Pengembangan Sistem Pendukung Keputusan Audit Mutu Internal Berbasis IAPS 4.0 Menggunakan Algoritma Profile Matching

Abstract

Abstrak : 

Audit Mutu Internal (AMI) merupakan salah satu elemen utama dalam Sistem Penjaminan Mutu Internal (SPMI) perguruan tinggi yang bertujuan menjamin penyelenggaraan tridharma sesuai standar mutu. Namun, proses AMI sering menghadapi kendala seperti subjektivitas auditor, keterlambatan pelaporan, dan kurangnya integrasi data digital. Penelitian ini bertujuan mengembangkan Sistem Pendukung Keputusan Audit Mutu Internal berbasis IAPS 4.0 dengan menerapkan algoritma Profile Matching untuk meningkatkan objektivitas dan efisiensi penilaian. Sistem dibangun menggunakan framework CodeIgniter 4 dengan arsitektur Model–View–Controller (MVC) serta basis data MySQL. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model Waterfall, meliputi analisis kebutuhan, perancangan, implementasi, dan pengujian sistem. Pengujian dilakukan melalui black box testing dan validasi hasil perhitungan algoritma dibandingkan dengan hasil audit manual. Hasil menunjukkan bahwa sistem berfungsi dengan baik secara fungsional dan memiliki tingkat akurasi sebesar 94,2% (selisih rata-rata 5,8%) terhadap hasil audit manual. Selain itu, sistem mampu mengurangi waktu pelaksanaan audit hingga 40% dan meningkatkan objektivitas penilaian. Integrasi metode Profile Matching dengan indikator IAPS 4.0 menjadi inovasi baru yang belum diterapkan sebelumnya di konteks Perguruan Tinggi Keagamaan. Dengan demikian, sistem ini dapat menjadi model decision support system berbasis data yang efektif dalam mendukung pelaksanaan AMI secara objektif, efisien, dan terukur.

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Abstract :

The Internal Quality Audit (IQA) is a core component of the university’s Internal Quality Assurance System (IQAS), aiming to ensure the implementation of the tridharma complies with national quality standards. However, IQA processes often face issues such as auditor subjectivity, delayed reporting, and lack of integrated digital documentation. This study aims to develop a Decision Support System for Internal Quality Audit based on IAPS 4.0, integrating the Profile Matching algorithm to enhance the objectivity and efficiency of evaluations. The system was developed using the CodeIgniter 4 framework with a Model–View–Controller (MVC) architecture and MySQL database. The research employed a Research and Development (R&D) approach with the Waterfall model, including needs analysis, system design, implementation, and testing. The system was evaluated using black box testing and validated by comparing the algorithmic results with manual audit outcomes. The findings indicate that the system operates effectively with an accuracy level of 94.2% (average deviation of 5.8%) compared to manual audits. It also reduces audit time by up to 40% and minimizes auditor subjectivity. Integrating the Profile Matching method with IAPS 4.0 indicators represents a novel contribution, particularly in the context of Islamic higher education institutions. Therefore, the system serves as a data-driven decision support model that enhances the effectiveness, efficiency, and objectivity of the internal quality audit process.