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All Journal Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M)
Muh Arif Rahman
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : knowledge engineering and data science

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Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network Priambodo, Bagus; Mahmudy, Wayan Firdaus; Rahman, Muh Arif
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Earthquakes, a type of inevitable natural disaster, is responsible for the highest average death toll per year compared to other types of a natural disaster. Even though it is inevitable, but it can be anticipated to minimize damage and casualties, such as predicting the earthquake‘s magnitude using a neural network. In this study, a backpropagation algorithm is used to train the multilayer neural network to weekly predict the average magnitude of earthquakes in grid-based locations in Indonesia. Based on the findings in this research, the neural network is able to predict the magnitude of earthquakes in grid-based locations across Indonesia with a minimum error rate of 0.094 in 34.475 seconds. This best result is achieved when the neural network is trained for 210 epochs, with 16 neurons used in the input and output layer, one hidden layer consisted of 5 neurons and a learning rate of 0.1. This result showed backpropagation has pretty good generalization capability in order to map the relations between variables when mathematical function is not explicitly available.
Efficient Scheduling of Plantation Company Workers using Genetic Algorithm Mahmudy, Wayan Firdaus; Pardede, Andreas; Widodo, Agus Wahyu; Rahman, Muh Arif
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually.
Co-Authors Agus Wahyu Widodo, Agus Wahyu Bagus Priambodo Candra Dewi Indriati Indriati Iqbal, Thariq Issa Arwani Kurniawan, I Gede Jayadi Lailil Muflikhah Muzakki, Rayhan Pardede, Andreas Qodafi, Muhammad Muammar Al Rozaq, Fatkur Utami, Nofi Sri Wayan Firdaus Mahmudy