PERFORMANCE EVALUATION OF THE INDF.JK STOCK PRICE MOVEMENT PREDICTION MODEL USING RANDOM FOREST METHOD WITH GRID SEARCH CROSS VALIDATION OPTIMIZATION

  • Della Zaria Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia https://orcid.org/0009-0001-6383-2344
  • Evy Sulistianingsih Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia https://orcid.org/0000-0002-7133-1822
  • Shantika Martha Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia https://orcid.org/0000-0001-6124-8534
  • Wirda Andani Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia https://orcid.org/0000-0002-2210-8253
DOI: https://doi.org/10.30598/barekengvol19iss3pp2155-2168
Keywords: Cross Validation, Grid Search, Random Forest, Stock

Abstract

Investment in financial instruments in Indonesia has shown significant growth over time, with stocks often being the first choice for investors to invest money. Unfortunately, deciding to buy and sell stocks is not easy. When determining the right time to buy or sell stocks, volatile stock price movements and losses caused by wrong decisions are investors' problems. Thus, it is essential to analyze stock price movement predictions. This study aims to evaluate the prediction model's performance for PT Indofood Sukses Makmur Tbk (INDF.JK) stock price movement in the next 30 days to reduce the risk of possible losses and help the decision-making process. We used the Random Forest method and Grid Search Cross Validation (CV) optimization to form the model. The data used is the closing price of INDF.JK stock for the period January 2, 2014, to December 29, 2023, from Yahoo Finance, which is processed into eight types of stock technical indicators, namely SMA_5, SMA_10, SMA_15, SMA_30, EMA_9, MACD, MACD_Signal, and RSI. The research pipeline includes descriptive statistics, preprocessing, feature and target variables determination, data split, model formation without and with optimization, testing accompanied by performance evaluation, and comparison of the formed model. The results show that the prediction model of INDF. JK's stock price movement in the next 30 days has excellent performance, proven accurate by 90.8% with the application of Random Forest and Grid Search CV. The Random Forest prediction model with Grid Search CV optimization has better performance indicators than the Random Forest model without Grid Search CV optimization, which is shown by the increase of all indicator values. The relative Strength Index is the variable with the best performance for the prediction model. It can be used as the primary consideration for investors when deciding on the buying and selling process of INDF.JK stock in the next 30 days.

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References

idx.co.id, “MELALUI BERBAGAI PENCAPAIAN TAHUN 2023, PASAR MODAL INDONESIA TUNJUKKAN OPTIMISME HADAPI TAHUN 2024,” BURSA EFEK INDONESIA, Press Release 117/BEI.SPR/12-2023, Desember 2023. [Online]. Available: https://www.idx.co.id/id/berita/siaran-pers/2080

M. N. Syafira and D. Saepudin, “PREDIKSI RETURN SAHAM DENGAN METODE RANDOM FOREST DAN PENERAPANNYA UNTUK SELEKSI PORTOFOLIO,” in e-Proceeding of Engineering, Oktober 2023, pp. 4996–5001.

idx.co.id, “INVESTOR PASAR MODAL TEMBUS 13 JUTA, KOLABORASI DENGAN KOMUNITAS DAN IDX MOBILE JADI ANDALAN,” Bursa Efek Indonesia, Press Release 039/BEI.SPR/06-2024, Jun. 2024. [Online]. Available: https://www.idx.co.id/id/berita/siaran-pers/2166

A. Pratomo, R. F. Umbara, and A. A. Rohmawati, “PREDIKSI PERGERAKAN HARGA SAHAM DENGAN METODE RANDOM FOREST MENGGUNAKAN TREND DETERMINISTIC DATA PREPARATION,” in e-Proceeding of Engineering, Apr. 2019, pp. 2545–2556.

K. Sidiq and D. R. S. Saputro, “CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION,” BAREKENG J. Ilmu Mat. Dan Terap., vol. 17, no. 2, pp. 0847–0854, Jun. 2023, doi: https://doi.org/10.30598/barekengvol17iss2pp0847-0854.

