Optimization of Weight Backpropagation with Particle Swarm Optimization for Student Dropout Prediction

Sari, Eka Yulia; Kusrini, Kusrini; Sunyoto, Andi

doi:10.1109/icitisee48480.2019.9004032

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…As mentioned above, considering the availability and accessibility of academic performance data within both secondary and higher education contexts, the GPA features have been widely studied and were an influential factor in school dropout in multiple investigations [22][23][24]. Furthermore, in other studies [25,26], low GPA and absenteeism have been identified as standard features in students who drop out, while Barros et al (2019) [27] and Bedregal-Alpaca et al (2020) [28] identified the number of credits the student enrolls in.…”

Section: Related Workmentioning

confidence: 99%

Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques

Zapata-Medina,

Espinosa-Bedoya,

Jiménez-Builes

2024

Mathematics

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The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall.

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Section: Related Workmentioning

confidence: 99%

Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques

Zapata-Medina,

Espinosa-Bedoya,

Jiménez-Builes

2024

Mathematics

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“…The results of the study using the Backpropagation method produced an accuracy rate of 77.78% with 17 epochs. The highest accuracy was obtained using the 8-8-1 architecture [4].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Artificial Neural Network in Predicting Birth Rate in Batam City Using Backpropagation Method

Jufri

2021

JURTEKSI

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The population growth in Indonesia is increasing rapidly every year, so to help the government control the population growth through family planning programs, especially in the city of Batam. This study explains and describes one of the Artificial Terms Network methods, namely Backpropagation, where this method can predict what will happen in the future using data and information in the past. This study aims to predict the birth rate in the city of Batam to help the government with the family planning program. The data used is the annual data on the number of births in the city of Batam in 2016-2020 at The Civil Registry Office. To facilitate the analysis of research data, the data were tested using Matlab R2015b. In this study, the training process was carried out using 3 network architectures, namely 4-10-1, 5-18-1, and 4-43-1. Of these 3 architectures, the best is the 4-43-1 architecture with an accuracy rate of 91% and an MSE value of 0.0012205. The Backpropagation method can predict the amount of population growth in the city of Batam based on existing data in the past. Keywords: artificial neural network; backpropagation; prediction Abstrak: Pertumbuhan jumlah penduduk diindonesia yang setiap tahun meningkat dengan pesat, maka untuk membantu pemerintah mengendalikan jumlah pertumbuhan penduduk melalui program keluarga berencana khususnya dikota Batam. Penelitian ini menjelaskan dan memaparkan tentang salah satu metode Jaringan Syarat Tiruan yaitu Backpropagation, dimana metode ini dapat memprediksi apa yang akan terjadi masa yang akan datang dengan menggunakan data dan informasi dimasa lalu. Penelitian ini bertujuan untuk memprediksi tingkat kelahiran di kota Batam sehingga membatu pemerintah untuk perencanaan keluarga berencana. Data yang digunakan yaitu data tahunan jumlah kelahiran di kota Batam pada tahun 2016-2020 pada Dinas Kependudukan dan Catatan Sipil. Untuk mempermudah analisis data penelitian maka, data diuji menggunakan Matlab R2015b. Pada penelitian ini dilakukan proses pelatihan menggunakan 3 arsitektur jaringan yaitu 4-10-1, 5-18-1, dan 4-43-1. Dari ke-3 arsitektur ini yang terbaik adalah arsitektur 4-43-1 dengan tingkat akurasi sebesar 91% dan nilai MSE 0,0012205. Metode backpropagation mampu memprediksi jumlah pertumbuhan penduduk di kota Batam berdasarkan data yang ada dimasa lalu. Kata kunci: backpropagation; jaringan syaraf tiruan; prediksi

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Classification of COVID-19 Data Using Decision Tree Optimized with Particle Swarm Optimization and Genetic Algorithm

Rahmadeyan,

Mustakim,

Ocviani

et al. 2024

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)

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Optimization of Weight Backpropagation with Particle Swarm Optimization for Student Dropout Prediction

Cited by 6 publications

References 5 publications

Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques

Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques

Implementation of Artificial Neural Network in Predicting Birth Rate in Batam City Using Backpropagation Method

Classification of COVID-19 Data Using Decision Tree Optimized with Particle Swarm Optimization and Genetic Algorithm

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