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All Journal Jurnal Pengembangan Rekayasa dan Teknologi BBM (Buletin Bisnis dan Manajemen) Information Science and Library
Kartika, Mira
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Mechanical Engineering Transportation

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FAKTOR-FAKTOR YANG MEMPENGARUHI HARGA SAHAM PERUSAHAAN TEKSTIL DAN GARMEN DI BURSA EFEK INDONESIA Kartika, Mira; Tahwin, Muhammad
BBM (Buletin Bisnis & Manajemen) Vol 6, No 2 (2020): Volume 6, No. 02, 2020
Publisher : Sekolah Tinggi Ilmu Ekonomi YPPI Rembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47686/bbm.v6i2.301

Abstract

The research aims to prove and analyze the influence of current ratio, return on asset, debt to equity and earning per share towards share prices in textile and garment company listed in Indonesia Stock Exchange.  The population of the research is textile and garment factories listed in Indonesia Stock Exchange in 2013 – 2017 periods. Technique selection sample used is purposive sample. Data analyzes method used is multiple linier regression analyzes. The research result shows that current ratio has insignificant negative effect towards share price. Return to asset ratio has insignificant positive effect towards share price. Debt to equity ratio has significant negative effect towards share price.  Earning per share has significant positive effect towards share price. Determination test result shows that variable current ratio, return on asset, debt to equity and earning to share are able to explain of 63,1% share price, while 36,9%  of the remaining is explained by other factors that are not included  in this research model.
IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI JUMLAH JIWA TERDAMPAK BANJIR DI DKI JAKARTA Kartika, Mira; Sekarwati, Kemal Ade
Information Science and Library Vol. 5 No. 2 (2024): Desember
Publisher : UPT Perpustakaan Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jisl.v5i2.5346

Abstract

Floods are natural disasters that often occur in the DKI Jakarta area. DKI Jakarta government needs to anticipate the impact of the flood disaster by estimating the number of people affected by the flood. The number of people affected by floods that are uncertain every month can be predicted for the future. There are many ways that can be predict the number of people affected by floods, one of them is artificial neural network method. One of learning algorithms in artificial neural networks is backpropagation algorithm. This research applies an artificial neural network method with backpropagation algorithm to predict the number of people affected by floods in DKI Jakarta. In this research, training process was carried out 100 times on each network architecture (12-10-1, 12-12-1, 12-14-1) with several parameters such as epoch, momentum constant, and learning rate. The best results in the training process are carried out to testing process to test the network. In the testing process, the best results are 12-10-1 architecture with an accuracy rate 98.704%. Based on these results, it can be said that this network can predict well and can be implemented for forecasting the number of people affected by floods in DKI Jakarta.
IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI JUMLAH JIWA TERDAMPAK BANJIR DI DKI JAKARTA Kartika, Mira; Sekarwati, Ade Kemal
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 6 No. 2 (2022): November (2022)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v18i2.7082

Abstract

Floods are natural disasters that often occur in the DKI Jakarta area. DKI Jakarta government needs to anticipate the impact of the flood disaster by estimating the number of people affected by the flood. The number of people affected by floods that are uncertain every month can be predicted for the future. There are many ways that can be predict the number of people affected by floods, one of them is artificial neural network method. One of learning algorithms in artificial neural networks is backpropagation algorithm. This research applies an artificial neural network method with backpropagation algorithm to predict the number of people affected by floods in DKI Jakarta. In this research, training process was carried out 100 times on each network architecture (12-10-1, 12-12-1, 12-14-1) with several parameters such as epoch, momentum constant, and learning rate. The best results in the training process are carried out to testing process to test the network. In the testing process, the best results are 12-10-1 architecture with an accuracy rate 98.704%. Based on these results, it can be said that this network can predict well and can be implemented for forecasting the number of people affected by floods in DKI Jakarta.
Co-Authors Kemal Ade Sekarwati Muhammad Tahwin Sekarwati, Ade Kemal