Queue Optimization Using Heuristic-Based Decision Algorithms A Comparative Evaluation of Array-Based and Rule-Based Approache

Authors

DOI:

https://doi.org/10.30646/sinus.v23i2.965

Keywords:

sistem antrean, klasifikasi, metode array, metode MAPE, akurasi

Abstract

Penelitian ini menyajikan evaluasi kinerja komparatif antara dua pendekatan pemrosesan keputusan ringan untuk klasifikasi antrean, yaitu model indeks berbasis array dan logika berbasis aturan tradisional (if-else). Tujuan utama penelitian ini adalah mengidentifikasi strategi algoritmik yang efisien dan dapat diterapkan pada sistem layanan cerdas waktu nyata. Metode berbasis array dalam penelitian ini mengadopsi pendekatan berbasis heuristik ringan yang dirancang untuk mencapai efisiensi tinggi dalam pemrosesan keputusan tanpa kompleksitas komputasi tinggi, sehingga cocok diterapkan pada sistem cerdas yang memerlukan respons cepat dan akurat. Metode kedua diuji menggunakan data kondisi antrean nyata dengan variasi jumlah data uji (50, 100, dan 200 entri). Fokusnya terfokus pada akurasi klasifikasi dan kecepatan eksekusi. Hasil pengujian menunjukkan bahwa model berbasis array mencapai akurasi klasifikasi sebesar 94,6%, lebih tinggi dibandingkan metode berbasis aturan yang hanya mencapai 88,2%. Dari sisi kecepatan, pendekatan array menunjukkan efisiensi yang signifikan dengan waktu pemrosesan rata-rata 0,49 hingga 0,67 detik per entri, atau 13 hingga 16 kali lebih cepat dibandingkan metode if-else yang memerlukan waktu 7,86 hingga 8,95 detik per entri. Temuan ini menunjukkan bahwa logika berbasis array lebih efisien, skalabel, dan cocok berinteraksi ke dalam sistem manajemen antrean cerdas yang membutuhkan respons tinggi dan akurasi

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Published

2025-07-25

How to Cite

Apriliyanto, E., Sandi A, A. S., & Rahayu, Y. S. (2025). Queue Optimization Using Heuristic-Based Decision Algorithms A Comparative Evaluation of Array-Based and Rule-Based Approache. Jurnal Ilmiah SINUS, 23(2), 73–84. https://doi.org/10.30646/sinus.v23i2.965

Issue

Vol. 23 No. 2 (2025): Vol. 23 No. 2, Juli 2025

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