(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 10, 2022 115 | Page www.ijacsa.thesai.org Classification of Agriculture Area based on Superior Commodities in Geographic Information System Lilik Sumaryanti 1 , Rosmala Widjastuti 2 , Firman Tempola 3 , Heru Ismanto 4 Department of Informatic Engineering, Universitas Musamus, Merauke, Indonesia 1, 4 Department of Agrotechnology, Universitas Musamus, Merauke, Indonesia 2 Department of Informatic Engineering, Universitas Khairun, Ternate, Indonesia 3 Abstract—Research carries a classification model that combines LQ analysis and hierarchical classification using a single linkage. The classification results are a basis for mapping the potential of agriculture areas based on superior food commodities in Merauke Regency, Indonesia. LQ analysis is used to select food commodities. In contrast, the application of single linkage uses the production of three features, rice, corn, and peanuts, which have an LQ value>1, to group sub-districts based on agricultural potential. Intelligent mapping is represented by mapping the sub- districts agricultural areas according to the cluster. The classification results show that the first cluster has sixteen sub- district members, the second consists of three sub-districts, and the third cluster consists of one sub-district. Each cluster member has similarities based on the distance measurement with the smallest value using the Euclidean distance. The proposed classification model is a creative idea to map agricultural areas, which can present information on regional potential based on superior food crop commodities. Keywords—Classification; agriculture; location qoutient; single linkage; geographic information system I. INTRODUCTION The agricultural sector is an essential source of income in the national economy in Indonesia, as evidenced by its contribution to gross domestic product. The agricultural sector's potential is the basis for developing rural economic activities through business development, namely agribusiness and agro- industry. Merauke is one of the regencies in eastern Indonesia, where most of the population depends on the agricultural sector, especially for rice commodities, and has become a food barn in Papua Province, with rice production in 2022 reaching 354192.32 tons [1]. Agriculture is a sector being the center of attention in efforts to develop and grow the economy that concerns many people's lives, not only the current generation but also the generations to come. Agricultural potential in Merauke Regency for food crops consists of various commodities, namely, rice, corn, green beans, soybeans, peanuts, cassava, and sweet potatoes [2]. The information on agriculture areas in Merauke Regency is presented by survey reports from the Central Statistics Agency or by seeking information directly to visit the place. Making it difficult for those who want to seek information on the agriculture sector's potential, do not support user mobility, and are not efficient in doing so. The agriculture sector's potential has to increase by determining the best commodity of food crops in an area to be used as information for local governments to make programs and policies [3]. The aim of developing the best commodity is to meet the needs of local consumption in the region and to develop prospects so the production can be exported outside the region. The results of mapping agriculture areas based on superior food crop commodities can be used as a source of information for policymakers, both the government and farmers, to support the sustainability of the livelihoods of people who depend on the agrarian sector to improve their welfare. They can also be used as information for the private sector as potential investors for assisting in finding the potential of agricultural areas based on superior food crop commodities. Building agricultural businesses following strategies maximizes the available and optimally managed agriculture potential. The use of technology with the concept of intelligent agriculture [4] is a significant change in the development of the agriculture sector [5][6]. The applications for agricultural information classification analysis and agriculture production management have been developed [7], such as the classification model used to determine superior food crop clusters [8], The use of machine learning models for crop cultivation prediction [9], allows farmers to assess the cultivated types, monitor plant growth, and choose the correct harvest time [10]. System development using a classification model that combines qualitative and quantitative methods improves the relevance [11], completeness, and accuracy in finding information and increases the utilization of agricultural data information [12]. The proposed classification model for mapping agriculture areas combines two methods, Location Quotient (LQ) and Single Linkage, which aims to determine hierarchically based on regional potential. Using the LQ method to determine the regionally superior food crop commodities in Merauke Regency[13], the classification model produces the best commodity types based on the LQ value > 1. The results are then used as a feature for cluster analysis of agricultural areas using the single linkage method, so the classification accuracy is high. The proposed classification model is generated well with relevant features [14]; the classification model is trained by reducing the number of features that are the results of the LQ method analysis so that the proposed hybrid algorithm can work optimally [15]. Classification of superior food crop commodities provides essential information for mapping agricultural areas. Implementation of Hierarchical classification is widely used for data mining, and the performance of the single linkage method is suitable for handling various types of data [16]. The application of the proposed classification model produces a mapping of agriculture areas in geographic information systems [17]. The