Sistem Penjadwalan Mata Kuliah dengan Minimal Perturbation Menggunakan Algoritma Genetika, Simulated Annealing, dan TOPSIS di Departemen Teknik Informatika ITS

Arina, Nayla (2026) Sistem Penjadwalan Mata Kuliah dengan Minimal Perturbation Menggunakan Algoritma Genetika, Simulated Annealing, dan TOPSIS di Departemen Teknik Informatika ITS. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penjadwalan mata kuliah di perguruan tinggi merupakan masalah optimasi kombinatorial kompleks dengan batasan akademik dan preferensi dosen. Penelitian ini mengembangkan sistem yang mengintegrasikan TOPSIS untuk menentukan nilai senioritas dosen, algoritma Genetika (GA) untuk menyusun jadwal mata kuliah, dan Simulated Annealing (SA) untuk penanganan disrupsi berbasis konsep minimal perturbation. TOPSIS menggunakan pembobotan Mean Weight (MW) untuk menghasilkan nilai senioritas (Ci*) yang unik pada rentang 0–1 bagi 53 dosen sebagai dasar prioritas. Pada penjadwalan mata kuliah, GA dengan improving rate 0,5 terbukti unggul dan konsisten dibanding penjadwalan manual dan sistem semi-digital berbasis SA pada seluruh metrik kualitas solusi dengan menghilangkan pelanggaran hard constraint, menurunkan soft constraint hingga 98,6%–100% dan 97,9%–100%, meningkatkan kepuasan dosen hingga 21,3%–41,6% dan 22,1%–26,7%, serta menurunkan penalti preferensi dosen hingga 98,7%–100% dan 98,6%–100% pada kedua metode pembanding. Waktu komputasi GA lebih bervariasi karena early stopping, tetapi tetap kompetitif, lebih cepat dibanding sistem manual, serta selisih 47,6% lebih lambat pada semester ganjil dan 27,8% lebih cepat pada semester genap dibanding sistem semi-digital berbasis SA yang menunjukkan trade-off antara kualitas solusi dan waktu komputasi. Pada penanganan disrupsi, SA secara konsisten memulihkan seluruh skenario dengan menghilangkan pelanggaran hard constraint, menurunkan penalti 97,1%–98,5% pada semester ganjil dan 96,9%–98,5% pada semester genap dengan hanya menyisakan soft constraint stabilitas, menjaga kepuasan dan penalti preferensi dosen, membatasi jarak Hamming maksimum 3 sesuai minimal perturbation, serta menyelesaikan seluruh skenario dalam waktu kurang dari 21 detik.
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Course scheduling at universities is a complex combinatorial optimization problem with academic constraints and lecturer preferences. This study develops a system integrating TOPSIS to determine lecturer seniority, a Genetic Algorithm (GA) for course scheduling, and Simulated Annealing (SA) for schedule disruption handling based on the minimal perturbation concept. TOPSIS applies Mean Weight (MW) to generate lecturer seniority scores (Ci*) uniquely scaled in the range 0–1 for 53 lecturers as the basis for priority ranking. In course scheduling, GA with an improving rate of 0.5 is proven to be superior and consistent compared to manual scheduling and a semi-digital SA-based system across all solution quality metrics, eliminating hard constraint violations, reducing soft constraints by 98.6%–100% and 97.9%–100%, increasing lecturer satisfaction by 21.3%–41.6% and 22.1%–26.7%, and decreasing lecturer preference penalties by 98.7%–100% and 98.6%–100% relative to the two benchmark methods. GA computation time is more variable due to early stopping but remains competitive, faster than manual scheduling, with a trade-off of 47.6% slower on the odd semester and 27.8% faster on the even semester compared to the semi-digital SA system, indicating a trade-off between solution quality and computational stability. In disruption handling, SA consistently restores all scenarios by eliminating hard constraint violations, reducing penalties by 97.1%–98.5% in the odd semester and 96.9%–98.5% in the even semester while retaining only stability-related soft constraints, maintaining lecturer satisfaction and preference penalties, limiting the Hamming distance to a maximum of 3 in accordance with minimal perturbation, and completing all scenarios within 21 seconds.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Algoritma Genetika, Minimal Perturbation, Senioritas Dosen, Simulated Annealing, TOPSIS, Penjadwalan Mata Kuliah, Course Scheduling, Genetic Algorithm, Lecturer Seniority, Minimal Perturbation, Simulated Annealing, TOPSIS.
Subjects:	Q Science > QA Mathematics > QA9.58 Algorithms
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Nayla Arina
Date Deposited:	18 Jul 2026 08:30
Last Modified:	18 Jul 2026 08:30
URI:	http://repository.its.ac.id/id/eprint/135410

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