Comparing Selection Method in Course Scheduling Using Genetic Algorithm

Saptarini, Ni Gusti Ayu Putu Harry; Ciptayani, Putu Indah; Wisswani, Ni Wayan; Suasnawa, I Wayan; Indrayana, Nyoman Eddy

doi:10.2991/icst-18.2018.119

Cited by 4 publications

(2 citation statements)

References 23 publications

(23 reference statements)

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“…6) Use a backward pass on the ANN by updating the weight and bias with a genetic algorithm begin with selecting the numbers of chromosomes for the next generation based on the fitness value (RMSE) according to the retained value of p (truncation selection [20] is used here). Place the results of the chromosome selection in the reproduction pool.…”

Section: Artificial Neural Network With Genetic Algorithmmentioning

confidence: 99%

Comparing Restricted Boltzmann Machine â€“ Backpropagation Neural Networks, Artificial Neural Network â€“ Genetic Algorithm and Artificial Neural Network â€“ Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Handari¹,

Wulandari²,

Aquita³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Dengue hemorrhagic fever (DHF) is a common disease in tropical countries such as Indonesia that is often fatal. Early predictions of DHF case numbers help reduce the risk of community transmission and help related authorities develop prevention plans and strategies. Previous research shows that temperature, rainfall, and humidity indirectly affect DHF spread patterns. Therefore, this research uses and compares three machine learning models-restricted Boltzmann machine-backpropagation neural network (RBM-BPNN), artificial neural network-genetic algorithm (ANN-GA), and artificial neural network-particle swarm optimization (ANN-PSO)-to predict DHF case numbers in DKI Jakarta, the capital of Indonesia, which is in the DHF red zone. RBM and PSO are used to calculate optimal initial weight and bias before starting the prediction stage with ANN; meanwhile, GA updates weight and bias during the backward pass in ANN. The data includes temperature, rainfall, and humidity, plus previous DHF case data for five districts in DKI Jakarta from Jan. 6, 2009, to Sept. 25, 2017. We used Arima, Autocorrelation, and Pearson correlation for pre-processing data. The DHF case data fluctuates strongly and requires the moving averages method. The data consists of 70% training data and 30% testing data. The results show that each district requires different model architectures for the best predictions. `The best RMSE prediction of DHF cases with RBM-BPNN in Central Jakarta is 3,78%; the best RMSEs using ANN-GA in North and East Jakarta are 5,65% and 5,99%, respectively. The ANN-PSO model had the largest RMSE value in every district, with an average of 8,43%.

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Section: Artificial Neural Network With Genetic Algorithmmentioning

confidence: 99%

Comparing Restricted Boltzmann Machine â€“ Backpropagation Neural Networks, Artificial Neural Network â€“ Genetic Algorithm and Artificial Neural Network â€“ Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Handari¹,

Wulandari²,

Aquita³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Selain penjadwalan mata kuliah, GA mampu menghasilkan solusi optimal pada penjadwalan mata pelajaran. Hal itu sesuai pada makalah [13], [20]. Namun pada makalah [8], GA belum mampu menunjukkan hasil optimal.…”

Section: Analisis Algoritmaunclassified

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Handayani¹,

Fudholi²,

Rani

2020

petir

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Penjadwalan mata kuliah merupakan hal penting yang dilakukan pada awal semester akademik. Proses penyusunan jadwal kuliah secara manual seringkali mengalami kesulitan karena terdapat beberapa konstrain sehingga membutuhkan waktu yang lama. Penelitian ini bertujuan mengkaji algoritma-algoritma yang sesuai dengan masalah penjadwalan mata kuliah. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan optimasi penjadwalan. Proses pencarian literatur dilakukan pada Google Scholar dan Science Direct dengan memasukkan kata kunci utama “course timetable”, “university timetable problem”, “school scheduling”, dan “algoritma penjadwalan”. Hasil analisis literatur meliputi sebaran domain, analisis algoritma serta gap dari penelitian sebelumnya. Pada penelitian sebelumnya terdapat kekurangan seperti algoritma yang tidak dapat menghasilkan solusi optimal. Hasil sebaran domain yang diperoleh ialah universitas dan sekolah dengan persentase 88% dan 12% dari keseluruhan makalah. Adanya temuan 14 sebaran algoritma dapat diklasifikasikan menjadi 3 metode, yaitu heuristic, metaheuristic, dan hyper-heuristic. Berdasarkan hasil analisis, dapat diberikan beberapa rekomendasi. Untuk optimasi yang cepat, Simulated Annealing (SA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.481-10.102s. Untuk solusi waktu dan nilai fitness terbaik, Genetic Algorithm (GA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.964-73.461s dan nilai fitness 1.

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GALAS: Greedy Local-Adjusting Room Allocation Framework

Chi,

Xie,

et al. 2023

2023 IEEE Smart World Congress (SWC)

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Comparing Selection Method in Course Scheduling Using Genetic Algorithm

Cited by 4 publications

References 23 publications

Comparing Restricted Boltzmann Machine â€“ Backpropagation Neural Networks, Artificial Neural Network â€“ Genetic Algorithm and Artificial Neural Network â€“ Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Comparing Restricted Boltzmann Machine â€“ Backpropagation Neural Networks, Artificial Neural Network â€“ Genetic Algorithm and Artificial Neural Network â€“ Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

GALAS: Greedy Local-Adjusting Room Allocation Framework

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