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All Journal Journal of Research and Technology Jurnal Teknik Industri Terintegrasi (JUTIN)
Musridho, R. Joko
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Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Impurity-Based Important Features for feature selection in Recursive Feature Elimination for Stock Price Forecasting: Fitur Penting Berbasis Impurity untuk pemilihan fitur dalam Recursive Feature Elimination untuk Peramalan Harga Saham Priyatno, Arif Mudi; Sudirman, Wahyu Febri; Musridho, R. Joko; Amalia, Fazilla
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 6 No. 4 (2023): Oktober
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v6i4.17726

Abstract

Stock investors perform stock price forecasting based on technical indicators and historical stock prices. The large number of technical indicators and historical data often leads to overfitting and ambiguity in forecasting using machine learning. In this paper, we proposed a feature selection approach using impurity-based important features in recursive feature elimination for stock price forecasting. The data utilized includes historical data and various moving averages. Feature selection is employed to reduce the number of features and obtain important and relevant features. The recursive feature elimination with impurity-based important features is utilized as the feature selection method. The machine learning methods employed are linear regression, support vector regression, multi-layer perceptron regression, and random forest regression. The measurement results of mean squared error (mse), root mean squared error (rmse), mean absolute error (mae), and mean absolute percentage error (mape) show that the optimal feature selection and machine learning method is achieved with six features and linear regression. The average mse, rmse, mae, and mape values are 0.000279, 0.016577, 0.012843, and 1.42236%, respectively. These results validate the effectiveness of impurity-based important features for feature selection in recursive feature elimination using historical data and various moving averages in stock price forecasting.
Design of a Biomass Waste Shredding Machine Using the Quality Function Deployment (QFD) Method Silma, Muhammad; Fiatno, Aris; Musridho, R. Joko; Sianturi, Gabriel; Henny, Henny; Setiawan, Benny; Tanjung, Lailatul Syifa
Journal of Research and Technology Vol. 11 No. 2 (2025): JRT Volume 11 No 2 Des 2025
Publisher : 2477 - 6165

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/w6fzm137

Abstract

This research aims to design a biomass waste shredder using the Quality Function Deployment (QFD) method to create a product that aligns with the needs and desires of users. The QFD method is employed to identify user requirements, translate them into technical specifications, and produce an optimal product design. The study involved 20 respondents from the Universitas Pahlawan Botanical Garden team as subjects to collect data on needs through closed questionnaires. The results indicate that consumers desire a shredder with key criteria, including speeding up the shredding process, high durability, safety in use, as well as ease of mobility and storage. The designed shredder product features several advantages, such as: An inlet feeder for biomass waste, six cutting blades with a bolt system, wheels for easy transportation. Through the House of Quality (HOQ) matrix, consumer needs were successfully translated into priorities for the development of the product's technical features. Compared to other shredders, this shredder has competitive advantages in functional aspects, user safety, and design. This demonstrates that the application of the QFD method is effective in producing an innovative, functional shredder design that meets user preferences. The findings of this study are expected to serve as a reference for the future development of shredder products.
Co-Authors Amalia, Fazilla Arif Mudi Priyatno Aris Fiatno Benny Setiawan, Benny Henny Henny, Henny Sianturi, Gabriel Silma, Muhammad Tanjung, Lailatul Syifa Wahyu Febri Ramadhan Sudirman