M. Azhar and H. F. Pardede, “KLASIFIKASI DIALEK PENGUJAR BAHASA INGGRIS MENGGUNAKAN RANDOM FOREST,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 2, pp. 439–446, Apr. 2021, doi: https://doi.org/10.30865/mib.v5i2.2754.

L. N. Aina, V. R. S. Nastiti, and S. K. Aditya, “IMPLEMENTASI EXTRA TREES CLASSIFIER DENGAN OPTIMASI GRID SEARCH CV PADA PREDIKSI TINGKAT ADAPTASI,” MIND Multimed. Artif. Intell. Netw. Database J., vol. 9, no. 1, pp. 78–88, 2024, doi: https://doi.org/10.2670/mindjournal.v9i1.78-84.

B. F. Sitanggang and P. Sitompul, “DETEKSI AWAL KELANGSUNGAN HIDUP PASIEN GAGAL JANTUNG MENGGUNAKAN MACHINE LEARNING METODE RANDOM FOREST,” Innov. J. Soc. Sci. Res., vol. 4, no. 2, pp. 3347–3357, 2024, doi: 10.31004/innovative.v4i2.8189.

D. Z. H. Iskandar and Y. Ramdhani, “OPTIMASI PARAMETER RANDOM FOREST MENGGUNAKAN GRID SEARCH UNTUK ANALISIS TIME SERIES,” PETIR J. Pengkaj. Dan Penerapan Tek. Inform., vol. 16, no. 2, pp. 267–277, Nov. 2023, doi: https://doi.org/10.33322/petir.v16i2.2084.

B. Jange, “PREDIKSI HARGA SAHAM BCA MENGGUNAKAN XGBOOST,” Arbitr. J. Econ. Account., vol. 3, no. 2, pp. 231–237, Nov. 2022, doi: https://doi.org/10.47065/arbitrase.v3i2.495.

M. Sadikin and R. Agustina, “ANALISIS FUNDAMENTAL DAN TEKNIKAL SAHAM BCA DAN BRI (TAHUN 2019-2021),” Semin. Nas. Akunt. Dan Call Pap. SENAPAN, vol. 3, no. 1, pp. 57–67, Oct. 2023, doi: https://doi.org/10.33005/senapan.v3i1.285.

T. Astutiningsih, D. R. S. Saputro, and Sutanto, “OPTIMASI ALGORITME XTREME GRADIENT BOOSTING (XGBOOST) PADA HARGA SAHAM PT. UNITED TRACTORS TBK.,” SPECTA J. Technol., vol. 7, no. 3, pp. 632–641, Dec. 2023, doi: https://doi.org/10.35718/specta.v7i3.1031.

K. Valiant, Y. Lukito, and G. Santosa, “SISTEM PREDIKSI HARGA SAHAM LQ45 DENGAN RANDOM FOREST CLASSIFIER,” JUTEI, vol. 3, no. 2, pp. 127–136, Oktober 2019, doi: https://doi.org/10.21460/jutei.201932:187.

Arman, A. I. Haanurat, and Nur’aeni, “Analisis Moving Average Convergence Divergence pada Investasi Saham Syariah saat New Era,” Ekombis Sains J. Ekon. Keuang. Dan Bisnis, vol. 7, no. 1, pp. 1–10, Mar. 2022, doi: 10.24967/ekombis.v7i1.1540.

R. G. Firdaus, “ANALISIS TEKNIKAL SAHAM MENGGUNAKAN INDIKATOR RSI DAN BOLLINGER BANDS PADA SAHAM KONSTRUKSI,” J. Pasar Modal Dan Bisnis, vol. 3, no. 1, pp. 15–26, Feb. 2021, doi: https://doi.org/10.37194/jpmb.v3i1.60.

