Analisis Klasterisasi Patok Jalan Berbasis Geospasial Menggunakan K-Means dan Evaluasi Davies-Bouldin - Dien, Habibie Ed; Ratsanjani, M. Hasyim; Saputra, Agung Adi; Noprianto; Ririd, Ariadi Retno Tri Hayati; Nugraha, Bagas Satya Dian (Jurnal Pekommas, 2024)
Geographic Information System (GIS) has become a very important tool for spatial analysis and decision making in various fields. In this paper, the analysis process of grouping Kilometer Hectometer (KM/HM) road milestones use the K-Means method in the context of GIS. The KM/HM road milestones are road equipment made of concrete or signboards equipped with texts containing information about the distance and the name of the city to be traveled by road users. The length of the road that has such a long distance will make it difficult to maintain and to manage the road milestones that are scattered throughout the road. Currently, the process of mapping the location of the road milestones is still being carried out using the conventional method, in which the survey officer records the data on paper and measures it with the vehicle's odometer. However, this method often causes data loss, errors in determining the location, and lacks photographic evidence as a reference for assessing the condition of the road milestones. The role of GIS for survey officers is to map the road milestones and to visualize the road milestone data. The main objective is to gain meaningful insights from the spatial distribution of these road milestones, which will assist in better navigation and infrastructure planning. The K-Means method separates clusters from KM/HM road milestones which are identified based on geographical proximity. To assess the quality of these clusters, the evaluation is conducted using the Davies-Bouldin index (DBI) which provides a quantitative measure of inter-group similarity and within-group dissimilarity. For officers, it can be useful to find location points for the road milestones that have high damage conditions to prioritize to be repaired first. Based on the test results using DBI, it produces a value close to zero, which is equal to 0.1656, indicating that the clusters formed have very good quality.