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All Journal Jurnal Manajemen Pendidikan: Jurnal Ilmiah Administrasi, Manajemen dan Kepemimpinan Pendidikan Emitor: Jurnal Teknik Elektro JURNAL MAHKAMAH Sistemasi: Jurnal Sistem Informasi Al Qodiri : Jurnal Pendidikan, Sosial dan Keagamaan Jurnal Pilar Nusa Mandiri Jurnal Administrasi Pendidikan Indonesia Indonesian Journal of Islamic Educational Management Martabat: Jurnal Perempuan dan Anak JURNAL PENDIDIKAN TAMBUSAI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Review Pendidikan dan Pengajaran (JRPP) Andragogi: Jurnal Pendidikan Islam dan Manajemen Pendidikan Islam Lisan An Nathiq : Jurnal Bahasa dan Pendidikan Bahasa Arab Asatiza: Jurnal Pendidikan Ta'lim al-'Arabiyyah: Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban Al-Fusha : Arabic Language Education Journal Alsina : Journal of Arabic Studies MANAJERIAL: Jurnal Inovasi Manajemen dan Supervisi Pendidikan Lugawiyyat Kode : Jurnal Bahasa al-fikrah: Jurnal Manajemen Pendidikan BUANA GENDER : Jurnal Studi Gender dan Anak Al Hurriyah : Jurnal Hukum Islam Jurnal Pendidikan Islam At-Tadzkir: Islamic Education Journal FATAWA: Jurnal Pendidikan Agama Islam Jurnal Keamanan Nasional Al-Tadris: Jurnal Pendidikan Bahasa Arab
Asy'ari, Hasyim
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Pilar Nusa Mandiri

 1 
PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION Urrochman, Maysas Yafi; Asy'ari, Hasyim; Hizham, Fadhel Akhmad
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6072

Abstract

The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.
Co-Authors Abdul Rozaq Ades Marsela, Ades Agus Setiawan Agus Supardi Ahkami, Nazli Ahmad Munawar Amelia, Firda Aprianty, Dinda N Armai Arief Azizah, Karimatul Azizatulhadhro, Neng Lulu Bana Handaga Dimyati, Yayat Dwi Bhakti Indri M, Dwi Bhakti ElJack Ibrahim, Nahla Ibrahim Elok Rufaiqoh Elok Rufaiqoh, Elok Fernanda, Widyawati Firdaus, Dani GANDASCITA, ILHAM Habib Ismail, Habib HAIRIAH, MUSLIMATUL Hasanah, Mizanul Himawan, Muh Azis Hindarsih, Tri Puji Hizham, Fadhel Akhmad Humairoh, Widya Belqis Istiqomah, Rofiatul Jejen Musfah, Jejen Jumanto, J Karmila, Dela Silmi KURNIAWAN, SYAHRUL FAUZI Luluk Fikri Zuhriyah. Maftuhah Maftuhah Mamluatul Hasanah Masae, Murnee Mustoto, Musoto Nikma, Sulalatun NURJANAH, RIFQOH Nurochim, Nurochim Pangestu, Prayogo Pardini, Agung Putra, Feri Setia PUTRI, FATIKA ADEKIA Rahmadina, Rizka Ratnaningsih, Sita Rizal Putra, Novit Sandria, Anis Sawitri, Sona Sepudin, Mohammad Fahmi Siti Fatimah, Fahmi Siti Maesaroh Syuhadak, Syuhadak Urrochman, Maysas Yafi Wafiroh, Evi Zulfatun Wijayanto, Ari Yundari, Yundari Zibrija, Fiki Roi’atuz