Perbandingan Metode Support Vector Regression dan Simple Moving Average pada Peramalan Harga Saham BBCA.JK
DOI:
https://doi.org/10.29407/inotek.v7i1.3439Abstract
Saat ini trader merupakan salah satu pekerjaan idaman bagi warga Indonesia. Hal ini dapat dibuktikan dengan data dari Google Trend. Saham merupakan salah satu instrumen yang diminati oleh para trader untuk melakukan trading. Harga saham yang naik turun menyebabkan para trader baru kesulitan memprediksi harga saham yang akan datang. Oleh karena itu dibutuhkan bantuan sebuah indikator. Penelitian ini bertujuan untuk melakukan perbandingan akurasi metode SVR (Support Vector Regression) dan SMA(Simple Moving Average) dimana kedua metode tersebut terbukti memiliki keakuratan yang tinggi dalam meramal harga saham BBCA.JK di masa depan. Evaluasi yang digunakan yaitu MAPE. Hasil penelitian ini menunjukkan bahwa SVR memiliki hasil evaluasi MAPE sebesar 0.97% dan SMA sebesar 0.67%. Ini membuktikan bahwa SMA memiliki performa lebih baik dalam meramal harga saham BBCA.JK.
Keywords:
Trading, Saham, BBCA.JK##plugins.themes.default.displayStats.downloads##
References
W. C. Utomo, M. A. Saputra, T. Informatika, and F. Teknik, “Forecasting Pergerakan Harga Volatility Index dengan Menggunakan Metode Fuzzy Tsukamoto dan Evaluasi Dstat Metric,” vol. 7, no. 1, pp. 14–22, 2023, doi: https://doi.org/10.29407/gj.v7i1.19605.
D. T. Anggraeni, “Forecasting Harga Saham Menggunakan Metode Simple Moving Average Dan Web Scrapping,” J. Ilm. Matrik, vol. 21, no. 3, pp. 234–241, 2019, doi: 10.33557/jurnalmatrik.v21i3.726.
A. M. Zaimsyah and S. Herianingrum, “Jurnal Ilmiah Ekonomi Islam , 5 ( 02 ), 2019 , 113-119 Analisis Fundamental Terhadap Harga Saham yang Terdaftar di Jakarta Islamic Index Tahun 2010-2017,” vol. 5, no. 02, pp. 113–119, 2019.
L. Choirotul Mafula, “ANALISIS TEKNIKAL SEBAGAI DASAR PENGAMBILAN KEPUTUSAN DALAM TRADING SAHAM PADA BURSA EFEK INDONESIA (Studi pada Indeks IDX30 Periode Agustus 2014-Januari 2015),” J. Ilm. Mhs. FEB Univ. BRAWIJAYA, vol. 3, no. 2, pp. 1–11, 2015.
E. M. M. Ida Bagus Nyoman Udayana, Lusia Tria Hatmanti Hutami, Mar’atul Fahimah, “Fakultas ekonomi dan bisnis universitas wiraraja - sumenep,” Minat Beli Pada Marketpl. Unic. Di Indones. Tokopedia Dan Bukalapak, vol. 9, no. 1, pp. 1–12, 2019.
D. I. Purnama, “Peramalan Jumlah Penumpang Datang Melalui Transportasi Udara Di Sulawesi Tengah Menggunakan Support Vector Regression (SVR),” J. Ilm. Mat. Dan Terap., vol. 17, no. 1, 2020, doi: 10.22487/2540766x.2020.v17.i1.15186.
N. Litha and T. Hasanuddin, “Analisis Performa Metode Moving Average Model untuk Prediksi Jumlah Penderita Covid-19,” Indones. J. Data Sci., vol. 1, no. 3, pp. 87–95, 2020.
N. D. Maulana, B. D. Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus : Harum Bakery),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2986–2995, 2019.
E. M. Priliani, A. T. Putra, and M. A. Muslim, “Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO),” Sci. J. Informatics, vol. 5, no. 2, pp. 118–127, 2018, doi: 10.15294/sji.v5i2.14613.
S. Rusdiana, Syarifah Meurah Yuni, and Delia Khairunnisa, “Comparison of Rainfall Forecasting in Simple Moving Average (SMA) and Weighted Moving Average (WMA) Methods (Case Study at Village of Gampong Blang Bintang, Big Aceh District-Sumatera-Indonesia,” J. Res. Math. Trends Technol., vol. 2, no. 1, pp. 21–27, 2020, doi: 10.32734/jormtt.v2i1.3753.
A. N. Safira, B. Warsito, and A. Rusgiyono, “Analisis Support Vector Regression (Svr) Dengan Algoritma Grid Search Time Series Cross Validation Untuk Prediksi Jumlah Kasus Terkonfirmasi Covid-19 Di Indonesia,” J. Gaussian, vol. 11, no. 4, pp. 512–521, 2023, doi: 10.14710/j.gauss.11.4.512-521.
S. Suwanto and D. C. R. Novitasari, “Forecasting Solar Activities based on Sunspot Number Using Support Vector Regression (SVR),” JPSE (Journal Phys. Sci. Eng., vol. 5, no. 1, pp. 6–10, 2020, doi: 10.17977/um024v5i12020p006.
D. I. Purnama and S. Setianingsih, “Support vector regression (SVR) model for forecasting number of passengers on domestic flights at Sultan Hasanudin airport Makassar,” J. Mat. Stat. dan Komputasi, vol. 16, no. 3, p. 391, 2020, doi: 10.20956/jmsk.v16i3.9176.
I. Nabillah and I. Ranggadara, “Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut,” JOINS (Journal Inf. Syst., vol. 5, no. 2, pp. 250–255, 2020, doi: 10.33633/joins.v5i2.3900.
A. M. Maricar, “Analisa Perbandingan Nilai Akurasi Moving Average dan Exponential Smoothing untuk Sistem Peramalan Pendapatan pada Perusahaan XYZ,” J. Sist. dan Inform., vol. 13, no. 2, pp. 36–45, 2019, [Online]. Available: https://www.jsi.stikom-bali.ac.id/index.php/jsi/article/view/193
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Achmad Youngy Fernando, Umi Mahdiyah, Ardi Sanjaya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
