Analysis of Implementation AHP Algorithm as a Decision Support System for the Feasibility of Rehabilitating Low-Income Residents' Houses
Implementasi Algoritma AHP Sebagai Sistem Pendukung Keputusan Kelayakan Rehabilitasi Rumah Warga Berpenghasilan Rendah
DOI:
https://doi.org/10.52187/rdt.v7i1.381Keywords:
dss, housing rehabilitation, low income communities, analytical hierarchy process.Abstract
The availability of decent housing is one of the important indicators in improving the quality of life of the community, especially for residents with low incomes. In practice, the process of determining the eligibility of recipients of housing rehabilitation assistance is often still done manually and subjectively, thus potentially causing inaccuracy and injustice in decision making. The initiative stems from the growing need for systematic and objective tools to support government and social institutions in identifying eligible households for housing rehabilitation programs. In addressing this need, the Analytical Hierarchy Process (AHP) method is employed as a core decision-making framework within the DSS due to its strength in handling complex decision problems involving multiple criteria. The system incorporates various assessment criteria, including household income, structural condition of the house, number of dependents, ownership status, and age of the building. These criteria are structured hierarchically, and their relative importance is determined through pairwise comparisons facilitated by AHP. By applying this method, the system is capable of producing a weighted score for each applicant, thus enabling decision-makers to prioritize assistance based on quantifiable and transparent factors. The research includes both qualitative and quantitative analysis, starting from needs assessment, system requirement analysis, and user interface design, to the implementation of the AHP algorithm within the DSS. Results from case studies and prototype testing demonstrate that the system improves the accuracy and fairness of housing rehabilitation assessments compared to conventional manual evaluations. Moreover, it offers a user-friendly interface for stakeholders with varying technical backgrounds. In conclusion, the integration of AHP in the proposed DSS provides a reliable and scalable solution to support equitable housing policy decisions. Future developments may include integration with GIS for location-based analysis and mobile accessibility for field surveys
References
Annas, F., Ediana, D., Kurniawan, A., Wandira, R., & Zakir, S. (2021). Decision Support System in Detrmination of Project Tender Winner Using the Analytical Hierarchy Process (AHP) Method. Journal of Physics: Conference Series, 1779(1), 1–12. https://doi.org/10.1088/1742-6596/1779/1/012006
Araujo, S. M., Sousa, P., & Dutra, I. (2020). Clinical decision support systems for pressure ulcer management: Systematic review. JMIR Medical Informatics, 8(10), 1–12. https://doi.org/10.2196/21621
Badan Pusat Statistik Kabupaten Klaten. (29 November 2024). Indikator Kesejahteraan Rakyat Kabupaten Klaten 2024. Diakses pada 16 Maret 2026, darihttps://klatenkab.bps.go.id/id/publication/2024/11/29/638b596788d4939314ec138f/indicators-of-peoples-welfare-of-klaten-regency-2024.html
Basiroh, B., Asmarajati, D., & Fatmafury, W. (2020). Pengaruh User Interface Toko Online Terhadap Kenyamanan Pengguna Studi Kasus Pada E – Commerce Wonosobo Mall. Device, 10(1), 33–37. https://doi.org/10.32699/device.v10i1.1484
Basiroh, B., & Irjananto, H. (2024). C4.5 Algorithm As a Decision Support System for Social Welfare Aid Recipients. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 12(1), 9. https://doi.org/10.30646/tikomsin.v12i1.816
