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Salah satu upaya pemerintah untuk mengatasi masalah kemiskinan di Indonesia yaitu membuat program beras sejahtera (RASTRA). RASTRA merupakan program dari pemerintah berupa bantuan beras bersubsidi untuk membantu masyarakat yang berpenghasilan rendah. Permasalahan yang terjadi yakni banyaknya kriteria penilaian yang digunakan dalam pedoman RASTRA dan penduduk miskin di suatu area/wilayah seringkali menyulitkan proses penentuan Keluarga Penerima Manfaat yang berhak menerima RASTRA pada Musyawarah desa/kecamatan. Tujuan penelitian ini adalah merancang dan mengembangkan sistem penunjang keputusan menggunakan model matematika  Simple Additive Weighting  (SAW) dan  Weighted Product  (WP) untuk memberikan rekomendasi penerima RASTRA. Terdapat empat tahapan penelitian yang digunakan untuk mencapai tujuan penelitian, yaitu analisis kebutuhan perangkat lunak, desain perangkat lunak, pengembangan, dan pengujian perangkat lunak. Berdasarkan hasil pengujian, hasil perhitungan nilai preferensi SAW   memiliki performa yang lebih baik daripada WP karena SAW mampu meminimalisir nilai preferensi alternatif yang sama. Hal ini tampak dari perankingan alternatif berdasarkan hasil perhitungan SAW sejumlah 13 peringkat, dan WP sejumlah 10 peringkat.       Abstract    One of the government's efforts to overcome the poverty problem in Indonesia is to make the program "Beras Sejahtera" (RASTRA). RASTRA is a government program of subsidised rice to help low-income communities. The problems which occur are the number of assessment criteria used in the RASTRA guidelines and the poor in an area/region often complicate the process of determining the Beneficiary Family who are eligible to receive RASTRA at the village/sub-district deliberation. The purpose of this research is to design and develop decision support system using Simple Additive Weighting (SAW) and Weighted Product (WP) mathematical model to give the recommendation of RASTRA recipient. There are four research stages to achieve the research objectives, namely software requirements analysis, software design, development, and software testing. Based on the test results, the calculation of SAW preference values has better performance than WP because SAW can minimise the value of the same alternative preferences. This can be seen from the alternative ranking based on the calculation of SAW of 13 ranks, and WP 10 rank number.

Implementasi Simple Additive Weighting dan Weighted Product pada Sistem Pendukung Keputusan untuk Rekomendasi Penerima Beras Sejahtera