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All Journal Jurnal Sains dan Seni ITS Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA)
Brodjol Sutijo Suprih Ulama
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Peramalan Harga Saham Jakarta Islamic Index Menggunakan Metode Vector Autoregressive Farida Nur Hayati; Brodjol Sutijo Suprih Ulama
Jurnal Sains dan Seni ITS Vol 5, No 2 (2016)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1095.899 KB) | DOI: 10.12962/j23373520.v5i2.16931

Abstract

Saham merupakan instrumen investasi yang banyak dipilih para investor, karena saham mampu memberikan tingkat keuntungan yang menarik. Sejak 12 Mei 2011, Bursa Efek Indonesia (BEI) mempunyai dua indeks harga saham Syariah, yaitu Indeks Saham Syariah Indonesia (ISSI) dan Jakarta Islamic Index (JII). Saham-saham dalam indeks ini mempunyai keistimewaan yaitu memiliki tingkat hutang yang rendah, sehingga risiko dalam berinvestasi semakin terkendali [1]. Terdapat 8 sektor di dalam indeks JII salah satunya adalah sektor properti. Perkembangan industri properti dan real estate menunjukkan pertumbuhan yang sangat pesat. Hal ini ditandai dengan maraknya pembangunan perumahan, apertemen, perkantoran dan perhotelan di kota-kota besar. fluktuasi harga saham sektor properti merupakan salah satu masalah bagi investor dalam menjual atau membeli saham sehingga diperlukan analisis untuk meramalkan harga saham guna meminimalkan risiko yang diperoleh. Metode VAR adalah metode yang digunakan untuk meramalkan data dua variabel atau lebih yang memiliki hubungan timbal balik. Berdasarkan analisis peramalan harga saham sub sektor properti dan real estate dengan metode VAR maka dapat diketahui model akhir yang terbentuk adalah VARX (1,1) dengan RMSE 3 periode ke depan variabel ASRI sebesar 6,2, BSDE sebesar 22,4, LPKR sebesar 22,7, PWON sebesar 12,8, dan SMRA sebesar 14,2
Kajian Penentuan Variabel dan Pendekatan Model Peramalan Harga Saham INCO Moch Abdillah Nafis; Brodjol Sutijo Suprih Ulama; Rakha Maheswara
Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora Vol. 9 No. 3 (2026): Kaganga: Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/tgvd6v19

Abstract

Nickel is a strategic commodity that plays an important role in the global industry, particularly as a key material for electric vehicle batteries. PT Vale Indonesia Tbk (INCO), a nickel mining company listed on the Indonesia Stock Exchange, experiences stock price fluctuations driven by technical and macroeconomic factors. This study aims to develop a forecasting model for INCO's stock price using Bidirectional Long Short-Term Memory (Bi-LSTM) optimized with a Genetic Algorithm (GA) and to identify the most influential predictor variables using Shapley Additive Explanations (SHAP). Monthly data from January 2007 to December 2025 include INCO's stock price, nickel price, exchange rate, inflation, and the BI-Rate. A forward selection procedure was applied to determine the best predictor combination, after which the model was trained using the Adam Optimizer with GA-optimized hyperparameters and evaluated using Mean Absolute Percentage Error (MAPE). The best model was obtained from the combination of historical stock price, nickel price, and the BI-Rate, with optimal hyperparameters of 150 epochs, a batch size of 20, 150 neurons, a learning rate of 0.002844, and a dropout of 0.0502, producing a MAPE of 8.751%. SHAP results indicate that historical stock price and nickel price contribute the most to the prediction, whereas the BI-Rate contributes relatively less. This study concludes that combining Bi-LSTM, GA, and predictor variable selection improves the forecasting accuracy of INCO's stock price and provides useful insights for long-term investment decision-making in the nickel mining sector. Keywords:  Bidirectional Long Short Term Memory, Forecasting, Genetic Algorithm, Macroeconomics, Stocks
Co-Authors Farida Nur Hayati Moch Abdillah Nafis Rakha Maheswara