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Course Scheduling Optimization Using Genetic Algorithms with Fuzzy Tsukamoto-Based Fitness Adjustment

Jurnal Penelitian Pendidikan · Vol. 42 No. 2 (2025) · hlm. 320–326

DOI
10.15294/jpp.v42i2.31594
ISSN
2580-4154, 2503-1260
Terbit
2025-10-31
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study develops a course scheduling optimization system that integrates a genetic algorithm with the Tsukamoto fuzzy inference system to dynamically adjust fitness values. The objective of this research is to overcome the limitations of conventional genetic algorithms that rely on static fitness functions, which only evaluate schedule quality based on the number of constraint violations. The Tsukamoto fuzzy inference system is designed with three input variables: constraint violation level, lecturer workload distribution, and classroom utilization efficiency. It employs 27 fuzzy rules based on triangular and trapezoidal membership functions to produce a fitness adjustment factor.The research methodology consists of four stages: requirements analysis and problem modeling, Tsukamoto fuzzy inference system design, hybrid genetic algorithm implementation, and performance testing and evaluation. Experiments were conducted using a synthetic dataset comprising 50 courses, 20 lecturers, 15 classrooms, and 30 weekly time slots. The results show that the proposed hybrid genetic algorithm achieves 42% faster convergence with an average fitness value of 0.89 compared to 0.76 in the conventional algorithm. Constraint satisfaction improved from 82.4% to 94.7%, lecturer workload distribution became more balanced with the coefficient of variation decreasing from 0.34 to 0.19, and classroom utilization efficiency increased from 76.8% to 88.5%. Statistical tests indicate a significant difference (p-value < 0.001) with a substantial effect size (Cohen’s d = 1.23). This research contributes to the development of a hybrid approach that integrates the Tsukamoto fuzzy inference system into genetic algorithms, resulting in more optimal, adaptive, and efficient course schedules compared to conventional methods.

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  • State University of Semarang
  • Universitas Nahdlatul Ulama Blitar
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Rekan penulisOrganisasiArtikel bersama
Muh. SholehAfiliasi tidak diketahui2
Martanto Setyo HusodoAfiliasi tidak diketahui2
Riza ArifudinState University of Semarang2
Florentina Yuni AriniState University of Semarang1
Athoillah AthoillahAfiliasi tidak diketahui1
Endang SugihartiState University of Semarang1
Kholiq BudimanAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Alfath YanuartoAfiliasi tidak diketahui1
Dinar Diaz SeptianAfiliasi tidak diketahui1
Akhmad MunawarAfiliasi tidak diketahui1
Andika Enggal RamadhanAfiliasi tidak diketahui1
Bhekti KumorowatiAfiliasi tidak diketahui1
Sugianto SugiantoUniversitas Nahdlatul Ulama Blitar1
Cahya WulandariState University of Semarang1
Muhamad Sayuti HassanAfiliasi tidak diketahui1
Zidney Ilma Fazaada EmhaAfiliasi tidak diketahui1
Angel Novita SinuratAfiliasi tidak diketahui1
Tazkiyatun Aeni AmaliyahAfiliasi tidak diketahui1
Devi Tri WidyantiAfiliasi tidak diketahui1
Much Azis MuslimAfiliasi tidak diketahui1
Ahmad WarsunAfiliasi tidak diketahui1
Pradistia Edo KristiantoAfiliasi tidak diketahui1
Syehudin SyehudinAfiliasi tidak diketahui1
Alamsyah AlamsyahState University of Semarang1
Hendi SusantoAfiliasi tidak diketahui1
Annisya Dira PrastiwiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/JPP/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Course Scheduling Optimization Using Genetic Algorithms with Fuzzy Tsukamoto-Based Fitness Adjustment</dc:title>
	<dc:creator xml:lang="en">Alexander, Taripar Matius </dc:creator>
	<dc:creator xml:lang="en">Putra, Anggyi Trisnawan </dc:creator>
	<dc:subject xml:lang="en">genetic algorithm, fuzzy Tsukamoto, course scheduling, optimization, dynamic fitness</dc:subject>
	<dc:description xml:lang="en">This study develops a course scheduling optimization system that integrates a genetic algorithm with the Tsukamoto fuzzy inference system to dynamically adjust fitness values. The objective of this research is to overcome the limitations of conventional genetic algorithms that rely on static fitness functions, which only evaluate schedule quality based on the number of constraint violations. The Tsukamoto fuzzy inference system is designed with three input variables: constraint violation level, lecturer workload distribution, and classroom utilization efficiency. It employs 27 fuzzy rules based on triangular and trapezoidal membership functions to produce a fitness adjustment factor.The research methodology consists of four stages: requirements analysis and problem modeling, Tsukamoto fuzzy inference system design, hybrid genetic algorithm implementation, and performance testing and evaluation. Experiments were conducted using a synthetic dataset comprising 50 courses, 20 lecturers, 15 classrooms, and 30 weekly time slots. The results show that the proposed hybrid genetic algorithm achieves 42% faster convergence with an average fitness value of 0.89 compared to 0.76 in the conventional algorithm. Constraint satisfaction improved from 82.4% to 94.7%, lecturer workload distribution became more balanced with the coefficient of variation decreasing from 0.34 to 0.19, and classroom utilization efficiency increased from 76.8% to 88.5%. Statistical tests indicate a significant difference (p-value &amp;lt; 0.001) with a substantial effect size (Cohen’s d = 1.23). This research contributes to the development of a hybrid approach that integrates the Tsukamoto fuzzy inference system into genetic algorithms, resulting in more optimal, adaptive, and efficient course schedules compared to conventional methods.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/JPP/article/view/31594</dc:identifier>
	<dc:identifier>10.15294/jpp.v42i2.31594</dc:identifier>
	<dc:source xml:lang="en">Jurnal Penelitian Pendidikan; Vol. 42 No. 2 (2025): October 2025; 320-326</dc:source>
	<dc:source>2580-4154</dc:source>
	<dc:source>2503-1260</dc:source>
	<dc:source>10.15294/jpp.v42i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/JPP/article/view/31594/5427</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Penelitian Pendidikan</dc:rights>
</oai_dc:dc>