Ratih Indri Hapsari, Gunawan Budiprasetyo, Banni Satria Andoko, Muhammad Irsyadi Firdaus, Nain Dhaniarti Raharjo, Agustin Dita Lestari, Fidia Sabilla Putri
This study develops a thorough method for gathering residential data with geospatial elements, validation tools, and a decision support system for reducing poverty using the habitability index. The benefits of using computational technology to generate a system for uninhabitable houses and to promote quicker decision-making for aid providence are shown. A decision-support system can improve the decision-making process, resulting in more effective and focused recommendation for the authority. Seven criteria are incorporated in the system, i.e. social, ownership, aid history, spatial location, finishing, structural, and sanitation. Using the proposed system, 1341 data points from houses in the Lumajang Regency slum area have been collected. As many as 119 households are classified as being less habitable. It is considered acceptable for the decision model to predict with a success percentage of 67.23%. This study would contribute to the authority in several advantages, including the reliable open data related to uninhabitable houses. Furthermore, it enables the generation of recommendations, facilitated by decision-making supported by computational application. This approach also helps in preventing biases when prioritizing uninhabitable houses. With this acceptance rate, the results can be dependably used as the foundation for poverty aid for house rehabilitation. The current study significantly advances Lumajang Regency's transformation into a smart city. © Published under an exclusive license by AIP Publishing.
Department of Civil Engineering, State Polytechnic of Malang, Jl. Soekarno Hatta 9, 65141, Indonesia; Department of Informatics Technology, State Polytechnic of Malang, Jl. Soekarno Hatta 9, 65141, Indonesia