PERAMALAN HARGA SAHAM NVIDIA DENGAN METODE DOUBLE MOVING AVERAGE
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)
Indonesia
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)
Indonesia
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)
Indonesia
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOM CKI)
Indonesia
Article Submitted: 25 June 2024
Article Published: 29 June 2024
Abstract
The movement of NVIDIA's stock price greatly affects investment decisions, so accurate forecasting is very important to influence investment decisions. This research will apply the data mining process for forecasting NVIDIA stock prices using the Double Moving Average method with the application of order 2 and order 3 models. The purpose of this study is to determine the forecasting of the NVIDIA stock price index based on historical data. The results show that stock price forecasting using the Double Moving Average method order 2 model is more accurate and in accordance with actual or actual results. On the other hand, forecasting the Double Moving Average method with the order 3 model produces unsatisfactory forecasts that can be used, making it less suitable for dynamic markets. Therefore, the results of forecasting NVIDIA stock prices using the Double Moving Average method order 2 obtained an accuracy of 98% compared to order 3 of 67%. Based on the comparison results of using orders 2 and 3, it can be an important factor for investors to help make investment decisions in terms of forecasting the next production of NVIDIA shares.
Keywords
References
M. Abdul Dwiyanto Suyudi, E. C. Djamal, A. Maspupah Jurusan Informatika, and F. Sains dan Informatika Universitas Jenderal Achmad Yani Cimahi, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,” Semin. Nas. Apl. Teknol. Inf., pp. 1907–5022, 2019.
V. P. Ramadhan and F. Y. Pamuji, “Analisis Perbandingan Algoritma Forecasting dalam Prediksi Harga Saham LQ45 PT Bank Mandiri Sekuritas (BMRI),” J. Teknol. dan Manaj. Inform., vol. 8, no. 1, pp. 39–45, 2022, doi: 10.26905/jtmi.v8i1.6092.
Z. A. Sari and M. Andarwati, “Peramalan Double Moving Average Dan Double Exponential Smoothing Jumlah Penumpang Di Stasiun Kotabaru Malang,” J. Inf. Syst. Manag. Digit. Bus., vol. 1, no. 2, pp. 263–272, 2024, doi: 10.59407/jismdb.v1i2.436.
M. N. Rafirhan et al., “Peramalan Indeks Harga Saham PT XYZ Menggunakan Metode Double Moving Average (DMA),” IJESPG (International J. Eng. Econ. Soc. Polit. Gov., vol. 1, no. 3, pp. 183–190, 2023, [Online]. Available: https://ijespgjournal.org/index.php/ijespg/article/view/52
M. B. Yel et al., “Sebuah Penerapan Metode Naïve Bayes dalam Klasifikasi Masyarakat Miskin pada Desa Tanjungsari,” vol. 6, no. 1, pp. 77–83, 2024.
Tundo, “Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Fuzzy Tsukamoto Dengan Rule Yang Terbentuk Menggunakan Decision Tree Reptree,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 9, no. 2, pp. 253–265, 2020, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/janapati/article/view/23868
N. N. M. Cahyani and L. P. Mahyuni, “Akurasi Moving Average Dalam Prediksi Saham Lq45 Di Bursa Efek Indonesia,” E-Jurnal Manaj. Univ. Udayana, vol. 9, no. 7, p. 2769, 2020, doi: 10.24843/ejmunud.2020.v09.i07.p1.
A. Kurniawati and A. Arima, “Analisis Prediksi Harga Saham PT. Astra International Tbk Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) dan Support Vector Regression (SVR),” J. Ilm. Komputasi, vol. 20, no. 3, pp. 417–423, 2021, doi: 10.32409/jikstik.20.3.2732.
C. V. Hudiyanti, F. A. Bachtiar, and B. D. Setiawan, “Perbandingan Double Moving Average dan Double Exponential Smoothing untuk Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Bandara Ngurah Rai,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2667–2672, 2019.
B. Diharjo and R. Arief, “Prediksi Harga Saham Indeks Idx30 Di Indonesia Saat Pandemi Covid-19 Dengan Autoregressive Integrated Moving Average (Arima),” J. Ilm. Inform. Komput., vol. 26, no. 3, pp. 248–276, 2021, doi: 10.35760/ik.2021.v26i3.5029.
Hilman Winnos, Richashanty Septima, and Husna Gemasih, “Perbandingan Metode Regresi Linier Berganda dan Autoregressive Integrated Moving Average (ARIMA) Untuk Prediksi Saham PT. BSI, Tbk.,” Ocean Eng. J. Ilmu Tek. dan Teknol. Marit., vol. 1, no. 4, pp. 15–23, 2022, doi: 10.58192/ocean.v1i4.350.
K. Harahap, “Prediksi Persediaan Stok Sparepart Mesin Produksi Kategori Fast Moving Menggunakan Metode Double Moving Average Berbasis Website Pada PT. Intan Hevea …,” J. Info Digit, vol. 2, no. 1, 2024, [Online]. Available: https://kti.potensi-utama.ac.id/index.php/JID/article/view/1540%0Ahttps://kti.potensi-utama.ac.id/index.php/JID/article/download/1540/589
G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM),” J. Nas. Teknol. dan Sist. Inf., vol. 8, no. 3, pp. 164–172, 2023, doi: 10.25077/teknosi.v8i3.2022.164-172.
B. yafitra Pandji, Indwiarti, and A. A. Rohmawati, “Perbandingan Prediksi Harga Saham Dengan Model Arima Dan Artificial Neural Network,” Ind. Comput., vol. 4, no. 2, pp. 189–198, 2019, doi: 10.21108/indojc.2019.4.2.344.
W. Y. Rusyida and V. Y. Pratama, “Prediksi Harga Saham Garuda Indonesia di Tengah Pandemi Covid-19 Menggunakan Metode ARIMA,” Sq. J. Math. Math. Educ., vol. 2, no. 1, p. 73, 2020, doi: 10.21580/square.2020.2.1.5626.