Basiroh, B., & Lestari, W. (2020). Analysis of Plant Fragaria Xananassa Disease Diagnoses Using Production Rules Base on Expert System. Jurnal Pilar Nusa Mandiri, 16(1), 25–32. https://doi.org/10.33480/pilar.v16i1.1174
Chaipetch, P., Amprayn, C., Pawan, P., & Ratanavaraha, V. (2025). A Multi-Criteria Decision Support System for Prioritizing Road Maintenance: Integrating AHP and TOPSIS with a Focus on Low-Volume Roads. IOP Conference Series: Earth and Environmental Science, 1450(1), 1–11. https://doi.org/10.1088/1755-1315/1450/1/012004
Chuanlei, Z., Shanwen, Z., Jucheng, Y., Yancui, S., & Jia, C. (2017). Apple leaf disease identification using genetic algorithm and correlation based feature selection method. International Journal of Agricultural and Biological Engineering, 10(2), 74–83. https://doi.org/10.3965/j.ijabe.20171002.2166
Fauzi, A., Harli, E., & Teknik, F. (2019). Rancang Bangun Sistem Informasi Akademik Smk Negeri 1 Depok Berbasis Android Dengan Pendekatan Rapid Application Development. Jurnal Teknik Informatika, 12(2), 129–136. https://doi.org/10.15408/jti.v12i2.10939
Irfan, M., Uriawan, W., Kurahman, O. T., Ramdhani, M. A., & Dahlia, I. A. (2018). Comparison of Naive Bayes and K-Nearest Neighbor methods to predict divorce issues. IOP Conference Series: Materials Science and Engineering, 434(1), 1–12. https://doi.org/10.1088/1757-899X/434/1/012047
Lestari, S., & Karim, A. S. (2015). Model Klasifikasi Kinerja Dan Seleksidosen Berprestasi Dengan Algoritma C.45. Prosiding Sembistek 2014, 1(02), 340–350. https://jurnal.darmajaya.ac.id/index.php/sembistek/article/view/221/104
Listyaningsih, V., & Utami, E. (2018). Decision support system performance-based evaluation of village government using AHP and TOPSIS methods: Secang sub-district of Magelang regency as a case study. International Journal of Intelligent Systems and Applications, 10(4), 18–28. https://doi.org/10.5815/ijisa.2018.04.03
Luo, J., Ahmad, S. F., Alyaemeni, A., Ou, Y., Irshad, M., Alyafi-Alzahri, R., Alsanie, G., & Unnisa, S. T. (2024). Role of perceived ease of use, usefulness, and financial strength on the adoption of health information systems: the moderating role of hospital size. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-02976-9
Masruroh, S. U., Swaraja, A. A., Shofi, I. M., Informatika, T., & Sains, F. (2019). Sistem Pengambilan Keputusan Pemetaan Pelatihan Teknologi Informasi untuk Penyandang Disabilitas Tuna Rungu Hard of Hearing Menggunakan Metode Analytical Hierarchy Process (ahp). (2019). Jurnal teknik informatika, 12(2), 201-212.
Muawwal, A., Ulum Ilham, B., Renny, & Hasniati. (2024). Penggunaan Teknologi AI dan Strategi Pemasaran Digital untuk Mendorong Pertumbuhan Penjualan Usaha Kecil Menengah di Wilayah Sulawesi Selatan. Jurnal Sipissangngi, 4(1), 1–9. https://doi.org/10.35329/jurnal.v4i1
Mulyana, A., Mahmudah, S. M., Saifudin, M., & Saleh, M. (2024). Smart as User-Generated Content in taking advantage of new economic opportunities for Students of SMK 60 Kedoya, West Jakarta. 9, 181–192.
Nugraha, N. B., Puspaningrum, A., & Santosa, Y. M. (2025). Using AHP to prioritize flood mitigation measures in urban areas. E3S Web of Conferences, 605. https://doi.org/10.1051/e3sconf/202560503001
Siwa, N. N. A. P., Putrama, I. M., & Santyadiputra, G. S. (2020). Development of car rental system based on geographic information system and decision support system with AHP (Analytical Heirarchy Process) and SAW (Simple Additive Weighting) method. Journal of Physics: Conference Series, 1516(1), 1–9. https://doi.org/10.1088/1742-6596/1516/1/012013
Utomo, D. T., Pratikto, Santoso, P. B., & Sugiono. (2022). Implementation of a Fuzzy Decision Support System for Selection of Hand Tractor Assembly Suppliers. EUREKA, Physics and Engineering, 1, 44–52. https://doi.org/10.21303/2461-4262.2022.001864
