Komparasi Pergerakan Saham Apple Dan Samsung Menggunakan Algoritma Support Vector Machine (SVM)

Falentino Sembiring(1*), Mayang Gunawan(2), Rosalinda Hakim(3), Vemi Januarita Putri(4),

(1) Universitas Nusa Putra, Indonesia
(2) Universitas Nusa Putra, Indonesia
(3) Universitas Nusa Putra, Indonesia
(4) Universitas Nusa Putra, Indonesia
(*) Corresponding Author

Abstract


The capital market creates opportunities for the public to participate in economic activities, especially in investing. One of the assets for investment is stock. The capital market creates opportunities for the public to participate in economic activities, especially in investing. One of the assets for investment is stock. The purpose of this research is to compare stock price movements between Apple companies and Samsung companies after the pandemic. One method that can be used to predict stock price movements is the support vector machine (SVM). This study uses two approaches as input models, the first approach for data input is obtained from the calculation of ten technical parameter indicators using trading data (open, high, low, close, price) while the second approach focuses on stating the results of calculations using several indicators Technical parameters become trend deterministic data preparation. Even this research uses historical data from each company from 2017 to 2022. This data is used to study patterns that can ultimately predict stock price movements of each company. From the results of this study with the help of Orange software, it can be concluded that the application states that in terms of data, Samsung's ROC analysis is 0.435% superior to Apple, only 0.359%.

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References


Dasriyan Saputra, “‘Pengaruh Manfaat, Modal, Motivasi Dan Edukasi Terhadap Minat Dalam Berinvestasi Di Pasar Modal,’” J. Manaj. Dan Akunt., vol. 5, no. 2, pp. 178–190, 2018.

S. Kasus et al., “Prediksi Pergerakan Harga Saham dengan Metode Support Vector Machine ( SVM ) Menggunakan Trend Deterministic Data Preparation Program Studi Sarjana Ilmu Komputasi Fakultas Informatika Universitas Telkom Bandung,” 2018.

“PrediksiHarga…(HasbiYasin),” pp. 29–35.

“CMR Institute of Technology,” pp. 1–49, 2019.

Y. Ramdhani and A. Mubarok, “Analisis Time Series Prediksi Penutupan Harga Saham,” J. Responsif, vol. 1, no. 1, pp. 77–82, 2019.

F. Sembiring, D. Gustian, A. Erfina, and Y. Vikriansyah, “Analisis Tingkat Akurasi Algoritma Moving Average dalam Prediksi Pergerakan Uang Elektronik Bitcoin,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 10, no. 1, p. 23, 2021, doi: 10.35889/jutisi.v10i1.577.

M. Awadalla, “The Analysis of Strategic Management of Samsung Electronics Company through the Generic Value Chain Model,” vol. 63, no. Ngcit 2014, pp. 75–79.

R. F. T. Wulandari and D. Anubhakti, “Implementasi Algoritma Support Vector Machine (Svm) Dalam Memprediksi Harga Saham Pt. Garuda Indonesia Tbk,” IDEALIS Indones. J. Inf. Syst., vol. 4, no. 2, pp. 250–256, 2021, doi: 10.36080/idealis.v4i2.2847.

W. R. U. Fadilah, D. Agfiannisa, and Y. Azhar, “Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine,” Fountain Informatics J., vol. 5, no. 2, p. 45, 2020, doi: 10.21111/fij.v5i2.4449.

M. Raehanun, “Analisis Support Vector Machine (SVM) Dalam Prediksi Permintaan Emas Perhiasan (Studi Kasus: Permintaan Emas Perhiasan dari Beberapa Negara Tertentu Periode Tahun 2000-2021),” vol. 1, pp. 105–112, 2019.

S. Analisis Kegiatan Trading dengan SVM, K. R. Dan, N. Resti Wardani, S. Saepudin, and C. Warman, “Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 2, pp. 863–870, 2022.

Y. Liu, Q. Zeng, J. Ordieres Meré, and H. Yang, “Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach,” Complexity, vol. 2019, 2019, doi: 10.1155/2019/9202457.

A. A. Z. Syahputra, A. D. Atika, M. A. Aslamsyah, M. C. Untoro, and W. Yulita, “Smartphone Price Grouping by Specifications using K-Means Clustering Method,” J. Tek. Inform. C.I.T Medicom, vol. 13, no. 2, pp. 64–74, 2021, doi: 10.35335/cit.vol13.2021.98.pp59-68.

Q. M. Ilyas, K. Iqbal, S. Ijaz, A. Mehmood, and S. Bhatia, “A Hybrid Model to Predict Stock Closing Price Using Novel Features and a Fully Modified Hodrick–Prescott Filter,” Electronics, vol. 11, no. 21, p. 3588, 2022, doi: 10.3390/electronics11213588.

Y. Song, “Stock Trend Prediction: Based on Machine Learning Methods,” ProQuest Diss. Theses, p. 43, 2018, [Online]. Available: https://search.proquest.com/docview/2031586457?accountid=49007%0Ahttp://www.yidu.edu.cn/educhina/educhina.do?artifact=&svalue=Stock+Trend+Prediction%3A+Based+on+Machine+Learning+Methods&stype=2&s=on%0Ahttp://sfx.cceu.org.cn:3410/bisu?url_ver=Z39.88-2004&r

R. W. Pratiwi, S. F. H, D. Dairoh, D. I. Af’idah, Q. R. A, and A. G. F, “Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM),” J. Informatics, Inf. Syst. Softw. Eng. Appl., vol. 4, no. 1, pp. 40–46, 2021, doi: 10.20895/inista.v4i1.387.




DOI: https://doi.org/10.30645/kesatria.v4i1.118

DOI (PDF): https://doi.org/10.30645/kesatria.v4i1.118.g112

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