method integrated with risk analysis. 354 | Page www.ijacsa.thesai.org