Application of Triple Exponential Smoothing Method to Predict LQ45 Saham Stock Price
DOI:
https://doi.org/10.24014/coreit.v8i2.14935Abstract
The capital market is one of the investment models that is currently growing so rapidly because there are more and more digital-based investment platforms that can be accessed using mobile smartphones. The amount of interest in investing makes many people who experience losses due to not understanding the investment risks. For this reason, it is necessary to have the ability to analyze technically based on historical data. The object of this research is LQ45 shares in three companies, Indofood Sukses Makmur Tbk (INDF), Unilever Indonesia Tbk (UNVR), and Aneka Tambang Tbk (ANTM). The method used in this research is the Triple Exponential Smoothing method which is a prediction method that utilizes the statistical analysis method. The variables used in this study are historical prices ranging from Open, High, Low, and Close prices. The stages used are the collection of 125 historical data, where the data is taken through the Google Finance financial database. Then the Triple Exponential Smoothing calculation process is carried out, the data is stored in the database and presented in the form of graphs and tables. By using the parameter values = 0.13 and = 0.87 in the end it produces a Mean margin error level of Open price -0.10681%, High price -1.1156%, Low price 1.4616%, and Close price -0.2504%. The results of the study mean the margin of error is between -0.1% to 1%. The application of Triple Exponential Smoothing can be applied to predict stock prices. This research is to help investors analyze stock price movements.References
Aprayuda, R., & Misra, F. “Faktor Yang Mempengaruhi Keinginan Investasi Investor Muda di Pasar Modal Indonesia”, e-Jurnal Akuntansi, vol. 30, no. 5, 2020.
Irmanita, R., Prasetiyowati, S., & Sibaroni, Y. “Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes”, Jurnal RESTI, vol. 5, no. 1, pp. 10 - 16, 2021.
https://doi.org/10.29207/resti.v5i1.2770
Kartini, D., Abadi, F., & Saragih, T. H. “Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window”, Jurnal RESTI, vol. 5, no. 1, pp. 39 - 44, 2021.
https://doi.org/10.29207/resti.v5i1.2602
Agrawal, M., Khan, A. U., & Shukla, P.K. “Stock price prediction using technical indicators: A predictive model using optimal deep learning”, International Journal of Recent Technology and Engineering, vol. 8, no.2, 2297–2305, 2019. https://doi.org/10.35940/ijrteB3048.078219
Jonnius, “Peramalan Indeks Harga Saham dengan Pendekatan Exponential Smoothing Model”, Jurnal Penelitian Sosial Keagamaan, vol. 19, no. 2, 199–219, 2017.
Pratama, L. A. (2019). “Analisis Pembentukan Portofolio Saham Optimal Menggunakan Metode Single Index Model (Studi Empiris Pada Saham Indeks LQ 45 Di Bursa Efek Indonesia)”, Tesis, Universitas Negeri Yogyakarta, 2019.
Fitria, V., & Anwar, S. “Penerapan Triple Exponential Smoothing Dalam Meramalkan Laju Inflasi Bulanan Provinsi Aceh” E-Jurnal Ekonomi dan Bisnis Universitas Udayana, vol. 9, no.1, 23-38, 2020.
Jaen, H., Darnila, E., & Fikry, M. “Aplikasi Peramalan Kurs Bitcoin Rupiah Dengan Menggunakan Metode Double Exponential Smoothing,” TECHSI Jurnal Teknik Informatika, vo. 11, no.1, 2019.
https://doi.org/10.29103/techsi.v11i1.1295
Sravani, K. S., & Rajeswari, P. R. “Prediction of stock market exchange using LSTM algorithm”, International Journal of Scientific and Technology Research, vol. 9, no. 3, 417–421, 2020.
Singh, A., Gupta, P., & Thakur, N. “An empirical research and comprehensive analysis of stock market prediction using machine learning and deep learning techniques”, IOP Conference Series: Materials Science and Engineering, 1022(1), 1–12, 2020.
https://doi.org/10.1088/1757-899X/1022/1/012098
Nguyen, N. “An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction”, Risks, vol. 5, no. 4, 1–16, 2017. https://doi.org/10.3390/risks5040062
Nurdin, N., Rizal, R., & Rizwan, R. “Pendeteksian Dokumen Plagiarisme dengan Menggunakan Metode Weight Tree”, Jurnal Telematika, vol.12, no.1, 31-45, 2019.
Nurdin, N., Suhendri, M., Afrilia, Y., & Rizal, R. “Klasifikasi Karya Ilmiah Mahasiswa Menggunakan Metode Naive Bayes Classifier (Nbc)”, SISTEMASI: Jurnal Sistem Informasi, vol. 10, no. 2, 268-279, 2021.
Nurdin, N., Hamdhana, D., & Setiawan, M. J. “Sistem Pendeteksi Pola Lafadz Allah dan Muhammad pada Citra Al-quran Menggunakan Metode Peirce. Jurnal TECHSI, vol. 9, no.2, 78-90, 2017.
Nurdin, N., Hutomi, M., Qamal, M., & Bustami, B. “Sistem Pengecekan Toko Online Asli atau Dropship pada Shopee Menggunakan Algoritma Breadth First Search”, Jurnal RESTI, vol. 4, no.6. 1117-1123., 2020.
Nurdin, N., Darmansyah, A., & Fajriana, F. “Aplikasi Pemetaan Lokasi Distribusi Gas Elpiji 3 Kg Menggunakan Algoritma Ant Colony Berbasis Android”, Jurnal CoreIT, vol.6, no.1, 30-36, 2020.
Makridakis, M., & Wheelwright, H. R. “Forecasting Methods and Aplications”, In Forecasting methods and applications, 2017
Downloads
Published
Issue
Section
License
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to CoreIT journal and published by Informatics Engineering Department Universitas Islam Negeri Sultan Syarif Kasim Riau as publisher of the journal.
Authors who publish with this journal agree to the following terms:
Authors automatically transfer the copyright to the journal and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike (CC BY SA) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate permission for 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 acknowledgement 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 (See The Effect of Open Access).
