Sistem Pendukung Keputusan Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit di Usaha Penampungan Menggunakan Metode Simple Additive Weighting (SAW) Berbasis Web

Abstract

Determining the purchase price of fresh fruit bunches (FFB) at palm-oil collection businesses is often based on manual assessment and operator experience. This condition may cause slow decisions, inconsistent valuation, and limited transparency when several criteria must be considered simultaneously. This study develops a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to support objective FFB price recommendations. The system manages criteria, alternatives, assessment values, market-price references, SAW calculations, and reports. The Rapid Application Development (RAD) model is used for system development, while Black Box Testing is applied to verify functional requirements. The implemented model uses five criteria: fruit quality, bunch weight, travel distance, maturity, and impurity deduction, with weights of 30%, 25%, 20%, 15%, and 10%, respectively. Manual verification produced a highest preference value of 0.9500 for A3, and the system generated a recommendation of Rp2,600/kg. Functional testing of 13 scenarios showed valid results.

Intisari—Penentuan harga Tandan Buah Segar (TBS) pada usaha penampungan masih dapat dilakukan secara manual sehingga proses keputusan berpotensi lambat, tidak konsisten, dan kurang transparan ketika banyak kriteria harus dipertimbangkan. Penelitian ini membangun Sistem Pendukung Keputusan (SPK) berbasis web menggunakan metode Simple Additive Weighting (SAW) untuk menghasilkan rekomendasi harga TBS secara lebih objektif. Sistem mengelola kriteria, alternatif, nilai penilaian, harga pasar, proses perhitungan SAW, dan laporan. Pengembangan menggunakan Rapid Application Development (RAD), sedangkan pengujian fungsional menggunakan Black Box Testing. Lima kriteria yang digunakan adalah kualitas buah, berat tandan, jarak tempuh, kematangan, dan potongan kotoran dengan bobot 30%, 25%, 20%, 15%, dan 10%. Verifikasi manual menghasilkan nilai preferensi tertinggi 0,9500 pada A3 dan rekomendasi harga Rp2.600/kg. Pengujian terhadap 13 skenario menunjukkan seluruh fungsi utama berstatus valid.