Laskarjati, Safira Dwiyanti (2022) Perbandingan Peramalan Harga Saham Menggunakan Autoregressive Intergrated Moving Average (Arima) Dan Fuzzy Time Series Markov Chain (Studi Kasus Saham PT Indofood Cbp Sukses Makmur TBK). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Investasi adalah komitmen sejumlah uang atau sumber daya lainnya yang dilakukan saat ini dengan tujuan menerima manfaatnya di kemudian hari. Saham memiliki harga sebagai nilai pada sebuah perusahaan salah satu cara meminimalisir terjadi risiko dalam berinvestasi saham dengan mempelajari pola data time series pergerakan harga saham. Peramalan data time series dan pola pergerakan harga saham dapat dilakukan dengan beberapa metode seperti metode Autoregressive Intergrated Moving Average (ARIMA), dan Fuzzy Time Series Markov Chain. ARIMA merupakan metode peramalan data time series yang mampu menangani data yang tidak stasioner dalam mean dan varians seperti harga saham yang mengalami pergerakan secara naik turun tidak menentu. Sedangkan Fuzzy Time Series Markov Chain merupakan metode peramalan data yang menggunakan prinsip-prinsip logika fuzzy sebagai dasar proses perhitungan peramalan. Penelitian ini akan mengkaji metode ARIMA dan Fuzzy Time Series Markov Chain untuk diperbandingkan metode yang terbaik dalam meramalkan harga saham dengan studi kasus data harga penutupan saham PT Indofood CBP Sukses Makmur Tbk. Saham PT Indofood CBP Sukses Makmur Tbk merupakan salah satu emiten yang memiliki kapitalisasi pasar pada posisi ke-16 periode Januari tahun 2022 sebesar Rp 101.750.147,30 dan termasuk ke dalam indeks LQ45. Berdasarkan hasil penelitian yang telah dilakukan dengan data training, metode yang memiliki tingkat akurasi terbaik adalah Fuzzy Time Series Markov Chain dengan nilai MAPE sebesar 0,876% atau memiliki tingkat akurasi mencapai 99,124% daripada metode ARIMA(1,1,1). Sedangkan hasil data testing menunjukkan bahwa metode Fuzzy Time Series Markov Chain menghasilkan nilai MAPE sebesar 0,949% atau memiliki tingkat akurasi mencapai 99,051% daripada metode ARIMA(1,1,1). Oleh karena itu, metode terbaik yang digunakan untuk meramalkan data harga saham PT Indofood CBP Sukses Makmur Tbk adalah Fuzzy Time Series Markov Chain pada data training dan data testing.
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An investment is a commitment of a sum of money or other resources made at this time to receive its benefits in the future. Stocks have a price as a value in a company. One way to minimize risk in investing in stocks is by studying the time series data patterns of stock price movements. Forecasting time series data and stock price movement patterns can be done using several methods, such as the Autoregressive Integrated Moving Average (ARIMA) method and the Fuzzy Time Series Markov Chain. ARIMA is a time series data forecasting method that can handle data that is not stationary in the mean and variance, such as stock prices that experience erratic up and down movements. Meanwhile, Fuzzy Time Series Markov Chain is a data forecasting method that uses fuzzy logic principles as the basis for the forecasting calculation process. This study will examine the ARIMA method and the Fuzzy Time Series Markov Chain to compare the best method for forecasting stock prices with a case study of closing price data for PT Indofood CBP Sukses Makmur Tbk. Shares of PT Indofood CBP Sukses Makmur Tbk is one of the issuers with a market capitalization at the 16th position for the January 2022 period of Rp 101.750.147,30 and is included in the LQ45 index. Based on the results of research that has been done with training data, the method with the best accuracy level is the Fuzzy Time Series Markov Chain, with a MAPE value of 0,876% or an accuracy rate of 99,124% compared to the ARIMA method (1,1,1). In comparison, the testing data results show that the Fuzzy Time Series Markov Chain method produces a MAPE value of 0,949% or has an accuracy rate of 99,051% higher than the ARIMA method (1,1,1). Therefore, the best method used to predict the stock price data of PT Indofood CBP Sukses Makmur Tbk is the Fuzzy Time Series Markov Chain on training data and testing data.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSAk 519.535 Las p-1 2022 |
| Uncontrolled Keywords: | ARIMA, Fuzzy Time Series, Fuzzy Time Series Markov Chain, Peramalan, PT Indofood CBP Sukses Makmur Tbk, Saham, Forecasting, Fuzzy Time Series, Fuzzy Time Series Markov Chain,, PT Indofood CBP Sukses Makmur Tbk, Stock |
| Subjects: | H Social Sciences > HG Finance > HG4915 Stocks--Prices Q Science > QA Mathematics > QA280 Box-Jenkins forecasting |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 20 May 2024 05:11 |
| Last Modified: | 20 May 2024 05:11 |
| URI: | http://repository.its.ac.id/id/eprint/107962 |
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