I. Maulana, A. M. Siregar, and A. Fauzi, “OPTIMIZATION OF MACHINE LEARNING MODEL ACCURACY FOR BRAIN TUMOR CLASSIFICATION WITH PRINCIPAL COMPONENT ANALYSIS,” J. Tek. Inform. JUTIF, vol. 5, no. 3, pp. 903–915, 2024, doi: https://doi.org/10.52436/1.jutif.2024.5.3.2058.

L. Muflikhah, Widodo, W. Firdaus, and Solimun, MACHINE LEARNING DALAM BIOINFORMATIKA. in 1. Universitas Brawijaya Press, 2021.

R. A. Haristu and P. H. P. Rosa, “Penerapan Metode Random Forest untuk Prediksi Win Ratio Pemain Player Unknown Battleground,” MEANS Media Inf. Anal. Dan Sist., vol. 4, no. 2, pp. 120–128, Oct. 2019, doi: https://doi.org/10.54367/means.v4i1.545.

C. Yulianto, “MODEL PENILAIAN TANAH MASSAL BERBASIS BIDANG TANAH MENGGUNAKAN ALGORITMA RANDOMFOREST DI KOTA SURAKARTA,” J. Pertanah., vol. 14, no. 1, pp. 26–39, 2023, doi: https://doi.org/10.53686/jp.v4i1.204.

A. S. M. Shafi, M. M. I. Molla, J. J. Jui, and M. M. Rahman, “DETECTION OF COLON CANCER BASED ON MICROARRAY DATASET USING MACHINE LEARNING AS A FEATURE SELECTION AND CLASSIFICATION TECHNIQUES,” SN Appl. Sci., vol. 2, no. 7, p. 1243, Jul. 2020, doi: https://doi.org/10.1007/s42452-020-3051-2.

E. Christy and K. Suryowati, “ANALISIS KLASIFIKASI STATUS BEKERJA PENDUDUK DAERAH ISTIMEWA YOGYAKARTA MENGGUNAKAN METODE RANDOM FOREST,” J. Stat. Ind. Dan Komputasi, vol. 6, no. 1, pp. 69–76, 2021.

F. Ramayanti, D. Vionanda, D. Permana, and Zilrahmi, “APPLICATION OF RANDOM FOREST TO IDENTIFY FOR POOR HOUSEHOLDS IN WEST SUMATERA PROVINCE,” UNP J. Stat. Data Sci., vol. 1, no. 2, pp. 97–104, Mar. 2023, doi: https://doi.org/10.24036/ujsds/vol1-iss2/31.

U. Sunarya and T. Haryanti, “PERBANDINGAN KINERJA ALGORITMA OPTIMASI PADA METODE RANDOM FOREST UNTUK DETEKSI KEGAGALAN JANTUNG,” J. Rekayasa Elektr., vol. 18, no. 4, pp. 241–247, Dec. 2022, doi: https://doi.org/10.17529/jre.v18i4.26981.

Y. Widyaningsih, G. P. Arum, and K. Prawira, “APLIKASI K-FOLD CROSS VALIDATION DALAM PENENTUAN MODEL REGRESI BINOMIAL NEGATIF TERBAIK,” BAREKENG J. Ilmu Mat. Dan Terap., vol. 15, no. 2, pp. 315–322, Jun. 2021, doi: https://doi.org/10.30598/barekengvol15iss2pp315-322.

N. Hadianto, H. B. Novitasari, and A. Rahmawati, “KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 163–170, Sep. 2019, doi: https://doi.org/10.33480/pilar.v15i2.658.

Published
2025-07-01
How to Cite
[1]
D. Zaria, E. Sulistianingsih, S. Martha, and W. Andani, “PERFORMANCE EVALUATION OF THE INDF.JK STOCK PRICE MOVEMENT PREDICTION MODEL USING RANDOM FOREST METHOD WITH GRID SEARCH CROSS VALIDATION OPTIMIZATION”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 2155-2168, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Della Zaria, Evy Sulistianingsih, Shantika Martha, Wirda Andani

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