DEVELOPMENT OF A STOCK PURCHASE RECOMMENDATION SYSTEM APPLICATION

Greghar Juan Tjether Maruanaya; Gandung Triyono; Rita Fransina Maruanaya

doi:10.52436/1.jutif.2024.5.4.2322

Greghar Juan Tjether Maruanaya Master of Computer Science Study Programme, Universitas Budi Luhur, Indonesia
Gandung Triyono Master of Computer Science Study Programme, Universitas Budi Luhur, Indonesia
Rita Fransina Maruanaya Faculty of Education, Institute of Vocational Education and Vocational Didactics, Technische Universität Dresden, Germany

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2322

Keywords: Investment, Recommendations, Simple Moving Average, Stocks, TOPSIS

Abstract

Investing in stocks has become a significant source of passive income through indirect earnings with minimal activity. Choosing stocks for investment requires careful analysis. The Indonesia Stock Exchange has 866 listed stocks, divided into several indices, including IDXBUMN20, which includes 20 stocks from state-owned enterprises (BUMN), regional-owned enterprises (BUMD), and their affiliates. This index helps traders monitor the performance of BUMN stocks. The list of IDXBUMN20 stocks includes ADHI, ANTM, BBNI, AGRO, BBRI, BRIS, BBTN, BJBR, BMRI, MTEL, ELSA, JSMR, PGAS, PTBA, PTPP, SMGR, TINS, TLKM, WIKA, and WSKT. Traders need recommendations to select stocks with positive trends. Forecast analysis becomes a potential solution to provide references for stocks with positive trends. This study applies the Simple Moving Average (SMA) method to forecast the prices of IDXBUMN20 stocks. The SMA will be measured using 30, 40, 50, and 60-day periods as indicators. This method is chosen for its ability to identify stock price trends by calculating the average closing price over a specific period. Therefore, forecasting results using SMA will provide a more accurate picture of stock price movements and aid in making better investment decisions. From the forecasting results using the SMA method, recommendations for the top five stocks showing positive trends will be obtained. Subsequently, to determine which stock is most recommended, a stock recommendation model will be developed using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. TOPSIS will consider various criteria such as average frequency, Price Earning Ratio (PER), Price Book Value (PBV), Return on Assets (ROA), and Return on Equity (ROE). The results showed that the most recommendable stocks based on the positive trend of price movements are SMGR for indicator 30 and TLKM for indicators 40, 50 and 60. Therefore, it can be concluded that the most recommended stock is TLKM (PT Telkom Indonesia (Persero) Tbk).This recommendation model is expected to help traders select the stocks with the best investment potential, maximizing profits and minimizing investment risks in the capital market.

Downloads

Download data is not yet available.

References

R. A. Hidayana and B. N. Ruchjana, “Peramalan Return Saham Menggunakan Model Integrated Moving Average,” Jambura J. Math., vol. 5, no. 1, pp. 199–209, Feb. 2023, doi: 10.34312/jjom.v5i1.17381.

D. T. Anggraeni, “Forecasting Harga Saham Menggunakan Metode Simple Moving Average Dan Web Scrapping,” J. Ilm. MATRIK, vol. 21, no. 3, 2019.

I. S. Exchange, “Indeks Saham,” 2023. https://www.idx.co.id/id/produk/indeks (accessed Jun. 05, 2023).

F. D. Silalahi, K. Rozikin, D. Rutdjiono, and N. D. Setiawan, “Pemanfaatan Metode Moving Average Dalam Sistem Informasi Pendukung Keputusan Pembelian Barang Berdasarkan Peramalan Penjualan Dengan Berbasis Web,” J. Ilm. Elektron. DAN Komput., vol. 14, no. 2, pp. 198–207, 2021.

K. Ali, “Forecasting Analysis of Share Price Index in Construction Companies Registered in Indonesia Stock Exchange 2015-2019,” J. Econ. Res. Soc. Sci., vol. 5, no. 1, pp. 42–63, 2021, doi: 10.18196/jerss.v5i1.11044.

D. Ayu Rezaldi and Sugimana, “Peramalan Metode ARIMA Data Saham PT. Telekomunikasi Indonesia,” Peramalan Metod. ARIMA Data Saham PT. Telekomun. Indones. Prism. Pros. Semin. Nas. Mat., vol. 4, pp. 611–620, 2021, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/

A. Sabda, “Daftar Indeks IDXBUMN20 Periode 2023,” 2022. https://snips.stockbit.com/investasi/idxbumn20 (accessed Aug. 21, 2023).

Jony, “Sistem rekomendasi pemilihan saham lq45 menggunakan metode topsis pada banking,” J. Inf. Syst. Comput. Sci., 2021.

A. M. Yolanda and M. Ridhwan, “Peramalan Data dengan Teknik Pemulusan Simple Moving Average (Studi Kasus Harga Saham Harian PT Bank BRI Syariah Tbk),” AL-Muqayyad, vol. 3, no. 2, pp. 136–143, Dec. 2020, doi: 10.46963/jam.v3i2.195.

R. Ainaya and D. Gustian, “Sistem Pendukung Keputusan Pemilihan Calon Penerima Program Indonesia Pintar Dengan Metode Fuzzy TOPSIS,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 2, pp. 883–894, 2022.

E. T. Alawiah, Sefrika, and M. H. Siregar, “Sistem Pendukung Keputusan Pemilihan Instrumen,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. September 2019, pp. 8–13, 2020.

A. Andoyo et al., Sistem Penunjang Keputusan Konsep, Implementasi dan Pengembangan. Indramayu: Adab, 2021.

A. Surono, B. A. Rossena, and I. Kurniawati, “Perancangan Sistem Pendukung Keputusan Pemilihan Instrumen Investasi Terbaik Menggunakan Metode TOPSIS Anang,” Innov. Res. INFORMATICS, vol. 2, pp. 50–55, 2022.

M. A. Maricar, “Analisa Perbandingan Nilai Akurasi Moving Average dan Exponential Smoothing untuk Sistem Peramalan Pendapatan pada Perusahaan XYZ,” J. Sist. DAN Inform., pp. 36–45, 2019.

A. Fauza and E. Noviaty, “Analysis of Accuracy Level of Moving Average, Parabolic Sar and Convolutional Indicators Neural Network on Buy and Sell Decisions,” J. Ekon., vol. 11, no. 03, pp. 839–847, 2022, [Online]. Available: http://ejournal.seaninstitute.or.id/index.php/Ekonomi/article/view/779.