Sie, Yoseph Widjaja (2025) Studi penerapan deep learning untuk prediksi pergerakan indeks harga saham gabungan. Undergraduate thesis, Widya Mandala Surabaya Catholic University.
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Abstract
Penelitian ini mengembangkan model prediksi pergerakan Indeks Harga Saham Gabungan (IHSG) menggunakan metode Long Short-Term Memory (LSTM). Tiga pendekatan model yang diuji adalah Univariate, Multivariate All Feature, dan Multivariate Selected Feature, dengan mempertimbangkan faktor eksternal seperti harga emas dunia, harga minyak dunia, nilai tukar rupiah terhadap dolar Amerika, dan indeks saham internasional. Hasil evaluasi menunjukkan bahwa model Multivariate All Feature memberikan kinerja terbaik dengan MAPE sebesar 0.76, RMSE sebesar 66.72, dan MAE sebesar 51.58, lebih unggul dibandingkan dengan kedua model lainnya. Uji signifikansi menggunakan ANOVA dan Tukey HSD mengonfirmasi perbedaan signifikan antara ketiga model. Penelitian ini menyarankan bahwa memasukkan variabel eksternal dapat meningkatkan akurasi prediksi pergerakan IHSG.
Item Type: | Thesis (Undergraduate) |
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Department: | S1 - Teknik Industri |
Contributors: | Contribution Contributors NIDN / NIDK Email Thesis advisor Karijadi, Irene NIDN0717019205 UNSPECIFIED Thesis advisor Dewi, Dian Retno Sari NIDN0713127301 UNSPECIFIED |
Uncontrolled Keywords: | LSTM, IHSG, prediksi, univariate, multivariate. |
Subjects: | Engineering Engineering > Industrial Engineering |
Divisions: | Faculty of Engineering > Industrial Engineering Study Program |
Depositing User: | Yoseph Widjaja Sie |
Date Deposited: | 08 Jul 2025 08:56 |
Last Modified: | 08 Jul 2025 08:56 |
URI: | https://repositori.ukwms.ac.id/id/eprint/43469 |
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