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Nabire Regency in Central Papua Province confronts significant geographical complexities in transportation system development, characterized by hilly topography and limited road infrastructure that impacts community mobility efficiency. This research analyzes road route optimization through Geographic Information System (GIS) approach using QGIS 3.28.3 software with QNEAT3 plugin for network analysis. The research methodology applies network analysis considering multiple parameters including travel distance, travel time, road surface conditions, slope gradients based on Digital Elevation Model (DEM), and impedance factors according to infrastructure quality. Spatial data were obtained from Geospatial Information Agency (BIG) and OpenStreetMap (OSM) with analysis focusing on 8 origin-destination pairs representing critical movements to health facilities, education centers, and economic hubs. Analysis results identified alternative routes demonstrating 12.5-20% travel time efficiency compared to existing routes, with an average optimization of 15.7%. Field validation confirmed model prediction accuracy with error rates below 8%. These research findings provide strategic recommendations for local government in sustainable transportation infrastructure planning that can enhance regional accessibility and support local economic development in the Central Papua region.

Adapting to students’ learning styles is a key factor in enhancing the effectiveness of higher education, particularly in Informatics programs where learning preferences vary widely. This study aims to segment students based on their learning styles using the K-Means clustering algorithm, guided by the VARK model (Visual, Auditory, Read/Write, Kinesthetic). Data were collected from 130 Informatics students, including information on their learning preferences, and processed through normalization techniques. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, and subsequent cluster interpretation was conducted. The results identified three dominant clusters, each representing distinct learning behavior patterns. These clusters were analyzed to recommend tailored instructional strategies for each group. Specifically, Visual learners were found to benefit from graphic-heavy materials, Auditory learners preferred lectures and discussions, Read/Write learners thrived on written content and detailed notes, while Kinesthetic learners responded best to hands-on activities. The findings support the development of adaptive, data-driven teaching approaches that align with the actual learning tendencies of students in Informatics. Moreover, the study demonstrates that the K-Means method is effective in systematically identifying student learning profiles, which can be used to inform instructional improvements. This personalized approach to teaching could significantly enhance learning outcomes by providing students with the most effective educational experiences tailored to their individual learning styles