Penerapan Metode Oreste pada Sistem Pendukung Keputusan dalam Pemilihan Alasan Terbaik Diyah SMP Galih Agung Memilih Persantren Darularafah Raya

Nursakila Ena Anjani
Rika Hanifah Tanjung
Sofiah Aini
Khairunnisa Ani Putri

Bilangan: Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Vol. 3 No. 5 (2025): Oktober : Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa

N the rapidly evolving digital era, decision-making has become a critical aspect across various fields, including education, where choices such as selecting an Islamic boarding school (pondok pesantren) are often influenced by complex and subjective factors. This study addresses the dilemma faced by Diyah, a junior high school student, in determining the best reasons for choosing Pondok Pesantren Darularafah Raya, highlighting the limitations of manual, personal-based processes that fail to systematically consider measurable criteria like educational quality, learning environment, facilities, discipline, and instilled religious values. The advancement of information technology provides a solution through Decision Support Systems (DSS), utilizing the ORESTE (Organization, Rangement Et Synthèse De Données Relationnelles) method, which effectively processes ordinal data to produce objective rankings based on subjective yet structured preferences. Unlike other methods such as SAW or AHP that rely on numerical data, ORESTE emphasizes relative preference weights, making it suitable for individual decision-making contexts like educational choices. The novelty of this research lies in applying ORESTE in a DSS focused on analyzing an individual's best reasons for selecting a pesantren, aiming to reduce subjective bias and enhance rationality. The primary objective is to develop a DSS using the ORESTE method to analyze and determine Diyah's optimal reasons for choosing the pesantren. Through this, the system is expected to accelerate evaluation processes, improve objectivity, and identify dominant factors influencing educational decisions. Findings from the implementation demonstrate accurate rankings that prioritize key criteria, leading to more efficient and data-driven outcomes. Implications include aiding students, schools, and educators in understanding influential factors, fostering objective assessment systems, and serving as a reference for future studies integrating MCDM methods with computer-based systems in personalized educational decision-making.