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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Puspita, Melli
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Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Peningkatan Akurasi Nilai Harga Saham Menggunakan Metode Long Short-Term Memory (LSTM) pada PT Unilever Tbk Arinal, Veri; Puspita, Melli
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1190

Abstract

The rapid development of technology has an impact on the economy of society, one of which is investing in stocks. Stocks are a proof of an individual's ownership of an asset in a company. However, stock prices have a very high level of fluctuation, so an accurate method is needed to help predict stock prices. LSTM and GRU were chosen due to their intrinsic ability to handle long-term and short-term issues in time series data. LSTM has a complex memory structure that allows decision-making based on long-term and short-term information. Meanwhile, GRU has a simpler structure with a focus on gate mechanisms to control the flow of information, resulting in a lighter and faster model. Therefore, this study will compare two RNN methods, namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting stock prices using the stock price data of PT. Unilever (UNVR) with evaluation metrics MAPE and RMSE. The combination of parameters used to evaluate the MAPE and RMSE values in this study includes learning rate, timesteps, batch size, and epoch. The results of this study indicate that the GRU method is more accurate compared to the LSTM method. This is evidenced by the evaluation results of the LSTM method with the lowest MAPE value of 2.42% and the lowest RMSE value of 0.01807, while the evaluation results of the GRU method with the lowest MAPE value of 2.14% and the lowest RMSE value of 0.01775. The combination of parameters used in this study also has an impact on the final MAPE and RMSE results, especially with the use of learning rates of 0.001 and 0.0001. Thus, it can be concluded in this study that the GRU method is more accurate and effective compared to the LSTM method in predicting stock prices.
Penerapan Model Pembelajaran Kooperatif Tipe Team Assisted Individualization Terhadap Hasil Belajar Sejarah Di SMAN 1 Solok Puspita, Melli; Michellia Karima, Elfa
Jurnal Pendidikan Tambusai Vol. 8 No. 3 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Penelitian ini dilakukan untuk melihat pengaruh penggunaan model pembelajaran kooperatif tipe team assisted individualization terhadap hasil belajar Sejarah di SMAN 1 solok. Hal ini dilatarbelakangi oleh penggunaan model pemberian informasi dengan metode ceramah yang masih dominan dilakukan oleh guru SMAN 1 Solok yang mengakibatkan rendahnya hasil belajar siswa. Jenis penelitian ini adalah kuantitatif dengan metode Quasi Eksperimen tipe Nonequivalent Control Group Design. Populasinya adalah seluruh siswa kelas XII IPS SMAN 1 solok. Kelas yang Terpilih sebagai sampel adalah XII IPS 3 sebagai kelas eksperimen dan XII IPS 4 sebagai kelas kontrol. Hasil penelitian yang ditemukan yaitu skor rata-rata post-test kelas eksperimen adalah 80,47 sedangkan skor rata-rata kelas kontrol adalah 73,4. Pengujian dilakukan dengan perhitungan uji-t sebagai dasar pengambilan Keputusan. Berdasarkan Uji Independent Sample T Test, dihasilkan nilai 0,01 < 0,05. Kesimpulannya berarti Ha diterima dan H0 ditolak. Dengan demikian, dinyatakan bahwa penggunaan model kooperatif tipe team assisted individualization secara signifikan mempengaruhi hasil belajar Sejarah siswa di SMAN 1 solok..
Co-Authors Arinal, Veri Michellia Karima, Elfa